Ameneh Deljoo
email: a.deljoo@uva.nl

Tom Van Engers
email: vanengers@uva.nl

Leon Gommans
email: leon.gommans@klm.com

Cees De Laat
email: delaat@uva.nl

The impact of competence and benevolence in a computational model of Trust

Keywords: Computational trust, Social trust, Benevolence, Competence, Agent based modeling

Trust is a fundamental element of any social network. However, despite numerous studies on trust, few have conducted studies across disciplines to provide a complete picture of the different dimensions of trustworthiness, such as integrity, competence and benevolence. In this paper, we focus on two of these dimensions, competence and benevolence. We propose techniques to evaluate the competence of the trustee in specific situations and infer the benevolence of the tustee towards the trustor when the trust evaluation is made. Moreover, we evaluate both competence and benevolence on the perceived trustworthiness of the trustee, taking into consideration the development of the relationship between the trustor and the trustee over time. We identified different stages in this relationship development and use them to evaluate trustworthiness of trustee in the absence of evidence that could be used to evaluate trustworthiness. Finally, we set an experimental scenario implemented as an agent-based model to evaluate our approach. The results obtained from these experiments show that the proposed techniques can improve the reliability of the estimation of the trustworthiness of the agents.

Introduction

Trust is considered a fundamental basis in social societies. The social networks, however, evolve over time therefore, we need a sophisticated method that enables an agent to select the trusted peer to interact with. Trust has both a backward looking as well as a forward looking dimension, i.e. one expects certain behavior in the future based upon pastime experiences. Since one can never be sure that the conditions in the past are exactly the same as they are now (e.g, intentions of agents may change over time one may have to trust someone that had no experience with etc.), the estimation of trustworthiness is predominant to assess trust between each peer in such network. An agent-based model (ABM) of such societies requires a computational model of trust. Trust is a concept that will always need contextualization. It is something completely different to trust that someone will pay his debts, not commit a crime or is able to solve a complex mathematical calculation. In our pursuit for a computational model of trust that can be used in ABMs that allow us to study (non-)compliance in societies we focus on the trustworthiness of an individual in performing a certain task in a given situation, taking into account the agent's competence in the matter, his overall integrity, and the stage of his relationships with the trustor. In order to better estimate the trustworthiness of trustees, it is important to consider these three dimensions individually, and to combine them in a dynamic way by taking into consideration the situation and the development of the relationship. However, the majority of the computational trust approaches presented in literature [START_REF] Sabater | Review on computational trust and reputation models[END_REF] estimates the trustworthiness of agents as one single factors rather than distinguishing these three trustees' factors. In this paper, we present a computational trust approach grounded on the trust model presented by Marsh [START_REF] Marsh | Formalising trust as a computational concept[END_REF]. We will show that the resulting computational trust model is able to capture the competence and benevolence of the trustee. To estimate the trustee's benevolence and competence, we use the evidence available to the trustee by considering different situations. Our approach combines the estimated competence of the trustee with the estimated benevolence into one single trustworthiness score. This score reflects the development of the relationship between trustor and trustee at the time of the assessment take into consideration. To estimate the competence of the trustee, we introduce three different stages of relationships between trustor and trustee. The results we obtain and present in this paper are very encouraging, as they show that there is a clear benefit in considering different stage of relationships between trustee and trustor in the described situations. The benevolence enhanced trust models allowed for a more accurate estimation of the trustees' trustworthiness than the original computational trust models [START_REF] Urbano | The impact of benevolence in computational trust[END_REF]. The remainder of the paper is organized as follows. Section 2 highlights the trust model and its antecedents. Then, Section 3 introduces our social computational model with its components. Section 4 deals with the simulation and results of the proposed solutions. Related work is covered by Section 5. Finally, Section 6 concludes the paper.

Trust

Trust is an essential part of social interaction. Trust is a broad concept studied in areas such as sociology and psychology [START_REF] Mayer | An integrative model of organizational trust[END_REF]. The concept of trust has received ample attention from various disciplines, and although prior research has put forth diverse interpretations of trust, a common core emerges. Trust antecedents framework, illustrated in Figure 1, was proposed by Mayer et al. [START_REF] Mayer | An integrative model of organizational trust[END_REF]. Following Mayer et al. [START_REF] Mayer | An integrative model of organizational trust[END_REF] we use the following trust definition: "Trust is the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control other party [START_REF] Mayer | An integrative model of organizational trust[END_REF]". Specifically, trust is a decision of the trustor regarding the perceived trustworthiness of a trustee. Four trust factors are generic and important during the trust evaluation, namely competence, benevolence and integrity of the trustee, and the trust propensity of the trustor. The decision to trust means that the trustor is willing to take any possible risk caused by the trustee or environment, no matter whether s/he has the competence to monitor or control the trustee or environment. Building on this definition, we define inter-organizational trust as the expectation held by one firm that another will not exploit its vulnerabilities when faced with the opportunity to do so [START_REF] Mayer | An integrative model of organizational trust[END_REF][START_REF] Barney | Trustworthiness as a source of competitive advantage[END_REF][START_REF] Krishnan | When does trust matter to alliance performance?[END_REF]. This expectation is confirmed when parties:

demonstrate competence relate to the potential ability of the evaluated entity to do a given task, act accordingly to fulfill the commitments even when acting on them is not in self-interest and accept the consequences, and do good and act out of kindness even if unforeseen contingencies arise.

Our definition thus bases inter-organizational trust on three related components: competence, integrity, and benevolence, which have been proposed by Mayer et al. [START_REF] Mayer | An integrative model of organizational trust[END_REF]. Computational trust is considered as an enabler of technology in virtual societies, and the estimation of trustworthiness is paramount to assess the trust that a trustor agent has on a given trustee. An individual is more or less trustworthy in performing a task in a given situation depending on his competence in the matter, his overall integrity, and the stage of his relationships with the trustor. Therefore, in order to better estimate the trustworthiness of agents, it is important to consider these three dimensions individually, and to combine them in a dynamic way, by taking into consideration the situation and the development of the relationship. However, the majority of the computational trust approaches presented in literature estimate the trustworthiness of agents as a block and do not distinguish between these trustees' attributions. Proposition 1 . Close and long-term relationships have a direct impact on the competence and benevolence of partners. In following, we present a computational trust approach grounded on multidisciplinary literature on trust that is able to capture the competence and benevolence of the agent under evaluation. This framework has been implemented with ABM.

Computational trust model

In this section, we introduce the social computational trust model. Our aim is to define a mechanism for estimating trustworthiness of a trustee that can be used by the trustor to evaluate trust and make decisions about the future relationship with the trustee. Extracting trustworthiness of trustee based on Mayer et al.'s model [START_REF] Mayer | An integrative model of organizational trust[END_REF] has been only implemented by few scholars such as [START_REF] Urbano | The impact of benevolence in computational trust[END_REF][START_REF] Guo | Etaf: An extended trust antecedents framework for trust prediction[END_REF]. Most of these computational trust approaches estimate the trustees' trustworthiness using individual items of evidence about these trustees' behavior in the past interactions, either with the trustor or with third party agents [START_REF] Sabater | Regret: reputation in gregarious societies[END_REF][START_REF] Abdul-Rahman | Supporting trust in virtual communities[END_REF][START_REF] Mui | A computational model of trust and reputation[END_REF]. However, none of these approaches is able to estimate the benevolence of the trustee. We claim that understanding the benevolence and competence of the trustee towards the trustor at the moment of the trust decision is fundamental for being able to accurately estimate the latter's trustworthiness. With this in mind, we present the main hypothesis of this work as follows; Hypothesis 1. The extraction of benevolence-competence based information from the set of evidence on the trustee under evaluation and its use in adequate stages of the relationship between truster and trustee shows that trustee's trustworthiness improves by increasing the number of interactions between trustor and trustee.

Basic Notation

Our generic computational trust model is applied to environments where trustor agents select the best trustees to interact with, having the posterior establishment of dyadic agreements between partners. We define the society of agents by A, where x and y represent as trustor and trustee respectively and x, y ∈ A. We represent T (x,y) as the amount of trust x has upon y based on the realization of a given task t ∈ T with respect to the situation s i , where T = {t 1 , t 2 , ..., t m } is the set of all possible m tasks in the society and s i = {s 1 , s 2 , ..., s i } is the set of all possible situations in the society. These set of tasks bring duties for the trustee that need to be fulfilled. As we mentioned before, context plays an important role in our model. In order to characterize and describe the situation leading to an agreement, we consider the definition of context as including four main types of context: identity, time, location, and activity [START_REF] Abowd | Towards a better understanding of context and context-awareness[END_REF]. Finally, the set of all the existed evidence on given trustee is represented by

E * ,y = {e i ∈ ε : v ei 2 = y},
where ε represents all evidence available on trustee from the all the trustee's direct neighbor. Following, E (x,y) shows all the evidence about the direct interactions between trustor and trustee

E (x,y) = {e i ∈ ε : v ei 1 = x, v ei 2 = y}.

Our social computational trust Model

The benevolence-competence based computational model of trust that we present in this paper is a part of a larger framework of social trust based on Mayer's trust model. It integrates three distinct functions: the competence evaluation function (Com (x,y) :

s i × Ex ( * ,y) ∈ [0, 1]
), the benevolence evaluation function (Ben (x,y) : E (x,y) ∈ [0, 1]), and the trustworthiness evaluation function (T W (x,y) ∈ [0, 1]) in the set of situations s i . We describe each of these functions in the following subsections. We illustrated the computational model in Figure 2.

Benevolence function Benevolence is considered as a key element of trust and an antecedent of trustworthiness by several scholars (e.g. [START_REF] Levin | Trust and knowledge sharing: A critical combination[END_REF][START_REF] Koscik | The human amygdala is necessary for developing and expressing normal interpersonal trust[END_REF]). The estimated value of the benevolence of trustor x toward trustee y, Ben (x,y) , is derived from the direct interactions (i.e. E (x,y) ) between trustee and trustor in the situations s i . The output of the benevolence evaluation function Ben (x,y) , defined in [0, 1], is

Ben (x,y) = 1 |S| E (x,y) (val(E (x,y) )). ( 1
)
Where S is the set of situations, in which x has interactions with y.

Benevolence Evaluation Function

Ben (x,y)

Trustworthiness Evaluation Function

TW (x,y)

Trust T (x,y) E (x,y Competence function The competence evaluation function Com (x,y) estimates the general ability of the trustee under evaluation in performing a given task t in a specific situation s. This function takes all the evidence available on the trustee under evaluation Ex ( * ,y) as input. The output of the competence evaluation function is the estimated competence of the agent, Com (x,y) , defined in [0, 1]. Competence, as risk, involves an agent making a judgment about the trustee's ability to perform the given task. We consider three different possible situations to evaluate trustee's ability.

1. There is no evidence available from the trustee. To judge the trustee's competence, the trustor will calculate the risk of trusting a stranger and decide based on the risk as

Risk = Cost × (1 -P r) benef it × P r , (2)
where P r is the probability of performing the task by the given trustee. 2. Situation β: there are some evidence but not for the considered context. In this situation, the trustor collects all the evidence from other agents and evaluates the competence of trustee based on them as

Com = 1 |N | β∈N (val(E * ,y ) × T x (y, β)), (3)
where T x (y, β) denotes the basic trust that x has on y and β is the set of all situations in which x has interactions with y. The basic trust y calculated as 1 / |N | β∈N T (x,y) . N denotes as the set of situations similar to the present situation (s i ) in which x has interactions with y.

3. Situation α: there is related evidence about the agent in this context or similar situation.

Com = 1 |N | α∈N (val(E * ,y )), ( 4
)
where α is the set of all situations in which x has interactions with y. These three situations (no evidence available, β and α) are assumed to help an individual to make the decision. In this paper, we assume that trustor and trustee collaborate in the similar context (i.e. situation α).

Function T W (x,y) The trustworthiness evaluation function T W (x,y) takes into consideration the perception of the competence and benevolence of the trustee (Algorithm 1). We assume the same weight to the competence and benevolence dimension, this weight when both trustor and trustee are practically strangers (W (x,y) = 0), and progressively increasing the weight of these to dimension as the partners get to know each other better, this weight becomes one when partners are considered to be closed (Proposition 2). In the algorithm 1 N (x,y)

Algorithm 1 Calculate T W (x,y)

Require: E ( * ,y) : the set of all evidence about trustee y. Require: N ben = 2: minimum interaction between trustee and trustor.

Ensure:

E (x,y) / ∈ E ( * ,y) 1: N (x,y) ← |E (x,y) | 2: Ben (x,y) ← F (Ben (x,y) ) 3: Com (x,y) ← F (Com (x,y) ) 4: T W (x,y) = Com (x,y) + Ben (x,y) 5: return T W (x,y)
represnets the number of interactions between x and y, (line 1), and defined a minimum number of interactions (N ben = 2) between trustor x and trustee y. N (x,y) used to combin the estimated value of the trustees benevolence as returned by F (Ben (x,y) ) (line 2) with the estimated value of its competence as returned by F (Com (x,y) ) (line 3). Finally, the estimated value of the trustees trustworthiness T W (x,y) is computed as a sum of Com (x,y) and Ben (x,y) (line 4).

Simulation setup

In this set of experiments, we want to test Hypothesis 1, which we reformulated as follows: The extraction of benevolence-competence based on the information from the set of evidence on the trustee under evaluation and its use in adequate stages of the relationship between trustor and trustee shows that trustee's trustworthiness improves by increasing the number of interactions between trustor and trustee. The experiments were conducted in Jadex [START_REF] Braubach | Jadex: Implementing a bdiinfrastructure for jade agents[END_REF] environment. We set up a social network, shown in Figure 3 to represent our network. We setup a collaborative network of organizations. This social network represents a collaborative network of organizations like the ones we study in our SARNET research project where service providers collaborate and act on behalf of partners, acts that may harm the individual interests, all in order to protect the collaborative network against cyber attacks. Each node represents an autonomous organization that needs to trust other parties and share sensitive information with them. For the simplicity, we assumed that there is only one task being negotiated by all nodes which mitigates an attack and shares the attack information with other parties 5 . We define four different situations: s 1 provides a specific number of samples within 24 hours, s 2 provides a specific type of resource (e.g. allocating resources), s 3 blocks a link, and s 4 monitors a specific traffic. This model starts after the establishment of an agreement between the trustor and the selected trustee, thus excluding the selection process itself. It focuses on both types of agents' decision concerning the fulfillment of the established agreement: the trustees may opt to fulfill the agreement (the trustors will report the outcome F DD) or to delay its realization. Accordingly, the trustors may respond to a delay by either retaliating, denouncing the breach (reporting outcome V ) or forgiving the contingency (reporting outcome F D).

Result

Our result consists of two parts. First, we calculate benevolence of agent y by considering all the evidence (i.e direct interactions) that x has on y. Second, evaluate the competence of agent given trustees a, z and y in four mentioned situations from the trustor's x view. Hence, we perform four different types of situations simultaneously, each with six agents. We assume that agents are honest and there is no conflict on the evidence and message are encrypted (the interminable agents cannot manipulate the message). In order to compare all approaches, we measure and average the number of agreements with outcomes F D, F DD and V . We are able to calculate the benevolence and competence of each trustor by equation 1 and 4. To calculate the Ben (x,y) , we extract all the evidence that x has on y. We perform the simulation for 20, 50 and 100 rounds of interactions. We also consider that each agent can freely fulfill duty, fulfill duty with delay and violate the agreement. We have summarized the result in Table 1 (including mean M and standard deviation SD). To evaluate the competence function, we select three agents a, z and y from the set of agents and calculate the competence of these three agents from trustor' x view. Agent x will collect (collecting the evidence is done by sending a query to each agent and asking its opinion) all the available evidence from the neighbour's of a, z and y which are reported in Table 2. The simulation has been perform for four different situations and three different rounds. As result, we compared mean (M) and standard deviation(SD) and competence of each trustee for different rounds and we detailed them in Table 3 and Table 1. In Table 3 and Table 1, the highest values are in bold and the lowest values are in italics. We verified that in Table 1 the benevolence of trustees are increased as the number of interaction increases. For instance, with only 20 rounds, when the number of interactions between any two partners is not large the benevolence is small. By increasing the number of rounds, the benevolence increases significantly. Indeed, this confirms that the number of interactions is, in fact, impact the benevolence existing between any pair of trustor-trustee (We have proved the main hypothesis of this work3). In the case of competence (see Table 3), we also observed the same behavior from the simulation. The competence of agent is influence directly by the number of interactions. As we mentioned before, the simulation has been repeated for 50 and 100 rounds. The benevolence value reaches the maximum value of one by increasing the number of interactions between partners and the partners that are considered to be in a close relationship (Proposition 2). Indeed, each trustor can conclude the trustworthiness of trustee in each round and make a decision. Overall, we are able to confirm the truthfulness of Hypothesis 1.

Related work

Many computational trust models have been presented by different scholars, nevertheless, only a few models are actually social computational models. Adalie et al. [START_REF] Adali | A unified framework for trust in composite networks[END_REF] presented a conceptual model of social trust based on Kelton et al.'s model [START_REF] Kelton | Trust in digital information[END_REF]. Kelton et al.'s consider ability, positive intentions, ethics, and predictability as the trustworthiness components. Adalie et al. used a probabilistic approach to implement the model, but by recognizing the limits of such approach in the treatment of the social concepts, their model was not implemented. Among all the presented computational trust models [START_REF] Pinyol | Computational trust and reputation models for open multi-agent systems: a review[END_REF], the only computational approach that includes a comprehensive set of features grounded on the theory of trust is the socio-cognitive model of trust by Castelfranchi and Falcone [START_REF] Castelfranchi | Trust theory: A socio-cognitive and computational model[END_REF]. This model considers that the trustor has a goal that can be achieved by the action of the trustee. In their view, trust is formed by considering the different beliefs that the trustor has about the trustee, either internal (beliefs on competence, disposition, and harmfulness) or external (opportunities and dangers). The values of these beliefs are further modulated by meta-beliefs about the relative strength of each belief. The richness of this model makes it hard to implement in practice. In fact, the current implementation of the model (e.g., [START_REF] Castelfranchi | Trust theory: A socio-cognitive and computational model[END_REF]) requires extensive manual configuration by domain experts for each trustee and task under assessment and oversimplifies the theoretical model. Moreover, it requires explicit information about the competence and disposition (or similar beliefs) of the agent under evaluation, which may be hard to get in dynamic agent-based environments. Another social trust model was presented by Urbano et al. [START_REF] Urbano | The impact of benevolence in computational trust[END_REF] called situationaware and social computational trust Model (SOLUM). Their computational model consists of two parts: the first part is a general framework of computational trust, which is based on two fundamental characteristics of trust, the trustor's disposition and emotional state. They adapted also Mayer's trustworthiness dimension that includes the ability, integrity, and benevolence to determine trust. For the second part, they proposed a set of distinct techniques to extract information about the individual dimensions of the agent's trustworthiness from the set of structured evidence available to the agent. The main difference between our model and Urbano's model is that we consider different stages of relationships for the competence function. We slightly adapted and modified the Marsh [START_REF] Marsh | Formalising trust as a computational concept[END_REF] competence formulization by considering three different situations for trustor to make a decision about the (future) collaboration with the trustee. Finally, Herzig et al. [START_REF] Herzig | A logic of trust and reputation[END_REF] formalized the model of Castefranchi and Falcone, in multi-modal logic, adding the notions of occurrening trust and dispositional trust (i.e., trust in a general disposition of the trustee to perform a similar task some point in the future). Skopik et al. [START_REF] Skopik | Modeling and mining of dynamic trust in complex service-oriented systems[END_REF] purposed a semi-distributed information sharing platform where different organizations can share the incidents information with their trusted peers. Skopik et al., proposed a fuzzy method to evaluate trust among members. The major aim of social trust in their model is to personalize online interactions and prioritize collaboration with trustworthy individuals. The author claimed that trusted relations can be defined manually by users, e.g., by declaring "friend-relations" or can be determined automatically through mining of interactions. However, their social model is based on personal experience of each member and suffers from the scalability issue.

Conclusion and Discussion

Computational trust is crucial for decision making regarding possible agents' future joint activities such as the alliances. It heavily relies on the estimation of trustworthiness to assess the trust on particular trustees. To better estimate this trustworthiness, it is important to estimate, besides other relevant features, their competence and benevolence separately, and to combine them taking into consideration the particular situation and relationship. In this paper, we described a part of our trust computational model. We evaluated our approach in a simulated experimental environment. We have proved that the trustworthiness estimation grew with the increasing number of interactions between any trustor-trustee pair. Besides, we went beyond the traditional evaluation of computational trust models (such as Fuzzy logic evaluation) and used a model of agents' behavior where both trustors and trustees evolve their behaviors based on different stages of existing relationships (i.e. presented in the competence formula) between the agents. Concerning future work, we intend to further identify the particular circumstances in which the use of this sophisticated trust model is more relevant. Also, we intend to explore integrity as another dimension of trustworthiness, as well as exploring other ways of combining the trustworthiness dimensions, and to use other antecedents of trust, such as the trustors' own propensity to trust. A lot of future work remains to be done to ensure that the framework is functional in practice. We plan to conduct experiments to evaluate the robustness of the proposed trust mechanism against the badmouthing attacks and non-compliant members. Furthermore, evidential reasoning in the case of conflicting on evidence and evaluate trust based on this method will need to be elaborated and evaluated. Another interesting direction for future work is to develop guidelines in consultation with policy makers to define sounds policy and standard for determining the contract importance based on the risk associated with the proposed methods and the members' preferences. Many different adaptations, tests, and experiments for our model have been left for the future due to lack of time. Future work concerns the a deeper analysis of our proposed model and validates the model with different computational models that proposed by other scholars such as Urbano et al..

Fig. 1 .

 1 Fig. 1. Trust framework and its antecedents.

  considered three different outcomes (d 8 ) is defined by O = {F D, F DD, V }, where F D (fulfill duty) means the trustor considers that the trustee performed whatever matter he had to perform on time, F DD (fulfill duty with delay) means the trustee was presented with an unexpected delay in the performance of the task (or duty), and V (violation) means the trustee did not perform the given task. Considering O = {F D, F DD, V }, possible values for this function are val(F DD) = 1.0, val(F D) = 0.5, and val(V ) = 0.5.

Fig. 2 .

 2 Fig. 2. Our benevolence-competence computational model of trust.

Fig. 3 .

 3 Fig. 3. Social network schema.

  Urbano et al. [3] identified eight dimensions of context {d 1 , d 2 , ..., d 8 } 4 , where dimensions d 1 and d 2 represent the agents trustor and the trustee, respectively; d 3 and d 4 represent the time and the location of agreement; and d 5 , d 6 , d 7 and d 8 identifyand characterize the type of the task, its complexity, deadline, and outcome of its realization, respectively. We adapt the Urbano et al.[START_REF] Urbano | The impact of benevolence in computational trust[END_REF] context definition in this paper. For the simplicity, we assume that all agreements performed in the society of agents refer to the same type of task t (d 5 ), although tasks have different degrees of complexity (d 6 ) and deadlines (d 7 ). In this paper, we only consider four dimensions of context, which are d 1 , d 2 , d 5 and d 8 . We exclude time, complexity and deadlines from our formalization. They will be addressed in a future publication. Urbano et al.

Table 1 .

 1 Ben (x,y) evaluation for the number of rounds

	No. of rounds →	20	50	100
	Ben (x,y)	0.22	0.31	0.86
	SD	0.113	0.105	0.081
	M	0.762	0.777	0.810

Table 2 .

 2 Agent x asks different Agents' opinion about agent z in the four situations

	Situation →	s1	s2	s3	s4
	Y	FDD,FD	FD,FD	FDD,FD	FDD,FDD
	M	FDD,FD	FD,FD	FDD,FD	FDD,FDD
	W	FDD,FD	FD,FD	FDD,FD	FDD,FDD
	D	FDD,FD	FD,FD	FDD,FD	FDD,FDD

Table 3 .

 3 Competence evaluation for agents a, z and y and the number of rounds

	No. of rounds →	20	50	100
	Com (x,a)	0.21	0.40	0.65
	Com (x,z)	0.28	0.43	0.88
	Com (x,y)	0.18	0.33	0.54
	SDa	0.059	0.051	0.048
	SDz	0.068	0.058	0.042
	SDy	0.081	0.066	0.041
	Ma	0.927	0.938	0.941
	Mz	0.910	0.927	0.947
	My	0.917	0.936	0.962

The technical details and code of this research can be found in http://delaat.net/sarnet/index.html

Acknowledgment

This work is funded by the Dutch Science Foundation project SARNET (grant no: CYBSEC.14.003/618.001.016) and the Dutch project COMMIT (WP20.11). Special thanks go to our research partner KLM. The authors would also like to thank anonymous reviewers for their comments.


Neamah Al-Naffakh
email: neamah.al-naffakh@plymouth.ac.uk.

Nathan Clarke
email: nclarke@plymouth.ac.uk.

Fudong Li
email: fudongli@plymouth.ac.uk.

Continuous User Authentication using Smartwatch Motion Sensor Data

Keywords: biometrics, mobile authentication, gait biometrics, accelerometer, smartwatch authentication, activity recognition, Neural Network, user authentication. 1

Smartwatches, which contain an accelerometer and gyroscope, have recently been used to implement gait/activity-based biometrics. However, many research questions have not been addressed in the prior work such as the training and test data was collected in the same day from a limited dataset, using unrealistic activities (e.g., punch) and/or the authors did not carry out any particular study to identify the most discriminative features. This paper aims to highlight the impact of these factors on the biometric performance. The acceleration and gyroscope data of the gait and game activity was captured from 60 users over multiple days, which resulted in a totally of 24 hours of the user's movement. Segment-based approach was used to divide the time-series acceleration and gyroscope data. When the cross-day evaluation was applied, the best obtained EER was 0.69%, and 4.54% for the walking and game activities respectively. The EERs were significantly reduced into 0.05% and 2.35% for the above activities by introducing the majority voting schema. These results were obtained by utilizing a novel feature selection process in which the system minimizing the number of features and maximizing the discriminative information. The results have shown that smartwatch-based activity recognition has significant potential to recognize individuals in a continuous and user friendly approach.

Introduction

Activity recognition studies that used the acceleration (Acc) and gyroscope (Gyr) data to identify the user's identity based on their physical activities (e.g., normal walking and typing) attracted a lot of research. However, a large amount of the prior art captured the user's movement data by using costly specialized devices (i.e., attaching a wearable sensor to different positions around the human body such as hip, waist, and lower leg) [START_REF] Gafurov | Biometric gait authentication using accelerometer sensor[END_REF][START_REF] Gafurov | Spoof Attacks on Gait Authentication System[END_REF][START_REF] Nowlan | Human Identification via Gait Recognition Using Accelerometer Gyro Forces Michael Fitzgerald Nowlan CPSC-536-Networked Embedded Systems and Sensor Networks Professor Savvides Fall 2009 2[END_REF]. Furthermore, these devices require a comprehensive set-up that reduce the usefulness of their performance and increases the cost of implementation into a potential real-world system. Although the applications of activity recognition are greatly expanded by utilizing the potential of smartphone sensors (i.e., Acc and Gyr), it is widely understood that smartphones suffer from several issues to produce a consistent and reliable biometric signal in real life. For example, the problem of orientations (i.e., screen rotations) and off-body carry (e.g., when the device is carried in a handbag), making the collected data less accurate or unusable. These limitations are addressed by smartwatches [START_REF] Johnston | Smartwatch-Based Biometric Gait Recognition[END_REF][START_REF] Yang | MotionAuth: Motion-based authentication for wrist worn smart devices[END_REF][START_REF] Ramos | Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches : an Experimental Evaluation[END_REF][START_REF] Davidson | Smartwatch User Identification as a Means of Authentication[END_REF][START_REF] Kamoi | Biometrie authentication using hand movement information from wrist-worn PPG sensors[END_REF][START_REF] Weiss | Smartwatchbased activity recognition: A machine learning approach[END_REF][START_REF] Kumar | Authenticating users through their arm movement patterns[END_REF][START_REF] Babins | Walk-Unlock: Zero-Interaction Authentication Protected with Multi-Modal Gait Biometrics[END_REF][START_REF] Cola | Gait-based authentication using a wristworn device[END_REF][START_REF] Dong | User Authentication Using Motion Sensor Data from Both Wearables and Smartphones[END_REF][START_REF] Lewis | Real Time Motion-Based Authentication for Smartwatch[END_REF][START_REF] Wang | Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics[END_REF][START_REF] Griswold-Steiner | HandwritingWatcher: A Mechanism for Smartwatch-Driven Handwriting Authentication[END_REF][START_REF] Findling | ShakeUnlock: Securely Transfer Authentication States Between Mobile Devices[END_REF][START_REF] Cheng Lianga | User-Authentication on Wearable Devices Based on Punch Gesture Biometrics[END_REF][START_REF] Zeng | WearIA: Wearable Device Implicit Authentication based on Activity Information[END_REF][START_REF] Xu | Gait-watch: A context-aware authentication system for smart watch based on gait recognition[END_REF], which contain the requisite sensors such as Acc Gyr, due to their fixed contact with individuals (i.e., either on left or right wrist). As a result, these devices have the ability to capture more accurate personal data than smartphones do. Traditional user authentication approaches on smartphones and smartwatches such as password and PIN-based authentication are considered significantly intrusive which impact their usability and subsequently security [START_REF] Ai-Naffakh | Activity Recognition using Wearable Computing[END_REF][START_REF] Al-Naffakh | A Comprehensive Evaluation of Feature Selection for Gait Recognition Using Smartwatches[END_REF][START_REF] Al-Naffakh | Unobtrusive Gait Recognition Using Smartwatches[END_REF]. For example, Microsoft conducted a comprehensive study and showed that 72% of participants disabled their login credentials (i.e., PIN code) because of its intrusive implementation [START_REF] Hamblen | Mobile phone security no-brainer: Use a device passcode[END_REF]. Moreover, PIN-based authentication technique is susceptible to several types of attacks such as brute force and shoulder surfing [START_REF] Kim | Keypad against brute force attacks on smartphones[END_REF]. Given that smartwatches are usually connected with a smartphones via Bluetooth, implicit and continuous authentication to secure information on both devices from unauthorized access is essential. Activity recognition using smartwatches offers several advantages over traditional authentication techniques. For instance, it is reliable (i.e., nearly impossible to imitate), convenient for a user (i.e., does not require explicit user interaction with a sensor during authentication), and provides transparent and continues user authentication as long the user's hand moves [START_REF] Al-Naffakh | Unobtrusive Gait Recognition Using Smartwatches[END_REF]. To this end, this paper explores the use of smartwatches for transparent authentication based upon gait and game activities. The main contributions of this study are demonstrated as follows:

• To the best of the author's knowledge, this is the biggest dataset for smartwatchbased gait authentication, which contains gait data of 60 users over multiple days • The novel feature selection method utilised a dynamic feature vector for each user and successfully reduced the feature vector size with better performance. • Identifying the optimal source sensor for the authentication task.

• Highlighting the impact of Majority schema on the system accuracy.

• vastly superior results were achieved that outperform the prior accelerometerbased studies. The rest of the paper is organized as follows: Section 2 reviews the state of the art in transparent and continuous authentication that specifically uses accelerometer and gyroscope sensors. Data collection, feature extraction, the experimental procedure, and results are outlined in Sections 3 and 4. Sections 5 presents the conclusions and future research directions.

Related Work

Behavioural biometrics systems aim to authenticate individuals transparently based upon their activity (e.g., gait, keystroke, and handwriting). Apart from the traditional authentication approaches (i.e., PIN and passwords), a significant amount of studies have recently explored the use of built in smartphones sensors in order to improve the level of security as well as offer continuous and unobtrusive authentication. For example, Zhen et al. [START_REF] Zheng | You Are How You Touch: User Verification on Smartphones via Tapping Behaviour[END_REF] proposed to verify users based upon their keystroke while other studies involved gait [START_REF] Muaaz | Influence of different walking speeds and surfaces on accelerometer-based biometric gait recognition[END_REF][START_REF] Nickel | Scenario test of accelerometer-based biometric gait recognition[END_REF][START_REF] Nickel | Classifying accelerometer data via Hidden Markov Models to authenticate people by the way they walk[END_REF][START_REF] Nickel | Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm[END_REF][START_REF] Muaaz | Accelerometer based Gait Recognition using Adapted Gaussian Mixture Models[END_REF], activity [START_REF] Kumar | Continuous authentication of smartphone users by fusing typing, swiping, and phone movement patterns[END_REF][START_REF] Gascon | Continuous authentication on mobile devices by analysis of typing motion behavior[END_REF][START_REF] Kumar | Continuous User Authentication via Unlabeled Phone Movement Patterns[END_REF][START_REF] Heng | Human activity recognition based on transformed accelerometer data from a mobile phone[END_REF], typing [START_REF] Wang | Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics[END_REF][START_REF] Griswold-Steiner | HandwritingWatcher: A Mechanism for Smartwatch-Driven Handwriting Authentication[END_REF], and arm movement [START_REF] Okumura | A Study on Biometric Authentication based on Arm Sweep Action with Acceleration Sensor[END_REF][START_REF] Yang | MotionAuth: Motion-based authentication for wrist worn smart devices[END_REF][START_REF] Davidson | Smartwatch User Identification as a Means of Authentication[END_REF]. The use of sensor data, specifically the Acc and Gyr data, attracting an enormous amount of attention. Whilst previous research in activity recognition has focused on body worn sensors or using the smartphone's sensors (i.e. Acc and Gyr), little attention is given to the use of smartwatcheswhich tend to be sensor-rich highly personal technologies. Moreover, given that smartwatches are usually worn in a fixed position (i.e. right or left wrist), they offer the opportunity to collect the user's motion data in a more effective and reliable fashion than smartphones could. A comprehensive analysis of the prior studies on activity and gait recognition using smartphones and smartwatches sensors is summarized in Table I. Although the presented studies in Table 1 provide important insight in the domain of sensor-based activity recognition, they suffer from several issues such as data collection methodology. In most evaluations a relatively small data set was used and frequently obtained on the same day (SD), which is not a realistic evaluation as such data does not show the variability of the human behaviour over the time and might be overlap across a large population. Most research claim a system resilient to the crossday (CD) problem either trains on data from trials that are also used to test (thus not making it a true cross-day system) or has a high error rate, preventing the system being used practically. The lack of realistic data underpins a significant barrier in applying activity recognition in practice. Therefore, this study presents a realistic scenario (in terms of the data collection) by training and testing the user's movement data over multiple days. Moreover, the most effective device (i.e., smartwatches) is used to collect the user's motion signal, and hence design an effective transparent and continuous user authentication system for both smartphones/smartwatches. With respect to features, cycle and segment-based approaches are used in order to pre-process the raw Acc and Gyr data and then extracting several statistical and cepstral coefficient features from the segmented data (e.g., standard deviation, Variance, and Mel Frequency Cepstral Coefficients). While the cycle-based approach offers a precise manner of generating samples from the testing data by detecting steps and splitting the data accordingly, the recent studies showed that the fundamental performance of using cycle extraction method was very low (At best an EER of 14.4%). The high error rate of using this method was highlighted by several studies [START_REF] Ai-Naffakh | Activity Recognition using Wearable Computing[END_REF][START_REF] Al-Naffakh | A Comprehensive Evaluation of Feature Selection for Gait Recognition Using Smartwatches[END_REF][START_REF] Al-Naffakh | Unobtrusive Gait Recognition Using Smartwatches[END_REF][START_REF] Nickel | Classifying accelerometer data via Hidden Markov Models to authenticate people by the way they walk[END_REF] such as smartphones not being securely fastened to the user, cheap sensors, cycles are not guaranteed to be the same length, and rounding errors. In contrast, promising results were reported (i.e., EERs ranging from 1.4% to 8.24%) by applying the segment-based approach to the raw data [START_REF] Johnston | Smartwatch-Based Biometric Gait Recognition[END_REF][START_REF] Nickel | Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm[END_REF]. To predict the user's identity, several studies utilized the standard classification methods (e.g. Euc, and DTW metrics) to create a single reference template and is later tested based upon the similarity between the template and the test data. While this approach works well for certain biometric domains (e.g., fingerprint or facial recognition), it does not seem to be the most effective type of system for activity recognition or other behavioural biometric techniques. This is because the user's behaviour can change over time and be affected by other factors (e.g., mood and health). Therefore, this paper applied more complex algorithm (i.e., Neural Networks) to train and test the user's reference template.

Experimental Methodology

In order to overcome some of the shortcomings of prior work, this paper will explore the following research questions: 1-How does the accuracy vary across same and cross-day evaluation methodologies? 2-Which sensor can provide a more consistent and reliable motion data for recognizing individuals? 3-What relationship do features have upon performance? 4-What is the impact of applying the Majority voting schema on the system performance?

To address these questions, the following experiments were conducted: • Same & Cross day evaluation, accelerometer Vs gyroscope sensor (research questions 1 and 2) • Dynamic feature selection (research question 3) • Majority Voting Schema (research question 4)

Data Collection

This section describes the procedure for collecting the data and transforming it into a form suitable for traditional machine learning classification algorithms. As mentioned earlier in Section 2, the data collection methodology is definitely an issue for most gait and activity recognition related studies (e.g., the user's motion data was collected by placing a smartphone in a fixed position, using small data set that frequently captured on the same day). Therefore, it is important to select the most appropriate technology to capture the movement data and ensure the population sample being used as large and significantly reliable as possible. To achieve that, this study utilized the Microsoft Band to collect 24 hours of the movement data from 60 users; to the best of the author's knowledge, this is the largest dataset within this domain. During the data collection, the Acc and Gyr signal were sampled at 32 Hz. As soon as the data was collected by the smartwatch, it was sent to a smartphone residing in the user's pocket via Bluetooth. For all 60 users, each was asked to follow a predefined scenario. Aiming to study the practicality of such a system, the scenario included two simple and realistic activities that reflect the user's daily activities (i.e., normal walk, and playing Game). Each user completed six sessions for each activity over multiple days (a single session contained two minutes of the user's motion data); each of the three sessions were provided on different days within a time frame of 3 weeks. For the gait activity, users were required to walk on a predefined route on flat ground and encouraged to walk in their own natural and comfortable manner. For a more realistic scenario, the user had to stop in order to open a door, and take multiple turns. Moreover, no other variables, such as type of footwear or clothing, are controlled. In the game activity, users were asked to sit and playing a simple game on the touch screen of their smartphone. Once the data collection was completed, the signal processing phase was undertaken-a brief description of the steps are • Time interpolation: Due to the limited accuracy of the sensors in the Microsoft Band, the smartwatch was not able to record data at a fixed sample rate. Therefore, time interpolation was required to make sure that the time period between two successive data points was always equal. • Filtering: a low pass filter was applied in order to enhance the accuracy of the signal. This was carried out with several settings (i.e., 10, 20, and 30) and through experimentation the cut-off frequency of 20Hz achieved the best accuracy (examples of the filtering are shown in Figure 1).

• Segmentation: the tri-axial raw format for both Acc and Gyr signals were segmented into 10-second segments by using a sliding window approach with no overlapping. Therefore, in total 72 samples for each activity and each user over multiple days. 2. Legend: NF stands for the number of generated features.

The feature selection step has become the focus of many research studies in the area of authentication in order to reduce the potentially large dimensionality of input data, with the resultant effect of enhancing performance and reducing the computational complexity of the classifier. Subsequently making it easier to manipulate and calculate feature vectors on processing and battery limited digital devices. This study utilised a dynamic feature vector that contains distinctive features for each user. For example, the reference template of user 1 could be created by using features 1, 2, 3, and 7 while features 3, 4, 5, and 7 might be used to form the reference template of user 2. This is achieved by calculating the mean and standard deviation (STD) for each feature individually for all users and then compares the authorized user's results against impostors to select the feature set with the minimal overlap. In other words, for each feature, a score is calculated based upon the following condition:

• If the mean of imposter's activity is not within the range of the mean +/-STD of genuine, add 1 to the total score. • Dynamically select the features according to their score order from high to low.

The highest means less overlap between imposters and the genuine user as shown in Figure 2.

Experimental Procedure

Biometric authentication or verification is a binary classification problem, where the aim is to determine if a system can identify a user correctly (a "genuine" user) or as an imposter. The reference and testing templates were created under two different scenarios (i.e., SD, and CD). In the SD scenario, the data set was divided into two parts: 60% was used to train the classifier while the remaining 40% was utilised to evaluate the performance. To test the system under the CD scenario, the data from the first day was used for training and the second day data was employed for testing. A Feedforward Multi-layer Perceptron (FF MLP) neural network was used as the default classifier for the walking and game activities due to its reliable performance [START_REF] Al-Naffakh | Unobtrusive Gait Recognition Using Smartwatches[END_REF][START_REF] Kwapisz | Activity recognition using cell phone accelerometers[END_REF].

For each experiment, four different FF MLP neural network training size were evaluated (i.e., 10, 15, 20, and 25) with an average of repeating each of them 10 times. All the presented results in this study were based on using FF MLP neural network of size 10 as it showed the lower EER.

Results

After research questions of the prior art were identified and presented in the previous section, details of the results for the two evaluation scenarios (single day and multiday), the two different smartwatch sensors (i.e., Acc and Gyr), are presented in the following subsections. The results are first presented for "single-sample mode" and then using the majority voting scheme.

Single Day VS Cross Day Evaluation

A well-known serious criticism of the prior gait/activity-based biometrics is the data collection strategy, which the training and test data captured during a single sessionon a single day. This contradicts the notion that the only more reliable test comes from multi-day testing. This maxim holds because performance on single day datasets does little to test how resistant the system is to the variability of human gait over the time. In cases when the CD scenario is considered, the evaluation of most studies is often either done improperly (e.g., mixes the training and test data from multiple days [START_REF] Nickel | Classifying accelerometer data via Hidden Markov Models to authenticate people by the way they walk[END_REF][START_REF] Nickel | Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm[END_REF]) or the results are very poor [START_REF] Muaaz | Influence of different walking speeds and surfaces on accelerometer-based biometric gait recognition[END_REF][START_REF] Nickel | Scenario test of accelerometer-based biometric gait recognition[END_REF][START_REF] Muaaz | Accelerometer based Gait Recognition using Adapted Gaussian Mixture Models[END_REF][START_REF] Lewis | Real Time Motion-Based Authentication for Smartwatch[END_REF]. Table 3 demonstrates that the performance of using SD scenario is overly optimistic (i.e., EERs of 0.15% and 3.73% for the Acc and Gyr respectively). While the EERs are increased to 0.93% (for Acc) and 8.29% (for Gyr) under the CD test, this is the more realistic evaluation scenario as it avoids training the user's model every day.

Although sensor based-authentication systems could be implemented using accelerometers and/or gyroscopes as the source triaxial (three axes) sensor, the results clearly indicate that the gyroscope is not as effective as the accelerometer for authentication, which is consistent with what other researchers have found [START_REF] Ai-Naffakh | Activity Recognition using Wearable Computing[END_REF][START_REF] Al-Naffakh | A Comprehensive Evaluation of Feature Selection for Gait Recognition Using Smartwatches[END_REF][START_REF] Al-Naffakh | Unobtrusive Gait Recognition Using Smartwatches[END_REF][START_REF] Johnston | Smartwatch-Based Biometric Gait Recognition[END_REF][START_REF] Weiss | Smartwatchbased activity recognition: A machine learning approach[END_REF].

For example, the gait activity reported EERs of 0.15% and 0.93% for the SD and CD scenarios respectively, compared to 3.73% and 8.29% EER's by using the Gyr data of both scenarios respectively. Further experiments were conducted and the results presented in Table 4 in order to highlight the impact of selecting the most discriminative features subset for classification. The proposed feature selection approach successfully discarded some irrelevant and/or redundant features and improved the system accuracy. Impressive results were achieved by using the SD scenario for the walking activity (an EER of only 0.13% compared to EERs of 1.4%, 2.9%, and 0.65% [START_REF] Johnston | Smartwatch-Based Biometric Gait Recognition[END_REF][START_REF] Cola | Gait-based authentication using a wristworn device[END_REF][START_REF] Dong | User Authentication Using Motion Sensor Data from Both Wearables and Smartphones[END_REF]). By using a small feature subset of only 20 features, the proposed system can still precisely recognize the users with an EER of 0.78%.

As regards of the game activity, the reported results can be directly compared with the prior art [START_REF] Okumura | A Study on Biometric Authentication based on Arm Sweep Action with Acceleration Sensor[END_REF][START_REF] Kamoi | Biometrie authentication using hand movement information from wrist-worn PPG sensors[END_REF][START_REF] Wang | Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics[END_REF][START_REF] Findling | ShakeUnlock: Securely Transfer Authentication States Between Mobile Devices[END_REF] that reported EERs in the range of 4.27%-13.3% (against to 0.89% in this study). Although the EERs of both activities are increased to 0.69% and 4.54% by applying the CD scenario, these results still managed to produce a high level of security and better than the previous accelerometer-based studies that achieved EERs ranging from 5.6% [START_REF] Kumar | Continuous User Authentication via Unlabeled Phone Movement Patterns[END_REF] to 29.4% [START_REF] Muaaz | Influence of different walking speeds and surfaces on accelerometer-based biometric gait recognition[END_REF]. Table 5 shows that the walking activity contains high levels of distinguished information, hence surpasses the results of the game activity. This most probably due to that more movement data can be obtained when users are walking (compared to a limited motion while playing a game on the touch screen of smartphones). As expected, the results demonstrate that biometric performance is degraded under the more realistic evaluation scenario (i.e., CD scenario), but that smartwatch-based biometrics is still highly recommended and viable to be used at least as a complementary mechanism to password-based authentication.

The Impact of the Proposed Dynamic Feature Vector

As mentioned earlier, it is clear that the proposed feature selection method was capable of reducing the number of features and has a positive impact on the system performance. With respect to the feature subset size, the reported EERs in Table 4 show that the SD test for both activities, requires less features than the CD (i.e., 60 and 50 features for the walking and game activities respectively). This could be explained because the user's arm pattern could be vary or be inconsistent over the time, hence more features are required for individual to be identified. Moreover, the selected feature subset was fixed for all users (i.e., the size of the user's reference template of each activity was same for all users such as 60 features). Therefore, creating a dynamic feature vector size for each user independently might greatly reduce the EER (see Figure 3). As shown in Figure 3, reducing the number of features of the NW activity from 70 to 40 features decreased the EER for the majority of users or remains similar (apart from users 10, 16 18, 19, 24, 25, 30, 31, and 48 that negatively affect the overall system accuracy). Surprisingly, the EER was even better for some of the users (e.g., 3, 6, 10, 17, 20, 29, 37, 38, 40, 44, 47, 50, and 51). Therefore, the creation of dynamic feature vector size might offer better accuracy/error rates.

Majority voting schema

So far all the presented results were based upon classifying single sample in order to calculate the EER. Although the findings in Table 4 yield quite good results, it was interesting to find out the possibility of reducing the amount of the rejected samples of a genuine user. Several studies [START_REF] Nickel | Scenario test of accelerometer-based biometric gait recognition[END_REF][START_REF] Nickel | Classifying accelerometer data via Hidden Markov Models to authenticate people by the way they walk[END_REF][START_REF] Nickel | Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm[END_REF][START_REF] Johnston | Smartwatch-Based Biometric Gait Recognition[END_REF] have investigated the use of majority and quorum voting schemas in order to make a decision. The former is a scheme which accepts a user as genuine if a half or more of the user's test samples are positive; The biometric decision is then based upon merging multiple classification output to a single one. The latter is a method that authenticates a user as genuine if a requisite number of the user's samples are positive.

Although quorum voting usually yields greater performance, the majority voting appears to be more resilient to error given the higher threshold for classification. Quorum, while lowering the level of accuracy required to verifying a user, may result in a high false acceptance rate. This failure to identify imposters can be explained by the extremely low proportions of correct classifications required to accepting a user as genuine. Although this may be acceptable for systems more concerned with usability, such permissiveness will most likely render the system impractical for most uses. Majority voting, while requiring the system to be more discriminative, offers a greater level of security and thus is more likely to offer a suitable balance between usability and security. Ultimately, conscious decisions must be made to create a system that does not appear to the end user as too demanding without compromising too much security. Therefore, this study utilized the majority voting rather than the quorum voting schema.

As shown in Table 5, the majority voting scheme yields significant improvement on the system performance. At best, the EERs of the walking and game activities were 0.05% and 2.53% respectively (compared to 0.69% and 4.54% of EERs when a single-sample evaluation was used). It is also interesting to notice that only 10 features were required for the game activity to produce the lowest EER. This might be ex-plained because the user's arm pattern for this particular activity was consistent hence, less features was required to verify the legitimate user.

Conclusion and Future Work

This study shows that smartwatch-based activity recognition can produce vastly superior results when evaluated properly by using the realistic CD scenario. It does show that the results do improve when authentication decisions are made using the majority voting schema rather than single 10-second sample of data. This paper shows that the proposed feature selection approach has a positive effect on the system accuracy with a reduction of 32% of the whole features. For example, 60 features were used for the walking activity rather than 88 features in order to produce the lowest EER. It is also examined the effect of using the CD scenario on the system performance. Overall, this study serves as an endorsement for smartwatch-based activity recognition. Future work will explore the impact of the dynamic feature vector size for each user, applying a sensor fusion approach to combine the smartwatch accelerometer and gyroscope data, and testing different segment sizes (e.g., 7 and 5 seconds). Additionally, whilst this study utilized cross-day data collection, collecting real life data (i.e., users do not need to perform certain activities, but merely wear the smartwatch for a prolonged period) will enable a real-world evaluation of the approach. The challenge then becomes being able to identify which activity a user is doing -in order to be able to select the appropriate classifier to utliise. As such, future research will also focus upon developing a context-aware approach to predict the activity.

Figure 1 :

 1 Figure 1: the Acceleration Signal Before and After Filtering 3.2. Feature extraction As illustrated earlier, the raw Acc and Gyr signals are segmented into 10 seconds of time-series data and hence represented by a fixed set of features. In total, 88 of the time domain features were extracted based upon prior work identified in gait and activity recognition studies [10-20]. These features are the same regardless of whether the sample is being generated from Acc and Gyr sensor data. Since most features are generated on a per-axis basis and each sensor has 3 axes, most features are represented by a vector of three values. Details of these features (e.g., what they are and how they are calculated) are presented in Table2.

Features NF Description Interquartile range 3

 3 The range in the middle of the data. It is the difference between the upper and lower quartiles in the segment Skewness 3 A measure of the symmetry of distributions around the mean value of the segment Kurtosis 3 A measure of the shape of the curve for the segment data Percentile 25,50 6 The percentile rank is measured using the following formula: R = (P/100) * (N+1). Where R represents the rank order of the values, P : percentile rank, and N is the total number of data points Correlation Coefficients 3 The relationship between two axes is calculated. The Correlation Coefficients is measured between X and Y axes, X and Z axes, and Y and Z axes. Difference 3 The difference between the maximum and minimum of the values in the segment Median 3 The median values of the data points in the segment Root Mean square 3 The square root of the mean squared Maximum 3 The largest 4 values are calculated and averaged. Minimum 3 The smallest 4 values are calculated and averaged Average 3 The mean of the values in the segment. Standard Deviation 3 The Standard Deviation of the values in the segment Average Absolute Difference 3 Average absolute distance of all values in the segment from the mean value over the number of data point Time Between Peaks 3 During the user's walking, repetitive peaks are generated in the signal. Thus, the time between consecutive peaks was calculated and averaged. Peaks Occurrence 3 Determines how many peaks are in the segment. Variance 3 The second-order moment of the data. Cosine Similarity 3 All pairwise cosine similarity measurements between axes Covariance 3 All pairwise covariances between axes. Binned Distribution 30 Relative histogram distribution in linear spaced bins between the minimum and the maximum acceleration in the segment. Ten bins are used for each axis. Average Resultant 1 For each value in the segment of x, y, and z axes, the square roots of the sum of Acceleration the values of each axis squared over the segment size are calculated.

Figure 2 :

 2 Figure 2: The Effect of the Dynamic Feature Selection Approach

Figure 3 :

 3 Figure 3: the EER of Using the Walking activity and Utilizing Different Feature Subset Size Separated By Users

Table 1 . Comprehensive Analysis on Gait Authentication Using Mobile and Smartwatch Sensors.

 1

	Study	Approach	Features	Type	Classification methods	Accuracy %	Users	Duration	Device	System Type
	[10] S	TD	NN	91.7 (CCR)	29	SD	M	G
	[11] S	TD&FD	KNN	93.3 (CCR)	28	SD	M	AR
	[12] S	TD	SVM	92 (TP)	315 SD	M	AR
						1 (FP)
	[13] S	TD	RF	5.6 (EER)	57	CD	M	AR
	[14] S	TD&FD	SVM	85 (CCR)	5	SD	M	AR
	[15] S	TD	EUC	5 (EER)	22	SD	M	GES
	[16] C	TD	DTW	29.4 (EER)	48	CD	M	G
	[17] C	TD	DTW	21.7 (EER)	48	CD	M	G
	[18] S	FD	HMM	6.15 (EER)	48	CD	M	G
	[19] S	FD	KNN	8.24 (EER)	36	CD	M	G
	[20] C	TD	GMM UBM	14.4 (EER)	35	CD	M	G
	[21] S	TD	RF	10 (FRR), 0 (FAR)	20	SD	SW	AR
	[22] S	TD	RF	1.4 (EER)	59	SD	SW	G
	[23] S	TD	DTW	3.3 (EER)	26	CD	SW	GES
	[24] S	TD	SVM	88.5 (CCR)	13	SD SW&M	G
	[25] S	TD & FD	KNN	88.4(TP), 1.3 (FP)	10	SD	SW	GES
	[26] S	TD	RF	8.8 (EER)	15	SD	SW	GES
	[27] S	TD	RF	93.3 (CCR)	17	SD	SW	AR
	[28] S	TD&FD	KNN	95 (CCR)	40	SD	SW	G
	[29] S	TD	RF	4 (EER)	18	CD M+SW	G
	[30] S	TD	KNN	2.9 (EER)	15	SD	SW	G
	[31] S	TD	SVM	0.65 (EER)	20	SD M+SW	G
	[32] C	TD	DTW	30 (FRR), 15 (FAR)	5	CD	SW	GES
	[33] S	TD	Man	4.27 (EER)	10	SD	SW	GES
	[34] S	TD	SVM	6.56 (EER)	20	CD	SW	GES
	[35] S	FD	EUC	13.3 (EER)	29	SD M+SW	GES
	[36] S	TD	SVM	4 (EER)	20	CD	SW	GES
	[37] S	TD&FD	SVM	92.8 (TP), 0.4 (FP)	30	SD	SW	AR
	[38] S	TD	KNN	5 (EER)	20	CD	SW	G
	Legend: C: Cycle-based; S: Segment-based; TD: Time Domain; FD: Frequency Domain; DTW: Dy-
	namic Time Warping; HMM: Hidden Markov Model; SVM: Support Vector Machine; KNN: k-nearest
	neighbors; RF: Random Forest; NN Neural Network; EUC: Euclidean Distance; Man: Manhattan Distance;
	GMM-UBM: Gaussian Mixture Model-Universal Background Model; KRR: Kernel Ridge Regression;
	EER: Equal Error Rate; CCR: Correct Classification Rate; TP: True Positive; FP: False Positive; FRR:
	False Rejection Rate; FAR: False Acceptance Rate; M: Mobile; SW Smartwatch AR: Activity Recognition;
	G: Gait; GES: Gesture; SD: Same Day; CD: Cross Day.

Table 2 : List of the Extracted Time Domain Features

 2

Table 3 : the EER of SD and CD using the Acc and Gyr Sensors of Walking Activity Activity Type Evaluation Scenario Sensor Type

 3

				All
				Features
	Walking	SD	Acc	0.15
	Walking	CD	Acc	0.93
	Walking	SD	Gyr	3.73
	Walking	CD	Gyr	8.29

Table 4 : the EERs of the SD and CD test for the Walking and Game Activities

 4

	Activity	Type	Evaluation	Scenario	Sensors	10 Features	20 Features	30 Features	40 Features	50 Features	60 Features	70 Features	80 Features	88 Features
	Walking	SD	Acc	1.13 0.78 0.24 0.26 0.27 0.13 0.20 0.16 0.15
	Walking	CD	Acc	4.68 2.39 1.43	0.9	0.84 0.83 0.69 0.77 0.93
	Walking	SD	Gyr	6.6	4.88 3.63 3.74 3.12 3.58 3.48 3.43 3.73
	Walking	CD	Gyr 11.09 9.76 8.62 8.49 8.94 8.53 8.42 7.97 8.29
	Game	SD	Acc 2.40 1.76 1.38 1.18 0.89 1.20 1.14 1.20 1.33
	Game	CD	Acc 4.97 4.82 4.83 4.79 4.62 4.54 5.17 5.80 5.61
	Game	SD	Gyr	8.7	7.18 6.12 6.74 6.53 6.67 6.44 6.91 7.11
	Game	CD	Gyr 12.88 11.08 10.40 9.96 10.21 10.33 10.09 10.20 10.82

Table 5 : the EER of CD Scenario using the Majority Voting for the Walking and Game Activities

 5

	Activity	Type	Evaluation Scenario	Sensors	10 Features	20 Features	30 Features	40 Features	50 Features	60 Features	70 Features	80 Features	88 Features
	Walking	CD	Acc	1.68 0.51 0.21 0.15 0.08 0.05 0.12 0.18 0.15
	Game	CD	Acc 2.53 2.90 2.77 3.40 2.94 2.81 2.74 3.33 3.12