Raphael Weber

Sandie Cabon

Fabienne Por ée Antoine Simon

Fabienne Poree

Guy Carrault

Rapha Ël Weber

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning

Keywords: Deep transfer learning, neonatal intensive care units, preterm newborn, video monitoring

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

N EWBORNS who are born before a gestational age of 37 weeks are considered premature and have not fully developed all of their vital functions. In order to ensure their optimal development, preterm newborns are hospitalized in neonatal intensive care units (NICUs), where they generally start their life in an incubator before gradually being settled in an open bed and require particular attention from medical staff. During their first days of hospitalization, extremely and very preterm newborns are in an incubator, which is a closed bed reproducing the in utero environment. They are then transferred in an open bed when they are mature enough. Regarding moderate and late preterm newborns, they are directly in an open bed after their birth. The open bed might be a cradle or equipped with a warm radiant. The monitoring of physiological signals allows to give information to the medical staff about the newborn state and the progress of maturation. Thus it helps them in the diagnosis of a disease and supports the treatment decision. Cardiac and respiratory signals are always monitored to detect, for instance, bradycardia and apnea. Brain activity may also be monitored for detecting neurological disorders such as neonatal seizures [START_REF] Pisani | Monitoring of newborns at high risk for brain injury[END_REF]. The drawback of monitoring such signals is the invasiveness. Indeed, it requires to stick adhesive electrodes or transducers on the very fragile skin of the newborn, which may damage it and increase the risk of infection.

Several technologies have been proposed in order to monitor cardiac activity and/or respiratory signal in a non-invasive manner (see [START_REF] Daw | Medical devices for measuring respiratory rate in children: a review[END_REF], [START_REF] Kevat | A systematic review of novel technology for monitoring infant and newborn heart rate[END_REF] for reviews), such as the use of capacitive sensors [START_REF] Atallah | Unobtrusive ecg monitoring in the nicu using a capacitive sensing array[END_REF], piezoelectric sensors [START_REF] Nukaya | A noninvasive heartbeat, respiration, and body movement monitoring system for neonates[END_REF], ultrasonic systems [START_REF] Arlotto | An ultrasonic contactless sensor for breathing monitoring[END_REF], radar systems [START_REF] Kim | Non-contact respiration monitoring using impulse radio ultrawideband radar in neonates[END_REF], thermal imaging [START_REF] Pereira | Noncontact monitoring of respiratory rate in newborn infants using thermal imaging[END_REF] or cameras [START_REF] Villarroel | Noncontact physiological monitoring of preterm infants in the neonatal intensive care unit[END_REF].

Moreover, video modality can be used for high-level analysis and proved to be relevant in several clinical applications in pediatrics, particularly with motion analysis [START_REF] Cabon | Video and audio processing in paediatrics: a review[END_REF]. Regarding clinical applications involving preterm newborns, video-based motion analysis has been investigated for instance for early cerebral palsy detection [START_REF] Rahmati | Videobased early cerebral palsy prediction using motion segmentation[END_REF], estimation of sleep stages [START_REF] Cabon | Audio-and video-based estimation of the sleep stages of newborns in neonatal intensive care unit[END_REF], or maturation characterization [START_REF] Cabon | Motion estimation and characterization in premature newborns using long duration video recordings[END_REF]. The latter is motivated by the fact that the motor activity evolves along with the age of the newborns [START_REF] Curzi-Dascalova | Physiological correlates of sleep development in premature and full-term neonates[END_REF]. Recently, analysis of motion estimated from ECG has been investigated for the detection of late-onset sepsis [START_REF] Joshi | Predicting neonatal sepsis using features of heart rate variability, respiratory characteristics and ecg-derived estimates of infant motion[END_REF]. Indeed, it has been shown that sepsis is related to lethargy, which can be observed as the absence of motion [START_REF] Verstraete | Prediction models for neonatal health care-associated sepsis: A metaanalysis[END_REF]. So it could be interesting to tackle this problem with videobased motion analysis.

Nevertheless, video-based motion analysis faces several challenges due to the real-life acquisition conditions in NICUs, particularly in the context of long-term monitoring. As it has been already highlighted in [START_REF] Villarroel | Noncontact physiological monitoring of preterm infants in the neonatal intensive care unit[END_REF], [START_REF] Cabon | Video and audio processing in paediatrics: a review[END_REF], before analyzing newborn motion, it is crucial to detect the periods when the newborn is absent and when an adult is present in the image frame. Indeed, in NICU, the newborn is regularly taken out of the bed in order to change the bedding or to have skin-to-skin contact with the parents. These periods must be discarded from the analysis because otherwise it may be wrongly interpreted as an absence of motion. Moreover, nurses regularly handle the newborn inside the bed, which generates irrelevant motion patterns in the image, so they must be discarded as well. In . the perspective of a fully automatic video-based monitoring system, it is essential to automatically detect the periods of interest, i.e. when the newborn is solely present in the image. Only a few papers have proposed to address this problematic in NICUs. In [START_REF] Cabon | Motion estimation and characterization in premature newborns using long duration video recordings[END_REF] only adult presence detection in open bed is considered, whereas in [START_REF] Villarroel | Noncontact physiological monitoring of preterm infants in the neonatal intensive care unit[END_REF] both newborn absence and adult presence are considered in incubator.

In this paper, we propose a video-based method for automatic detection of newborn presence in incubator or open bed. The goal is to label images of a video stream with three classes: "adult & newborn present," "newborn absent" and "newborn solely present." Our method is based on deep transfer learning. We use two binary classifiers: on the one hand, we detect the presence of the newborn, on the other hand, we detect the presence of adults. We add a strategy of decision intervals fusion in order to take advantage of temporal consistency. Finally, we fuse the decision of the two classifiers in order to get one of the three classes. One originality of this work is to address the problem of newborn presence detection both in incubator and open bed, thus the whole stay of the newborn in NICU can be analyzed with a common system.

This work is part of the Digi-NewB project, whose aim is to develop a monitoring system of preterm newborns, with a focus on late-onset sepsis detection and maturation characterization. It is the extension of a previously published work [START_REF] Weber | Deep transfer learning for video-based detection of newborn presence in incubator[END_REF] where we used the same workflow, but instead of learning two binary classifiers, we trained one single 3-class classifier. Moreover, in [START_REF] Weber | Deep transfer learning for video-based detection of newborn presence in incubator[END_REF] the method was only tested in incubator.

The remaining of this paper is organized as follows. After having introduced the related work, we describe our method for automatic detection of preterm newborn presence. Then, we describe the database00 that we used in our experiments. Next, we report the results of our experiments. Then, we discuss the limitations of our method. Lastly, we conclude the paper and give some perspectives of this work.

II. RELATED WORK

In the context of the Digi-NewB project, our team aims at developing a new non-invasive monitoring system using video analysis. Previous video-based works were mainly related to motion analysis using frame differencing [START_REF] Adde | Early prediction of cerebral palsy by computer-based video analysis of general movements: a feasibility study[END_REF], [START_REF] Orlandi | Detection of atypical and typical infant movements using computer-based video analysis[END_REF] or optical flow [START_REF] Ihlen | Machine learning of infant spontaneous movements for the early prediction of cerebral palsy: A multi-site cohort study[END_REF], [START_REF] Dosso | Video-based neonatal motion detection[END_REF], followed by extraction of features to characterize motor activity [START_REF] Cabon | Video and audio processing in paediatrics: a review[END_REF]. However, a major limitation to deploy these methods as a continuous monitoring technique is the presence of adults in the field of view of the camera or the absence of the newborn from the bed.

To overcome this difficulty, we proposed a first ad-hoc method to deal with adult presence detection in open bed [START_REF] Cabon | Motion estimation and characterization in premature newborns using long duration video recordings[END_REF]. The arrival and the departure of the adult in the image frame are detected by analyzing the change of motion in the image edge. A manual initialization is needed as the algorithm must start at a frame where the newborn is solely present in the image. The method has been tested on a database of nine newborns with a total of 149 hours of recording.

A similar approach has been proposed in [START_REF] Long | An efficient heuristic method for infant in/out of bed detection using video-derived motion estimates[END_REF] for infant presence detection in cradle. It has not been applied to recordings in NICUs but at home. Motion is estimated both inside and outside a region of interest surrounding the cradle. A set of temporal rules based on both motion estimates are designed in order to decide whether the infant is in or out of the bed. The method has been tested on a database of five infants with 77 recordings of 24 hours each.

The drawbacks of these two methods are related to the acquisition context in NICUs. If the camera is not optimally placed, the newborn may generate motion in the image edge, that would lead to a false adult detection. Noisy motion can also be generated in the image edge by abrupt changes of lighting. Moreover, the detection of adult presence may be missed when the adult is present but is not moving for a while. The same phenomenon may happen in the case of infant detection when he or she is not moving. These methods are not viable in the context of long-term monitoring in NICUs because the approach has to be robust to camera placement and lighting variations without any manual intervention.

In this context, image classification based on deep learning appears as a way to overcome these limitations. In particular, deep transfer learning is a powerful approach when the database has a lot of variety but the number of images is not large enough to train a neural network from scratch. This allows to benefit from the generalization capacities of a deep neural network. Transfer learning methods aim at transferring the knowledge of a source task to a target task. In the case of deep learning, a neural network is pre-trained for the source task. Then, the output layer performing classification is replaced by another classifier that is trained for the target task. The training dataset of the source task has usually a huge number of samples, which leads to a model with a good ability to generalize.

This technique has been used for a wide variety of target tasks [START_REF] Villarroel | Noncontact physiological monitoring of preterm infants in the neonatal intensive care unit[END_REF], [START_REF] Oquab | Learning and transferring mid-level image representations using convolutional neural networks[END_REF]- [START_REF] Rezende | Malicious software classification using VGG16 deep neural network's bottleneck features[END_REF], where the source task is usually ImageNet classification [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF]. The implementation choices lie mostly in the deep neural network and the output classifier, and are motivated by the target task. The most commonly used deep neural networks are Alexnet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] and VGG16 [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF]. The output classifier can be the output layer of the deep neural network [START_REF] Shin | Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning[END_REF], another neural network with typically one or two layers [START_REF] Villarroel | Noncontact physiological monitoring of preterm infants in the neonatal intensive care unit[END_REF], [START_REF] Oquab | Learning and transferring mid-level image representations using convolutional neural networks[END_REF], [START_REF] Gopalakrishnan | Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection[END_REF], or classical machine learning algorithms such as support vector machine, logistic regression or random forests [START_REF] Gopalakrishnan | Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection[END_REF], [START_REF] Rezende | Malicious software classification using VGG16 deep neural network's bottleneck features[END_REF].

In particular, deep transfer learning with VGG16 [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] is used in [START_REF] Villarroel | Noncontact physiological monitoring of preterm infants in the neonatal intensive care unit[END_REF] for newborn presence detection. Regarding adult presence detection, a two-stream network with ResNet50 [START_REF] He | Deep residual learning for image recognition[END_REF] is trained. It has been tested in incubator on a database of 15 newborns with a total of 214 hours of video recordings.

III. METHOD

In this section, we introduce our method of automatic video-based detection of newborn presence. First, we give an overview. Then, we detail each step of the method.

A. Overview

Fig. 1 gives an overview of the method. The idea is to break the problem down into two binary classifications that . are fused afterwards. On the one hand, there is the newborn presence detection regardless of the adult presence, referred to as "newborn" classification. On the other hand, there is the adult presence detection, referred to as "adult" classification. Binary classification is based on deep transfer learning. The images of the video stream pass through a pre-trained deep neural network in order to extract bottleneck features. These features are then used as the input of the binary classifier, which gives a sequence of decisions. In order to remove short peaks of detection that are assumed to be irrelevant, we adopt a strategy of decision intervals fusion.

Finally, the binary decisions are fused in order to get one of the following classes, as illustrated in Table I: • "adult & newborn present": both newborn and adult are present, • "newborn absent": newborn is absent, regardless of the adult presence, • "newborn solely present": newborn is present and adult is absent. We choose to define only one class when the newborn is absent because there are only a few images with newborn absent and adult present compared to the other classes.

B. Bottleneck features extraction

In the context of deep transfer learning, bottleneck features correspond to the knowledge of the source task. They are extracted from a deep neural network that has already been trained for a specific classification problem.

Fig. 2 illustrates bottleneck features extraction. First, the image must be pre-processed according to the input layer of the pre-trained network. Then it passes through the network but stops before the output layer, which is usually a fullyconnected layer performing the classification. Thus, we obtain the most high-level features of the pre-trained network and they are ready to be used for another classification problem.

In this paper, the selected source task is ImageNet [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF] as it proved to be powerful in deep transfer learning. We test three different deep neural networks:

• VGG16 [29], • MobileNetV2 [31],
• InceptionV3 [START_REF] Szegedy | Rethinking the inception architecture for computer vision[END_REF]. The pre-processing step consists in resizing the image to the shape 250 pixels x 250 pixels. MobileNetV2 requires a specific shape, so in this case it is 224 pixels x 224 pixels.

C. Classification

Two binary classifications are performed: "newborn" classification and "adult" classification (see Subsection III-A). The binary classifiers are trained for the target task and take as input the bottleneck features extracted from a pre-trained deep neural network.

In this paper, two kinds of classifiers are tested: support vector machine (SVM) with a linear kernel and small neural network (NN). The small neural network consists of one fullyconnected layer with the rectified linear unit as the activation function, followed by another fully-connected layer with softmax as the activation function. Droupout regularization is applied before this layer in order to prevent overfitting.

D. Decision intervals fusion

A binary classifier returns a sequence of decisions for a video. Let i be a class. We call "interval of class i" a subsequence where all the decisions are equal to class i. If the duration of the temporal interval that separates two successive intervals of class i is less than a threshold δ i , then the latter are fused by changing the decisions of the separating interval to class i. This process is done sequentially with a different threshold for each class.

Fig. 3 illustrates the step of decision intervals fusion for the positive class. There are three intervals for the positive class:

[t 1 , t 2 ], [t 3 , t 4 ] and [t 5 , t 6 ]. So, the separating intervals are [t 2 , t 3 ] and [t 4 , t 5 ]. Since t 3 -t 2 > δ 1 and t 5 -t 4 ≤ δ 1 , only the interval [t 4 , t 5 ] is changed to class 1.

E. Binary fusion

Let d n be the decision for "newborn" classification and d a be the decision for "adult" classification. The final decision d f is computed as follows:

d f =      "adult & newborn present," if d n = 1 & d a = 1, "newborn absent," if d n = 0, "newborn solely present," if d n = 1 & d a = 0.
(1) For an objective evaluation, the proposed approach was compared to two other methods [START_REF] Cabon | Motion estimation and characterization in premature newborns using long duration video recordings[END_REF], [START_REF] Weber | Deep transfer learning for video-based detection of newborn presence in incubator[END_REF] in Section V.

IV. DATABASE DESCRIPTION

This work is part of the Digi-NewB project in which a large database of video recordings has been acquired. For our experiments, we created two subsets from this database: the database of still images and the database of videos. The former will be used to train the binary classifiers and the latter to set the parameters of decision intervals fusion. Both of them are split with respect to the bed type: incubator and open bed.

In this section, we introduce these two databases. In the first subsection we focus on the data acquisition setup. In the following subsections we describe the content of the database of still images and then of the database of videos.

This study received ethics approval from the Ouest IV Ethics Committee (reference number 34/16) and one parent of each newborn gave its signed agreement to take part in it.

.

> δ 1 ≤ δ 1 t t 0 t 1 t 2 t 3 t 4 t 5 t 6

Fig. 3. Illustration of decision intervals fusion for the positive class

A. Data acquisition setup

In the scope of the Digi-NewB project, a dedicated audio and video acquisition device was designed [START_REF] Cabon | Voxyvi: A system for long-term audio and video acquisitions in neonatal intensive care units[END_REF]. In particular, it embedded two black & white infrared cameras with a resolution of 752x480 pixels (FMVU-03MTMCS), each one associated with an infrared illumination by four LEDs (VSMY3850 from Vishay). LEDs operate in the near infrared at 850 nm and have a spectral bandwidth of 30 nm. Having two cameras allows two different viewpoints of the newborn. Video streams were encoded with MPEG-4 encoding, under AVC container, at 25 frames per second. The protocol for camera positioning has been approved by the Biomedical Engineering Department of CHU Rennes. Furthermore, the Hygiene department of CHU Rennes helped us to elaborate a sterilization protocol.

When the newborn is in an incubator, it has been decided, in coordination with the doctors and nurses, to place the camera inside the incubator at the foot of the newborn, in order to be the least disturbing for the medical staff. Thus it enables to acquire images of good quality, with a narrow image view. In this context, the newborns are continuously monitored during one day up to 11 days.

When the newborn is in an open bed, there is more freedom in the camera placement, so the view of the newborn might be quite narrow or wide. Compared to the incubator, this leads to very different viewpoints of the newborn and the surrounding environment, which explains why we split the database according to the bed type. In the context of open bed, there are up to five recordings per newborn, each one Data was collected in six different NICUs during routine care with no constraints apart from very few instructions on camera placement, so there is a wide variety of background and lighting. The variety of the images is illustrated in Fig. 4 to 6, according to the three target classes. Fig. 4 shows example images of the class "adult & newborn present." The adult presence might be temporally short, for example when the nurse measures temperature, or long, for example during an intervention. In incubator, it is characterized by the emergence of the adult's arm. The hand may be hardly visible when emerging in a shaded area (see the top right image, inside the white circle). Sometimes it can be characterized by the emergence of the adult's body as well. In open bed, the encountered situations are much more varied than in incubator since the surrounding environment might occupy a large part of the image. For example in the image in the bottom right, the adult is standing up in the background. We can see that the cradle is near the bed of the parents, who might lay on it for several hours. Fig. 5 shows example images of the class "newborn absent." We encounter images with a blank mattress or the bed body. The adult may be present for changing the bedding (see the top right and bottom right images). Fig. 6 shows example images of the class "newborn solely present." In incubator, the newborn may be totally covered by the blanket or there may be a stuffed toy near the newborn (see the top right image in the left bottom corner). In the case . of the open bed, the bed might be in any orientation. During the night, the surrounding environment is not visible.

Table II describes the population of the Digi-NewB project that has been used in this study, including 120 newborns from the six different NICUs. Since there can be several recordings for one newborn, for each newborn we computed the cumulative recording duration and then we computed the mean and standard deviation along newborns. The distribution of total recording duration per NICU is as follows: 35.6%, 3.2%, 17.5%, 3.6%, 20.2%, 19.8,%. This reflects the respective ability of each NICU to include newborns and the differences of acquisition conditions in each NICU.

B. Database of still images

In order to select the deep neural networks for bottleneck features extraction and then train the binary classifiers (see Fig. 1), we created a database of still images with 57 newborns in incubator for a total of 4961 images and 60 newborns in open bed for a total of 3859 images. The following procedure was adopted for extracting the images: a set of images were randomly picked from the Digi-NewB database and has been manually annotated by one annotator with regards to the three classes (see Table I). Table III details the distribution of images in each class.

C. Database of videos

For validating and testing the whole process on videos, we created a database of videos split into a validation set and a test set for both bed types. All the video recordings have been manually annotated by one annotator with regards to the three classes (see Table I). The validation set is composed of newborns that are included in the validation set used for training the final binary classifiers, so they are included in the database of still images. The test set is composed of newborns that are not included in the database of still images.

Table IV details the content of the database of videos with the number of newborns, the total quantity of data in number of days and the distribution of the three classes. We can see that in incubator this distribution is similar between the validation set and the test set, whereas in open bed there is a lot of variety. The difference of data quantity between incubator and open bed is explained by the fact that, according to the protocol of the Digi-NewB project, for a single newborn a recording can last up to ten days in incubator, whereas in open bed there are up to five recordings, each lasting up to 24 hours and spaced out of ten days.

V. RESULTS

In this section, we report the results of our experiments. They are divided in two parts: the classification of still images . and the classification of videos. In the former we focus on the selection of the pre-trained network for bottleneck features extraction and the selection of the classifier. The latter allows us to focus on the parameters setting of decision intervals fusion and to assess the performance of the whole method.

Experiments are conducted with scikit-learn [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF] and keras with tensorflow backend.

A. Classification of still images 1) Protocol: As illustrated in Fig. 1, two binary classifications are considered. First, "newborn" classification focuses on newborn presence detection, regardless of adult presence: the negative class is "newborn absent" and the positive class is the union of "adult & newborn present" and "newborn solely present." We used class balancing during the training as the dataset is heavily unbalanced for this problem. Secondly, "adult" classification focuses on adult presence detection: the negative class is "newborn solely present" and the positive class is "adult & newborn present."

For each classification problem, we have to select the pretrained deep neural network for bottleneck features extraction on the one hand (see Subsection III-B) and the classifier on the other hand (see Subsection III-C). We used the database of still images (see Subsection IV-B) for the experiments. We adopted a 5-fold cross-validation strategy with preservation of classes proportion. For each fold, the database is split in three sets: train (72.25%), validation (12.75%) and test (15%). We make sure that a newborn is included only in one of the three sets. The performance is assessed with the following metrics expressed as a percentage: balanced accuracy, recall and specificity. We computed the mean metrics over the test sets of the cross-validation folds. The advantage of balanced accuracy is to provide an overall performance, while taking into account unbalance between the classes. Recall and specificity are not impacted by unbalance between the classes.

Regarding SVM, regularization parameter is set on validation set so that it maximizes balanced accuracy. Regarding NN, as a first step we chose the stochastic gradient descent (SGD) as optimizer with decay and momentum set to 0. We conducted a grid search on the following parameters:

• Size of the fully-connected layer: {64, 128, 256}, • Learning rate of SGD: {10 -1 , 10 -2 , 10 -3 , 10 -4 , 10 -5 }. Then, we kept the couple of parameters leading to the best balanced accuracy and we conducted another grid search on the following parameters:

• Optimizer: {SGD, rmsprop, adagrad, adam} • Decay of the optimizer: {0, 10 -2 , 10 -3 }, • Momentum of the optimizer (when applicable): {0, 0.5, 0.9}.

In the end we kept the set of parameters leading to the best balanced accuracy.

2) Results: Table V reports the performance of classification in incubator on the database of still images. Regarding "newborn" classification, the highest balanced accuracy is obtained with the combination of MobileNetV2 and SVM. Regarding "adult" classification, the highest balanced accuracy is obtained with the combination of InceptionV3 and NN, followed by the combination of InceptionV3 and SVM. The combination of MobileNetV2 and NN gives a better balanced accuracy than InceptionV3 and SVM but it does not offer a good trade-off between recall and specificity.

Table VI reports the performance of classification in open bed on the database of still images. Regarding "newborn" classification, the best balanced accuracy is obtained with the combination of VGG16 and NN, followed by the combination of VGG16 and SVM. Regarding "adult" classification, the best balanced accuracy is obtained with the combination of VGG16 and SVM. The performances are significantly lower than the other binary problems, which was to expect since the variety of situations is much wider.

For comparison purpose, both in incubator and open bed, we reported the performance of classification with logistic regression (LR) applied directly on the pixels of the image (resized to 250 pixels x 250 pixels). We can see that the performance is really poor with LR compared to deep transfer learning, which witnesses the fact that the good performance of our method is not due to a highly biased database, but due to the pre-trained neural network.

B. Classification of videos

1) Protocol: In incubator, for "newborn" classification we selected the combination of MobileNetV2 and SVM, for "adult" classification we tested the combination of Incep-tionV3 and both SVM and NN. In open bed, for "newborn" classification we tested the combination of VGG16 and both SVM and NN, for "adult" classification we selected the combination of VGG16 and SVM. The binary classifiers have been trained on the database of still images (see Subsection IV-B) with 80% of images for training and 20% for validation.

We used the database of videos (see Subsection IV-C) for assessing the performance of the whole process. We downsampled the videos to one image per second because of heavy processing. It does not cause loss of information as we do not seek to detect events that are shorter than one second.

. Regarding decision intervals fusion, there are three parameters to set (see Subsection V-A.1 for the definition of the classification problems):

• δ n 0 for negative class of "newborn" classification, • δ n 1 for positive class of "newborn" classification, • δ a 1 for positive class of "adult" classification. In our database, we observed several situations where the presence of an adult lasts for only one second. So, we decided not to perform decision intervals fusion on the negative class of "adult" classification ("newborn solely present") in order to avoid changing the class of images with an adult present to "newborn solely present."

The thresholds were set separately for incubator and open bed thanks to a grid search with the following values: δ n 0 and δ n 1 ranging from 0 second to 30 seconds with a step of 2 seconds, δ a 1 ranging from 0 second to 120 seconds with a step of 5 seconds. We selected the triplet (δ n 0 , δ n 1 , δ a 1 ) that leads to the best balanced accuracy on the validation set.

2) Results: For "newborn" classification, the grid search showed that δ n 0 allows to decrease the recall and increase the specificity, whereas δ n 1 has the opposite effect with less impact. A combination of the two can lead to an increase in the balanced accuracy. First, δ n 0 is used to significantly increase the specificity, then the induced decrease of the recall is compensated with δ n 1 . In our experiments, the impact of δ n 0 stops being significant above 10 seconds. The optimal values are δ n 0 = 4 seconds and δ n 1 = 30 seconds in incubator, δ n 0 = 8 seconds and δ n 1 = 28 seconds in open bed. The choice of limiting the value of δ n 1 below 30 seconds is arbitrary. In open bed, SVM outperformed NN when applied on videos, so we chose to report the results with SVM.

Regarding "adult" classification, the grid search showed that . δ a 1 allows to increase the recall and decrease the specificity while increasing the balanced accuracy. The optimal values are δ a 1 = 40 seconds in incubator and δ a 1 = 120 seconds in open bed. In incubator, SVM outperformed NN when applied on videos, so we chose to report the results with SVM.

We compared the performance of our method with our previous work [START_REF] Weber | Deep transfer learning for video-based detection of newborn presence in incubator[END_REF] and the method of [START_REF] Cabon | Motion estimation and characterization in premature newborns using long duration video recordings[END_REF]. In our previous work, we used the same technique of deep transfer learning, but instead of training two binary classifiers, we trained one 3-class classifier. The method of [START_REF] Cabon | Motion estimation and characterization in premature newborns using long duration video recordings[END_REF] only deals with adult detection, so the detection of the newborn is the manual annotation and we do not report balanced accuracy and recall of absent". The detection of the adult is performed by analyzing the motion in the edge of the image and requires a manual initialization.

Table VII reports the performance of classification in incubator. The decision intervals fusion allows to increase the balanced accuracy, particularly on the test set, while increasing recall of "adult & newborn present" and decreasing recall of "newborn solely present." Since our priority in our application context is to minimize the number of false positives with regards to "newborn solely present," it is desirable to increase the recall of "adult & newborn present" and "newborn absent." Compared to the 3-class model [START_REF] Weber | Deep transfer learning for video-based detection of newborn presence in incubator[END_REF], the combination of binary classifiers allows to significantly increase the recall of "newborn absent," while keeping a similar recall on the two other classes. Compared to [START_REF] Cabon | Motion estimation and characterization in premature newborns using long duration video recordings[END_REF], our method reaches a greater recall of "adult & newborn present" and a lower recall of "newborn solely present," so we have a slightly better ability to detect adult presence.

Table VIII gives the mean confusion matrix computed on the test set in incubator. We can see that most of the errors occur mainly when incorrectly classifying "adult & newborn present" as "newborn solely present" and vice versa.

Table IX reports the performance of classification in open bed. The decision intervals fusion allows to increase balanced accuracy both on the validation set and the test set by significantly increasing the recall of "adult & newborn present" and decreasing the recall of "newborn solely present," which is desirable in our application context. Compared to the incubator, the performance is very low and there is a poor [START_REF] Weber | Deep transfer learning for video-based detection of newborn presence in incubator[END_REF]. In this context, the method of [START_REF] Cabon | Motion estimation and characterization in premature newborns using long duration video recordings[END_REF] based on motion analysis in the image edge could be more suitable for adult detection, but it would need to cope with two difficulties. First, it may happen that the light is often turned on and off, which generates motion in the image edge and leads to an incorrect adult detection. Thus this affects the recall of "newborn solely present." Secondly, the parent may lie motionless on the bed for a long time, which leads to adult presence not detected. Thus this affects the recall of "adult & newborn present." Table X gives the confusion matrix computed on the test set in open bed. We can see that regarding "adult & newborn present" and "newborn solely present," there are more images misclassified as "newborn absent" compared to the incubator (see Table VIII). Moreover, there is a more significant percentage of "newborn absent" images that are misclassified as "newborn solely present" compared to the incubator (see Table VIII).

VI. DISCUSSION

Overall, this work shows that a deep transfer learning approach is relevant for the detection of periods of sole presence of the newborn. Indeed, a good generalization of the model is obtained in incubator with more than 86% of balanced accuracy. The results are weaker in open bed but are still encouraging since it is the first time that the problems of adult presence detection and newborn presence detection have been considered together.

. The development of a method for automatic preterm newborn presence in NICU is a relatively new field of research. To date, the closest work to our study is [START_REF] Villarroel | Noncontact physiological monitoring of preterm infants in the neonatal intensive care unit[END_REF]. In this work, two systems are developed: one for newborn detection (equivalent our "newborn" classification) and one for intervention detection (equivalent to our "adult" classification). Their study took place in one NICU. Regarding "newborn" classification, they reached an accuracy of 98.8%, a recall of 100% and a specificity of 96.8%. Regarding "adult" classification, they annotated 214 hours of video recordings and reached an accuracy of 94.5%, a recall of 94.7% and a specificity of 94.4%. For both classification problems, their performances are higher than in our study. But it is worth to keep in mind that in [START_REF] Villarroel | Noncontact physiological monitoring of preterm infants in the neonatal intensive care unit[END_REF], only video recordings in daylight illumination were considered, whereas we include both daytime and nighttime in our study. Moreover, in our study, there are six different NICUs and the method has been validated and tested on a larger database of videos with more than 1300 hours of annotated video recordings in incubator. So there is a broader variety of situations in our study which might explain the weaker performance.

However, some limitations can be identified in our study and offer prospects for improvement. For instance, potential sources of bias such as capture bias, negative bias and category bias can be discussed [START_REF] Torralba | Unbiased look at dataset bias[END_REF], [START_REF] Tommasi | Domain adaptation in computer vision applications[END_REF].

First of all, although capture bias had been limited in our study thanks to the high diversity in collected images, we noticed a drop between the validation and the test set with regards to the class "adult & newborn present" in incubator (see Table VII). This may denote a lack of generalization of our model for this particular class. To cope with this difficulty, we could enhance the database of still images, which is used to train the binary classifiers.

Secondly, the negative bias induced by the two binary classifiers is restricted due to the clinical context of our application. Indeed, the situation where both newborn and adult are absent mostly concerns empty rooms with no activity, so that the diversity of this class is mainly based on the disparity of the viewpoints and the background of the rooms. However, the possibility of obstruction of the video camera was not taken into account and this may be a significant negative class bias. One way to solve this problem would be to integrate a class with obstructed camera into the learning process.

Thirdly, there may be a category bias in open bed as witnesses the poor performance in this case. We investigated this aspect further by splitting the results according to the type of open bed: warm radiant (see the right image in Fig. 4(b)) and cradle (see the left image in Fig. 4(b)). For the latter, the newborn is almost full-term and the cradle is near the parents' bed, which is partly visible in the image. These recordings are particularly difficult to analyze because of a wider variety of situations compared to recordings in warm radiant. We have noticed that the model has a better ability to generalize in warm radiant than in cradle. In future work, it may be relevant to split the data in open bed into two parts (warm radiant and cradle) and train two separate models. But this would require a much larger database of still images in order to cope with the wide variety of encountered situations.

Another limitation is that we take into account the full image which may cause the discard of relevant periods in clinical applications such as newborn motion analysis. Indeed, in open bed, images with an adult in the background would be discarded, despite the fact that the area of the newborn is not impacted by this presence. In order to be able to detect the adults only when they are manipulating the newborn, we could perform bed segmentation to extract the region of interest. Thus, this region would be the input for adult's detection and newborn motion estimation. The difficulty with this approach is that when the adult is in the background, he still may be visible next to the bed because of the open bed transparency.

VII. CONCLUSION

In the context of long-term video-based monitoring of preterm newborn activity in NICU, it is of primary importance to be able to accurately detect the presence of the newborn prior to automatic analysis such as motion estimation. In this paper, we introduced a new method of video-based detection of preterm newborn presence in incubator and open bed. To our knowledge, this is the first method exploiting videos acquired during standard clinical care of several NICUs. This problem was addressed as a classification problem and we proposed to fuse the decisions of two binary classifiers: one for newborn presence detection, the other one for adult presence detection. They both follow the same workflow based on deep transfer learning: bottleneck features, extracted from a pre-trained deep neural network, are used as input of a classifier. We then adopted a strategy of decision intervals fusion in order to take advantage of temporal consistency. A comparative study between several bottleneck features (extracted from VGG16, MobileNetV2 and InceptionV3 deep neural networks) and classifiers (SVM, NN) was conducted.

As a result, we obtained a good performance in incubator with a balanced accuracy of more than 86% on both the validation set and test set. This is a satisfying result and our method will be used in a clinical trial [START_REF]NEOVIDEO : Impact of monitoring motor activity by video analysis on the sleep of very preterm infants[END_REF]. Regarding open bed, the balanced accuracy on the validation set reached almost 80% but dropped to 58% on the test set, which witnesses the fact that there are a lot more of variability in open bed whereas less data are available. This result is not satisfying and is not sufficient for a use in a clinical application involving open bed.

Further works could focus on methodological aspects and clinical applications. For the first point, the use of classifiers . or neural networks that take into account temporal information -for example, to model arrival and the departure times of the adults-, is one perspective direction. Additionally, in open bed, our method could benefit from a greater amount of data in order to model the wide variety of situations. For the second point, the proposed approach will be used in order to automatically analyse newborn motion, which is of primary importance for different clinical applications, including late-onset sepsis detection and evaluation of neurobehavioral development.

ACKNOWLEDGMENT

Results incorporated in this publication received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 689260 (Digi-NewB project).

Fig. 1 .Fig. 2 .

 12 Fig. 1. Overview of our method for automatic video-based detection of newborn presence

Fig. 4 .

 4 Fig. 4. Example images of the class "adult & newborn present"

Fig. 5 .

 5 Fig. 5. Example images of the class "newborn absent"

TABLE II SUBSET

 II OF THE POPULATION OF DIGI-NEWB USED FOR THIS STUDY. GA STANDS FOR GESTATIONAL AGE. PMA STANDS FOR POST-MENSTRUAL AGE. STD STANDS FOR STANDARD DEVIATION AND IS GIVEN IN NUMBER OF DAYS (FOR GA, PMA, RECORDING DURATION) OR GRAMS (BIRTH WEIGHT).

	Number of newborns (% male)	120 (60%)
	Mean birth weight in grams (STD) 1319.71 (681.17)
	Mean GA (STD)	29+3 (28.94)
	Mean PMA (STD)	33+0 (35.09)
	Mean total recording duration per newborn in days (STD)	7.81 (8.01)

TABLE III DISTRIBUTION

 III OF THE CLASSES IN THE DATABASE OF STILL IMAGES

	Class	Number of images Incubator Open bed
	"adult & newborn present"	2150	1361
	"newborn absent"	1346	1370
	"newborn solely present"	1465	1128
	Total	4961	3859

TABLE IV DISTRIBUTION

 IV OF THE CLASSES IN THE DATABASE OF VIDEOS. ANP STANDS FOR THE CLASS "ADULT & NEWBORN PRESENT." NA STANDS FOR THE CLASS "NEWBORN ABSENT." NSP STANDS FOR THE CLASS "NEWBORN SOLELY PRESENT." STD STANDS FOR STANDARD DEVIATION.

			Validation	Test
		Number of newborns	4	7
	Incubator	Data quantity (days) Mean % ANP (STD) Mean % NA (STD)	28.02 8.69 (1.60) 11.58 (8.76)	28.48 8.24 (3.76) 9.42 (4.34)
		Mean % NSP (STD)	79.73 (9.77)	82.34 (6.81)
	Open bed	Number of newborns Data quantity (days) Mean % ANP (STD) 22.24 (21.87) 22.34 (16.54) 5 9 3.17 7.05 Mean % NA (STD) 14.14 (6.01) 31.33 (15.83)
		Mean % NSP (STD) 63.62 (20.23) 46.33 (19.00)

TABLE V PERFORMANCE

 V OF BINARY CLASSIFICATION IN INCUBATOR ON THE DATABASE OF STILL IMAGES

				"newborn" classification	"adult" classification
	Bottleneck Classifier Set	Balanced accuracy	Recall Specificity	Balanced accuracy	Recall Specificity
	None	LR	Val Test	55.71 56.00	65.82 69.88	45.60 42.11	52.37 54.09	60.39 63.28	44.35 44.91
	VGG16	SVM NN	Val Test Val Test	87.63 89.60 85.02 86.15	92.85 92.81 92.74 92.64	82.41 86.38 77.30 79.67	82.37 82.39 77.90 76.06	83.83 84.76 81.35 83.02	80.90 80.01 74.45 69.10
	Mobile-	SVM	Val Test	89.91 91.21	91.96 92.85	87.86 89.57	82.35 81.22	86.17 87.38	78.52 75.06
	NetV2	NN	Val Test	89.41 91.03	93.62 94.48	85.19 87.58	82.94 84.40	96.13 87.59	79.75 77.20
	Incep-	SVM	Val Test	88.37 88.16	90.25 90.03	86.48 86.29	82.74 83.50	85.08 84.04	80.39 82.96
	tionV3	NN	Val Test	88.64 89.68	94.47 93.79	82.81 85.57	84.06 85.36	86.64 85.36	81.48 85.13

TABLE VI PERFORMANCE

 VI OF BINARY CLASSIFICATION IN OPEN BED ON THE DATABASE OF STILL IMAGES

				"newborn" classification	"adult" classification
	Bottleneck Classifier Set	Balanced accuracy	Recall Specificity	Balanced accuracy	Recall Specificity
	None	LR	Val Test	49.55 51.10	60.41 55.48	38.69 46.72	60.39 62.36	63.75 63.65	57.04 61.08
	VGG16	SVM NN	Val Test Val Test	87.21 82.97 88.07 83.98	86.96 83.54 89.55 83.36	87.46 82.39 86.59 84.60	73.01 74.72 70.37 69.63	72.37 78.81 73.03 77.19	73.64 70.62 67.70 62.08
	Mobile-	SVM	Val Test	84.69 79.46	82.14 76.33	87.24 82.59	74.92 74.25	81.03 75.22	68.81 73.29
	NetV2	NN	Val Test	84.57 80.46	86.99 82.61	82.16 78.30	73.77 72.72	78.43 73.56	69.10 71.88
	Incep-	SVM	Val Test	86.16 81.94	83.36 76.76	88.97 87.12	68.39 69.00	77.65 72.13	59.13 65.86
	tionV3	NN	Val Test	86.24 81.68	89.04 83.87	83.45 79.49	70.87 72.99	72.04 71.30	69.70 74.69

TABLE VII PERFORMANCE

 VII OF CLASSIFICATION ON VIDEOS IN INCUBATOR. FUSION STANDS FOR DECISION INTERVALS FUSION. BA STANDS FOR BALANCED ACCURACY. R1, R2 AND R3 STANDS RESPECTIVELY FOR RECALL OF "ADULT & NEWBORN PRESENT," "NEWBORN ABSENT" AND "NEWBORN SOLELY PRESENT" CLASSES.

	Method Fusion Set	BA	R1	R2	R3
	Motion [13]	No	Val Test	--	87.78 80.99	--	88.98 90.98
	3-class [17]	Yes	Val 85.09 90.48 78.86 85.93 Test 80.50 82.47 75.42 83.60
	Propo-sed method	No Yes	Val 86.09 79.98 88.83 89.45 Test 80.91 65.68 88.27 88.77 Val 88.77 93.13 89.23 83.94 Test 86.02 84.37 89.18 84.50

TABLE VIII MEAN

 VIII CONFUSION MATRIX COMPUTED ON TEST SET OF THE DATABASE OF VIDEOS IN INCUBATOR. ANP STANDS FOR THE CLASS "ADULT & NEWBORN PRESENT." NA STANDS FOR THE CLASS "NEWBORN ABSENT." NSP STANDS FOR THE CLASS "NEWBORN SOLELY PRESENT."

				Prediction
			ANP	NA	NSP
	Ground	truth	ANP 84.37% 2.64% 12.99% NA 4.21% 89.18% 6.60% NSP 12.48% 3.02% 84.50%

TABLE IX PERFORMANCE

 IX OF CLASSIFICATION ON VIDEOS IN OPEN BED. FUSION STANDS FOR DECISION INTERVALS FUSION. BA STANDS FOR BALANCED ACCURACY. R1, R2 AND R3 STANDS RESPECTIVELY FOR RECALL OF "ADULT & NEWBORN PRESENT," "NEWBORN ABSENT" AND "NEWBORN SOLELY PRESENT" CLASSES.

	Method Fusion Set	BA	R1	R2	R3
	Motion [13]	No	Val Test	--	68.21 68.94	--	78.90 54.72
	3-class [17]	Yes	Val 72.91 62.15 77.69 78.90 Test 57.44 58.95 64.98 55.62
	Propo-sed method	No Yes	Val 73.85 51.81 86.26 83.46 Test 56.49 42.84 68.47 65.78 Val 79.48 76.16 89.67 72.61 Test 58.46 56.46 69.16 57.45
	ability to generalize since the balanced accuracy drops by
	21.02% between the validation set and the test set. The same
	conclusion applies to the 3-class method

TABLE X MEAN

 X CONFUSION MATRIX COMPUTED ON TEST SET OF THE DATABASE OF VIDEOS IN OPEN BED. ANP STANDS FOR THE CLASS "ADULT & NEWBORN PRESENT." NA STANDS FOR THE CLASS "NEWBORN ABSENT." NSP STANDS FOR THE CLASS "NEWBORN SOLELY PRESENT."

				Prediction
			ANP	NA	NSP
	Ground	truth	ANP 56.46% 17.12% 26.41% NA 8.42% 69.16% 22.41% NSP 19.86% 22.69% 57.45%

WEBER et al.: PRETERM NEWBORN PRESENCE DETECTION IN INCUBATOR AND OPEN BED USING DEEP TRANSFER LEARNING

received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 689260 (Digi-NewB project).


Version anglaise Introduction (slide 1)

Good morning, I'm olivier crasset sociologist at the university of Nantes in FRance In this paper, I am going to address the issue of the seasonality of work in home-help services and this is a question that, to my knowledge, has not been dealt with so far in the sociological literature, at least in France, but which is well known to the field actors I met. It was while investigating about something else that conversations indicated to me that this topic was a concern among professionals. Serendipity is therefore at the origin of this paper. What do I mean by "seasonality"? It refers to the fact that a phenomenon fluctuates according to an annual rhythm, this variation being linked to natural phenomena, such as weather seasons, but above all to social constructions (such as the date of vacations). This question has long been addressed by sociology. Since Emile Durkheim (Le suicide) and Marcel Mauss (Essais sur les variations saisonnières des sociétés eskimos, 1904), we know that suicides, marriages, and food consumption follow a seasonal cycle and that the organization of society is sometimes affected by this cycle. As for seasonality of work, it is well studied, for example in the tourism (Guibert, 2012) 1 and agriculture sectors (Morice and Michalon, 2008 2 ), but I have not found any publications on home help. However, it will be seen that seasonality plays an important role in some of the issues related to the homecare labour market.

annonce du plan

After presenting the survey methodology, I will describe the territory I surveyed. Then I will show the tensions that exist in the home help job market and I will propose some elements to explain their existence. Finally, I will describe two of its consequences among others : 1rst the deterioration of working conditions and 2 nd the evolution of the relationship between the profit and the non-profit sectors.

Méthodologie (slide 2)

To do this, I conducted about twenty interviews with actors in the sector: home help workers, nurses, social workers, managers of profit and non-profit services, municipal officials and shopkeepers. In addition to this, I conducted an ethnographic survey3 where I accompanied home helpers during their work day, participating in it whenever possible, which also allowed me to meet the elderly beneficiaries of the service and their families. The idea was to go through the whole organization of the work from the social workers to the person being helped.

Description territoire : vieux et vieillissant (slide 3)

The studied territory (communauté d'agglomération) is located in the North-West of France. It has about 100,000 inhabitants living in about fifty municipalities, some of which are located on the seafront. Its economy is mainly based on tourism and the food industry. From a demographic point of view, it is an area that is aging faster than the French national average. (Ageing index in 2016 = 1,44 versus 0,8 for metropolitan France) The ageing index climbs up to 3 in the areas closest to the sea, wich is why the survey focused on them.

Ageing index : Number of older people (65 and older) per 100 children and youth (0-20 years)

Aging is linked to the aging of the local population to which must be added the arrival of retirees who come to live by the sea. These people are often natives who return home after a career spent in other regions, or individuals who permanently occupy what was until then their second home. So , the territory is one of the most attractive in France for the retirees.

Climate change promises new arrivals because it is put forward as an argument to promote the territory to attract the elderly who are afraid to go to the south, too exposed to the heat waves.

According to demographic projections, the aging process is expected to continue at least until 2050.

As can be expected, these arrivals cause a gentrification of the coastal zone.

(rem : je reste assez peu précis sur les chiffres pour garantir l'anonymat du territoire)

marché du travail (slide 4)

At the national level, home help jobs are one of the ten activities designated by the unemployment services (Pole Emploi) as "jobs under stress", i.e. where job offers do not find takers.

Locally, the situation is made worse by two factors. Firstly, young families are no longer settling on the coast because of the dissuasive price of real estate, which is constantly rising, and the disappearance of certain services (school closures). However, home help workers don't want to work far from their homes because of the travel costs associated with the use of their personal vehicles and breaks during the work day.

Secondly, the home-help sector competes with the food industry and the hotel and restaurant sector, which I said were important in the local economy. These sectors are accessible to a workforce with little or no qualifications whose profile is close to that of home help. The need for workforce fluctuates over the course of the year, following a rhythm close to that of home help work.

There is a scissor effect, i.e. an increase in the demand for labor in a context where it is less and less available. This situation creates very strong tension in the market for home help in coastal areas throughout the year. It sets the context in which the question of seasonal fluctuations in work arises.

Variations saisonnières

More needs : 1. School vacations are critical periods, especially from the spring when residents of secondary residences (average age: 65 years old, 39% >70 years old) arrive and call on home help services. These are mostly elderly people who come to spend the summer season at the sea and ask for help, whether they are dependent or for comfort services. 2. Another phenomenon contributes to increasing the demand for care during vacation periods. During their summer stay, children of elderly people living in the territory come to visit their parents and sometimes become aware that their need for help has increased since their last visit. As a result, some of them submit a request or a reassessment of the assistance plan5 and are immediately looking for solutions while waiting for help to be put in place by the social services. A peak in the demand for assistance appears in July-August6 when the social workers are sometimes on vacation.

Less workforce available 1. The homecare sector is not the only one that is under pressure in the territory. The hotel and restaurant sector offer more jobs than there are candidates to fill them (Pôle Emploi, 2019: 11). Home help is a sector that offers poorer working conditions compared to other accessible sectors with a low level of education. This competition is exacerbated during vacation periods when seasonal employment in tourism is at its highest. 2. In addition, family rhythms affect the usual balances. Home help workers who are mothers prefer to look after their children who are on vacation, since the cost of childcare in recreational centers can be a dissuasive factor. This problem is amplified for single-parent families.

→ The scissor effect is thus reinforced during the summer months.

Conséquences (slide 5)

This situation has consequences on several levels.

5/12

I will develop two of them: the deterioration of working conditions and the relations between the profit and non-profit sectors. But other questions may arise like giving up vacations for some tourists or the reduction of the care of the beneficiaries to a minimum.

dégradation des conditions de w en été

The working conditions of employed home help workers are deteriorating in several aspects. For these highly mobile professionals, the difficulties of car traffic increase travel times, parking difficulties and cause significant stress. The situation becomes more complicated during market days or festivities when certain roads are cut off. It is then necessary to negotiate with the authorities to be able to access the beneficiaries' homes. Since the badges affixed to windshields have no legal value, free passage and the punishment of parking violations are left to the discretion of the police.

The time span of the days is lengthened by the difficulties of circulation, but especially by the increased number of elderly people to be cared for. The very high rate of part-time contracts (91%) makes it possible to increase working time to reach that of a full-time employee during critical periods.

In this context, the question of vacation or sick leave replacements poses serious problems and some employers encourage employees not to take them in the summer. The moral obligation to provide service, which is completely internalized by the vast majority of professionals, pushes them to go beyond their limits and the psychological risks lead to burn out and a high turn over.

Relations entre le secteur marchand et non-marchand

Since 2006, the home help market has been open to competition. Public and associative services are not in a position to absorb additional work. Demand is therefore shifting to private companies that are easily used by the wealthiest people, especially those in second homes. But the lack of workforce does not allow companies to meet all the demands. The public, associative and private actors manage the shortage by coordinating their actions to focus on the most problematic situations. Continuity of care often means breaking the rules. For example, a company that cares for a nondependent elderly person continues to do so when that person becomes dependent, even though it is not authorized to address this public. This situation, which is supposed to last as long as the assistance plan is put in place, continues while, at the same time, the enterprise is under the threat of sanctions for deviating from the law.

This example shows that the private sector does not disengage itself from the more "social" part of the work because it is involved in a local network of professionals guaranteeing the respect of certain values to which the private sector remains attached (bad image of those who make profit from fragile people, strong sensitivity to social inequalities, left-wing political culture).

While wealthy people in secondary residences have relatively little difficulty in finding the services necessary for their health and comfort, some members of the working classes are now giving up their vacations on the territory. This first sign of growing social inequalities is not alarming insofar as the phenomenon only concerns leisure. But its amplification would affect the care of the poorest dependent people living in the area all year round if economic logic creates competition between these social groups.

The difficulties encountered by public services and associations encourage the development of activities by private companies, which are more responsive and more flexible in their management. Even if most of them show real concern for the well-being of their clients, their presence reinforces an economic rather than a social logic by devoting part of their activity to the most solvent applicants.

Conclusion (slide 6)

Seasonality of work has very real effects; it contributes to increasing tensions in the labor market by reinforcing the scissors effect.

The results presented here have limitations; they are based on the study of a territory that is not chosen at random: the phenomenon is very visible there. But the expected aging of the population in France may lead to the same situation elsewhere. In the territory studied, it will undoubtedly worsen in the coming years. Indeed, according to the interviewees, seasonality has been less noticeable in recent years because the labor market is now tight all year round. The situation today is that of a permanent crisis, with peaks during vacation periods (summer but also end of year and Easter). In this context, the seasonality of work is sometimes the last straw.

Nevertheless, economic factors could play in favor of local development if we consider that offering services to vacationers is an asset for developing tourism or at least for not discouraging them from visiting the region7 .

To conclude, I come back to the title of this paper, the phenomenon is indeed a women's story because it is women who are the professionals of home help (98%) but also social workers. But not only. They are also very present as mothers who take care of children on vacation, or as family helpers for a dependent relative. These are women at work in a job that is sometimes paid (as productive work), but also a mother's family work (as reproductive work) or caring for a relative. This could lead to a reflexion on parity in these professions. Thank you.

Version française Introduction (slide 1)

Dans cette communication, je vais aborder la question de la saisonalité du travail dans l'aide à domicile et c'est une question qui, à ma connaissance, n'a pas été traitée jusqu'ici dans la littérature sociologique, au moins en France, mais qui est bien connue des acteurs de terrain que j'ai rencontrés. C'est en enquêtant à propos d'autre chose qu'au fil des conversations, je me suis rendu compte que ce sujet était une préoccupations chez les professionnelles. La sérendipité est donc à l'origine de ce papier.

Qu'est-ce que j'entends par "saisonalité" ? Ça désigne le fait qu'un phénomène fluctue selon un rythme annuel, cette variation étant liée à des phénomènes naturels, comme les saisons météorologiques, mais surtout à des constructions sociales (comme la date des vacances). Cette question a été abordée de longue date par la sociologie. Depuis Emile Durkheim (Le suicide) et Marcel Mauss (les eskimos), on sait que les suicides, les mariages, les consommations alimentaires suivent un cycle saisonnier et que l'organisation de la société est parfois affectée par ce cycle. Quant à la saisonalité du travail, elle est bien connue, par exemple dans les secteurs du tourisme (Guibert, 2012)8 et de l'agriculture (Morice et Michalon, 20089 ) mais je n'en ai pas trouvé de traces en ce qui concerne l'aide à domicile. Or, on va voir que la saisonalité joue un rôle important dans certaines questions liées au marché du travail de l'aide à domicile.

annonce du plan

Après avoir présenté la méthodologie de l'enquête, je vais décrire le territoire sur lequel j'ai enquêté. Puis je montrerai les tensions qui existent sur le marché du travail de l'aide à domicile et je proposerai quelques éléments d'explication de leur existence. Pour terminer, je décrirai deux de ses conséquences parmi d'autres (la dégradation des conditions de travail et l'évolution des rapports entre le secteur marchand et non-marchand).

Méthodologie (slide 2)

Cette recherche s'inscrit dans le programme ANR Profam (https://profam.hypotheses.org/) qui, depuis 2018, s'intéresse au travail d'aide aux personnes dépendantes, le travail étant considéré sous toutes ses formes, qu'eles soient familiales ou professionnelles. Dans ce cadre, j'ai enquêté sur plusieurs sujets dans le territoire dont il va être question (et dont le nom ne sera pas dévoilé). Les données sur la saisonalité ont été recoltées tout au long de la période d'enquête, mais l'été 2019 a été consacré spécifiquement à étudier la question de la saisonalité. Pour cela, j'ai réalisé une vingtaine d'entretiens auprès d'acteurs du secteur : aide à domicile, infirmières, social workers, cadres de services lucratifs ou non-lucratifs, élus municipaux et commerçants.

8/12

A cela s'ajoute une enquête ethnographique10 où j'ai accompagné des aides à domicile lors de leur journée de travail en participant à celui-ci à chaque fois que c'était possible, ce qui m'a aussi permis de rencontrer les personnes âgées bénéficiaires du service et leurs familles. L'idée était de parcourir toute l'organisation du travail depuis les travailleurs sociaux jusqu'à la personne aidée.

Description territoire : vieux et vieillissant (slide 3)

Le territoire étudié (communauté d'agglomération) se situe dans l'Ouest de la France. Il regroupe environ 100 000 habitants répartis dans une cinquantaine de communes dont certaines en bord de mer. Son économie repose principalement sur le tourisme et sur l'industrie agro-alimentaire. Du point de vue démographique, c'est un territoire qui vieillit plus vite que la moyenne française (indice de vieillissement = 1,411 contre 0,9 pour la région), parfois supérieur à 3 dans les zones les plus proches de la mer.

Indice de vieillissement = nbre d'individus de 65 ans et + nbre d'individus < 20 ans Le vieillissement est lié à celui de la population locale auquel il faut ajouter l'arrivée de retraités qui viennent s'installer en bord de mer pour y séjourner toute l'année, provoquant une gentrification de la côte. D'après les projections démographiques, le vieillissement devrait se poursuivre au moins jusqu'en 2050.

(rem : je reste assez peu précis sur les chiffres pour garantir l'anonymat du territoire) marché du travail (slide 4)

Les métiers de l'aide à domicile font partie des dix activités désignées par les services du chômage (Pole Emploi) comme des "métiers en tensions", çàd où les offres d'emploi ne trouvent pas preneurs.

Localement, la situation est aggravée par deux facteurs. Premièrement, les jeunes ménages ne s'installent plus sur la côte en raison du prix dissuasif de l'immobilier en hausse permanente et de la disparition de certains services (fermetures d'écoles). Or, les aides à domicile ne s'éloignent pas de leur domicile pour travailler en raison des frais de déplacement liés à l'utilisation de leur véhicule personnel et aux coupures dans la journée de travail. Deuxièmement, le secteur de l'aide à domicile est concurrencé par ceux de l'agroalimentaire et de l'hôtellerie-restauration dont j'ai dit qu'ils étaient important dans l'économie locale. Ces secteurs sont accessibles à une main d'oeuvre peu ou pas diplômée dont le profil est proche de celui des aides à domicile. Les besoins en main d'oeuvre y fluctuent au cours de l'année en suivant un rythme proche de celui de l'aide à domicile.

On assiste à un effet de ciseaux, c'est-à-dire une augmentation de la demande de main d'oeuvre dans un contexte où celle-ci est de moins en moins disponible.

10/12

Les aides à domicile en emploi voient leurs conditions de travail se dégrader sous plusieurs aspects.

Conclusion (slide 6)

La saisonnalité du travail a des effets bien réels, elle contribue à augmenter les tensions sur le marché de l'emploi en renforçant l'effet de ciseaux.

entre le secteur marchand et non-marchand

  Pour ces professionnelles très mobiles, les difficultés de circulation automobile augmentent les temps de trajet, les difficultés de parking et provoquent un stress important. La situation se complique lor des jours de marché ou de festivités à l'occasion desquelles certaines routes sont coupées. Il faut alors montrer patte blanche aux autorités pour pouvoir accéder au domicile des bénéficiaires. Les macarons apposés sur les pare-brise n'ayant aucune valeur légale, le libre passage et la sanction des infractions de parking sont laissés à l'appréciation de la police. L'amplitude horaire des journées est allongée par les difficultés de circulation, mais surtout par le nombre accru de personnes âgées dont il faut prendre soin. Le taux très important de CDD (91% selon l'étude GPECT) permet d'augmenter le temps de travail pour atteindre celui d'un temps plein aux périodes critiques.Dans ce contexte, la question des remplacements pour congés pose de sérieux problèmes et certains employeurs incitent les salariées à ne pas les prendre en été. L'obligation morale d'assurer le service, tout à fait intériorisée par la grande majorité des professionnels, elle les pousse à dépasser leurs limites et les risques psychologiques entrainent des burn out et un fort turn over.Depuis 2006, le marché des services à la personne est ouvert à la concurrence. Les services publics et associatifs ne sont pas en capacité d'absorber de travail supplémentaire. La demande se reporte donc sur les entreprises privées auxquelles recourent facilement les personnes les plus aisées, notamment celles qui sont en résidence secondaire. Mais le manque de main d'oeuvre ne permet pas aux entreprises de répondre à toutes les demandes.

	Relations Les différents acteurs publics, associatifs et privés gèrent la pénurie en coordonnant leurs actions pour
	se concentrer sur les situations les plus problématiques.
	La continuité des soins impose souvent d'enfreindre les règles. Ainsi, une entreprise qui s'occupe d'une
	personne âgée non-dépendante continue à le faire lorsque celle-ci devient titulaire de l'APA alors
	qu'elle n'est pas habilitée à s'adresser à ce public. Cette situation supposée durer le temps que se
	mette en place le plan APA, se prolonge alors que, dans le même temps, l'entreprise est sous la menace
	de sanctions de la part du département pour s'être écartée des normes.
	Cet exemple montre que le secteur privé ne se désengage pas de la partie plus "sociale" du travail car
	il est impliqué dans un tissu local de professionnels garant du respect de certaines valeurs auquelles le
	secteur privé reste arrimé (mauvaise image de ceux qui font du profit auprès de personnes fragiles,
	forte sensibilité aux inégalités sociales, culture politique de gauche dans le secteur).
	Si les personnes aisées en résidence secondaire rencontrent relativement peu de difficultés à trouver les
	services nécessaires à leur santé et à leur confort, certains membres des classes populaires renoncent
	désormais à séjourner sur le territoire. Ce premier signe de creusement des inégalités sociales n'est
	pas alarmant dans la mesure où le phénomène ne concerne que le loisir. Mais son amplification
	affecterait les soins auprès des personnes dépendantes les plus pauvres vivant sur le territoire à
	l'année si la logique économique crée une concurrence entre ces groupes sociaux.
	Les difficultés rencontrées par les services publics et associatifs favorisent le développement des

activités des entreprises privées, plus réactives et plus souples dans leur gestion. Même si la plupart d'entre elles témoignent de réelles préoccupations pour le bien-être de leurs clients, leur présence renforce une logique économique plutôt que sociale en consacrant une partie de leur activité aux demandeurs les plus solvables.

  Le phénomène est bien une histoire de femmes dans la mesure où ce sont des femmes qui sont les professionnelles qui interviennent (aides à domicile 98% mais aussi travailleuses sociales), mais pas seulement. Très présentes aussi en tant que mère s'occupant des enfants en vacances, ou comme aide familiale d'un parent dépendant. Il s'agit bien de women at work dans un travail qui est parfois rémunéré (travail productif), mais aussi un travail familial de mère (travail reproductif) ou de care auprès d'un parent. Les résultats présentés ici ont des limites, il reposent sur l'étude d'un territoire qui n'est pas choisi au hasard : le phénomène est y est très visible. Mais le vieillissement prévu de la population en France peut aboutir à la même situation ailleurs. Sur le territoire étudié, elle va sans doute se dégrader dans les années venir. En effet, selon les personnes interrogées, la saisonnalité est moins perceptible ces dernières années car le marché du travail est désormais tendu toute l'année. La situation est aujourd'hui celle d'une crise permanente, avec des pics aux périodes de vacances (été mais aussi fin d'année et Pâques). Dans ce contexte, la saisonnalité du travail est parfois la goutte qui fait déborder le vase. Néanmoins, les facteurs économiques pourraient jouer en faveur du développement local si l'on considère qu'une offre de services aux vacanciers est un atout pour développper le tourisme ou a minima pour ne pas les dissuader de fréquenter la région 14 . Faire face au contretemps pour faire son temps », Temporalités [En ligne], 25 | 2017, mis en ligne le 20 septembre 2017, consulté le 09 février 2020. URL : http://journals.openedition.org/temporalites/3685 ; DOI : https://doi.org/10.4000/temporalites.3685 Dussuet, A. (2013). Santé au travail et prévention des risques professionnels dans les associations d'aide à domicile. La Revue de l'Ires, 78, 77-97. https://doi.org/10.3917/rdli.078.0077 Berthier E. (2019), "Les résidences secondaires. Résultats de l'enquête 2019", Comité Régional du Tourisme de Bretagne, https://www.trebeurden.fr/actualite/resultats-de-lenquete-les-residencessecondaires/ , consulté le 31/03/2020.

	bibliographie
	Mauss
	Guibert et Slimani
	retraite société n°76
	Étienne Guillaud, «

This research is part of the Profam program (https://profam.hypotheses.org/) which, since 2018, is interested in the work of helping dependent people, work being considered in all its forms, whether family or professional. Within this framework, I have investigated several subjects in the territory to be discussed (and whose name will not be revealed). Seasonality data were collected throughout the survey period, but the summer of 2019 was devoted specifically to studying this issue. 1 Guibert Christophe, « Les effets de la saisonnalité touristique sur l'emploi des moniteurs de sports nautiques dans le département des Landes », Norois, 2012/2 (n° 223), p. 77-92. DOI : 10.4000/norois.4213. URL : https://www.cairn.info/revue-norois-2012-2-page-77.htm 2 Morice, A. et Michalon, B. (dir.) (2008), Travailleurs saisonniers dans l'agriculture européenne, Etudes Rurales, n°182.

Une semaine dans une entreprise privée et une semaine avec une aidom indépendante (Cesu).

https://www.observatoire-des-territoires.gouv.fr/indice-de-vieillissement

En 2018, 43% des demandes adressées aux CLIC proviennent des familles(rapport d'activité CLIC 2018).

d'après la cheffe d'un service d'action sociale, à objectiver par une téude des dossiers APA qui reste à venir.

Le rapport Durrieu sur le tourisme des seniors mentionne que "les craintes liées à la santé et au suivi des soins médicaux" sont un frein important au tourisme des personnes âgées (2016 : 13).

Guibert Christophe, « Les effets de la saisonnalité touristique sur l'emploi des moniteurs de sports nautiques dans le département des Landes », Norois, 2012/2 (n° 223), p. 77-92. DOI : 10.4000/norois.4213. URL : https://www.cairn.info/revue-norois-2012-2-page-77.htm

Morice, A. et Michalon, B. (dir.) (2008), Travailleurs saisonniers dans l'agriculture européenne, Etudes Rurales, n°182.

Une semaine dans une entreprise privée et une semaine avec une aidom indépendante (Cesu).

https://www.observatoire-des-territoires.gouv.fr/indice-de-vieillissement

En 2018, 43% des demandes adressées aux CLIC proviennent des familles(rapport d'activité CLIC 2018).

d'après la cheffe d'un service d'action sociale, à objectiver par une téude des dossiers APA qui reste à venir.

Le rapport Durrieu sur le tourisme des seniors mentionne que "les craintes liées à la santé et au suivi des soins médicaux" sont un frein important au tourisme des personnes âgées (2016 : 13).

Cette situation crée une tension très forte sur le marché de l'aide à domicile en zone côtière tout au long de l'année. Elle définit la toile de fond dans laquelle se pose la question des fluctuations saisonnières du travail.

Variations saisonnières

Plus de demandes : Les périodes de vacances scolaires sont des périodes critiques, particulièrement à partir du printemps quand arrivent les habitants des résidences secondaires (moyenne d'âge : 65 ans, dont 39% >70 ans) qui font appel aux services d'aide à domicile). Il s'agit en grande partie de personnes âgées qui viennent passer la saison d'été à la mer et demandent de l'aide, qu'elles soient dépendantes ou que ce soit pour des services de confort.

Un autre phénomène contribue à augmenter la demande de soins aux périodes de vacances. A l'occasion de leur séjour estival, les enfants des personnes âgées qui vivent sur le territoire viennent rendre visite à leurs parents et prennent parfois conscience que leur besoin d'aide a augmenté depuis leur dernière visite. En conséquence, certains d'entre eux introduisent une demande ou une réévaluation du plan d'aide 12 et sont en recherche immédiate de solutions en attendant que l'aide soit mise en place par les services sociaux. En conséquence, un pic dans les demande d'aide apparaît en juillet-août 13 alors que les social workers sont parfois en vacances.

Moins de mo disponible

Le secteur de l'aide à domicile n'est pas le seul qui soit en tension sur le bassin d'emplois étudié. L'hôtellerie-restauration et l'industrie offrent plus d'emploi qu'il n'y a de candidats pour les occuper (Pôle Emploi, 2019 : 11). L'aide à domicile est un secteur qui propose de moins bonnes conditions de travail par rapport à d'autres secteurs accessibles avec un faible niveau de diplôme. Cette concurrence est exacerbée aux périodes de vacances durant lesquelles l'emploi saisonnier touristique bat son plein. Par ailleurs, les rythmes familiaux affectent les équilibres habituels. Les aides à domicile qui sont mères de famille préfèrent parfois s'occuper de leurs enfants qui sont en vacances puisque le coût de la garde des enfants dans des centres de loisirs peut être dissuasif. Ce problème est amplifié pour les familles monoparentales. → L'effet de ciseau est donc renforcé à la belle saison, surtout durant les mois d'été

Conséquences (slide 5)

Cette situation à des conséquences à plusieurs niveaux (sur les services sociaux : demandes de révision des plans APA, renoncement au séjour de la part de pa, réduction des soins au minimum). Je vais en développer deux : la dégradation des consitions de travail et les relations entre le secteur marchand et non-marchand.

dégradation des conditions de w en été