A dynamic evidential network for fall detection.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 18, NO. 4, JULY 2014

1103

A Dynamic Evidential Network for Fall Detection Paulo Armando Cavalcante Aguilar, Jerome Boudy, Dan Istrate, Bernadette Dorizzi, and João César Moura Mota

Abstract—This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster– Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases. Index Terms—Dempster–Shafer theory (DST), dynamic evidential networks (DENs), fall detection, heterogeneous sensors data fusion, remote healthcare monitoring, temporal belief filter (TBF).

I. INTRODUCTION LDERLY population has been growing over the last few decades. This population is less autonomous and more prone to domestic accidents. Among these accidents, falls are a major public health problem that affects every year tens of millions of elderly people worldwide, yielding physical and psychological consequences. Generally, elderly people cannot lift themselves on their own after falling; therefore, it is important to detect this event as soon as possible. This problem is particularly relevant to researchers in the field of telehealth technologies [1].

E

Manuscript received April 25, 2013; revised August 12, 2013; accepted September 12, 2013. Date of publication October 16, 2013; date of current version June 30, 2014. This work was supported in part by the European FP7CompanionAble project and in part by the Brazilian Mobility Program Ciencias sem Fronteiras—CNPq, Brazil. P. A. C. Aguilar, J. Boudy, and B. Dorizzi are with the Department of Electronics and Physics, Institute Mines Télécom–Télécom SudParis, Evry 91011, France (e-mail: [email protected]; jerome. [email protected]; [email protected]). D. Istrate is with the Ecole Supérieure d’Informatique et Génie des Télécommunications (ESIGETEL), 94800 Villejuif, France (e-mail: dan. [email protected]). J. C. M. Mota is with the Department of Tele-Informatics, Federal University of Ceará, CEP 60455-760, Fortaleza, Brazil (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JBHI.2013.2283055

Fall detection systems have been highly developed relying on embedded accelerometers [2]–[5], computer vision [6], robotics [7], and multisensors systems [8] such as the multimodal home healthcare telemonitoring system [9], developed at Télécom SudParis and ESIGETEL, described in this paper. This telemonitoring system allows the detection of distress events and is composed of two complementary systems: RFPAT [3] and GARDIEN [10]. The GARDIEN system consists of a set of infrared sensors able to detect a person’s location and movement. RFPAT modality is able to measure a person’s vital (pulse) and actimetric (movement and fall) signals. In the framework of heterogeneous sensor systems, data fusion methods [11] are needed for combining information of many different sensors in order to obtain better quality information. Different methods for data fusion were proposed in the literature such as probabilistic methods [12], fuzzy subsets based methods [9], [13], and Dempster–Shafer theory (DST)based methods [14], [15]. These methods are also considered as the most suitable for representing imperfect information, important for modeling the information in a more reliable way. Mathematical objects handled by such theories are very closely related; however, DST can be considered as the most general one, being able to account for imperfections as imprecision, uncertainty, and conflict. As a matter of fact, probabilistic and fuzzy subsets theories are particular cases of the DST [16]. Analogous to the Bayesian networks, the DST relies on a graphical representation called evidential network [17]–[19]. In [20], we have proposed a fusion model of GARDIEN and RFPAT telemonitoring systems based on static evidential networks (SENs) for fall detection, focusing on the detection of soft falls, i.e., with soft impact on the ground. In this paper, we extend the formalism of [20] using matrix notations, which are more easy to understand and to implement, compared to those in [20]. We also propose as a new result a novel implementation of GARDIEN fusion through a dynamic evidential network (DEN) model. Indeed, the nonstationarity of sensors’ data, such as measurement noise, signal variability, malfunctioning or unreliable sensors, can lead to a degradation of the evidential networks. In order to cope with this problem, a DEN is proposed [21]. Based on dynamic approaches, such as a temporal belief filter (TBF) [22], DENs are adaptive evidential networks capable of modeling and analyzing the influence of time and uncertainty on the degradation of the system. In this paper, we aim to enable the GARDIEN system for fall detection in situations where the RFPAT system is not available. We propose, therefore, to use a DEN-based on a TBF model [22] to ensure the temporal coherence of the information of the GARDIEN infrared sensor data. This approach was evaluated through simulated fall scenarios.

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This paper is organized as follows: Section II further details the Télécom SudParis/ESIGETEL home healthcare telemonitoring system used in this study. Section III presents the stateof-the-art methods for heterogeneous fusion that account for imperfections in the sensors’ measurements. In Section IV, we give a brief review of the main propositions of DST. Section V presents our implementation of evidential networks applied to fall detection. In Section VI, DENs are introduced and the corresponding implementation and results are presented. Finally, Section VII presents conclusions and perspectives. II. HOME HEALTHCARE TELEMONITORING SYSTEM A remote medical patient’s monitoring system with alarm management [9], if integrated in a smart home environment, can use the fusion results of several observation data such as actimetric and vital signals captured by a device worn by the patient, external sensors such as acoustic and presence signals. Such a “Télévigilance” platform exists at Telecom SudParis elaborated in close collaboration with ESIGETEL [23] and U558INSERM [10]. This alarm management platform is composed of three detection subsystems/modalities: GARDIEN [9], [10], RFPAT [3], [9], and ANASON [9], [23]. In this new application work on the evidential network, we first focused on two of these modalities: GARDIEN and RFPAT. The RFPAT system was designed for the remote monitoring of vital and actimetric signals recorded on the dependent people. This system is composed of a wearable terminal carried by the patient that can automatically identify distress situations such as falls, abrupt changes of cardiac rhythms (namely bradycardia trend) or a person’s activity (movement rate and posture). The GARDIEN system consists in a fixed network of wired or wireless infrared motion sensors placed within the smart home environment and external to the person. These sensors are activated by body movements which therefore provide the localization of a person inside its covering area. Other information, such as the movement rate and the person’s posture, can also be estimated from the combination of two types of infrared sensors: one for the horizontal detection field, and the other one for the vertical detection field [10]. III. FALL DATABASE Remote healthcare monitoring systems suffer from a lack of experimental data and medical databases intended for their validation and improvement. This has motivated us to create and record data that can represent different types of normal and abnormal events related to the elderly or dependent persons. In the context of projects and research conducted by Telecom SudParis and ESIGETEL, we recorded databases, such as HOMECAD [9] and CHUTES [20]. In these databases, the vital, sound, movement, and location signals are extracted from the RFPAT, GARDIEN, and ANASON systems to describe the context of the everyday life of elderly people at home. Hence, we obtain more realistic conditions to feed decision systems and detect distresses. These databases were created after scenarios based on actual situations that reflect the daily life of the elderly. To define these

scenarios, we researched the CompanionAble project, where some elderly people living alone were followed by a team of health professionals (Broca Hospital, SAMU-92) to record and describe their daily activities. The duration of a certain scenario varies between 3 and 10 min and they are divided into two categories: a critical scenario with a distress event or a normal scenario without distress, as described by the following examples. A critical scenario The actor is sitting on a chair in the living room, he reads a newspaper; (120 ). He gets up and goes to the toilet and the bathroom; (60 ). He leaves the bathroom, he enters in the kitchen to prepare coffee; (180 ). He returns to the living room, and he drinks his coffee; (120 ). He gets up, he stumbles and falls, and he stays down. (120 ). Normal scenario The actor returns home, he closes the door, he puts the keys on the table; (60 ). He goes to the bathroom to wash his hands; (60 ). He goes to the living room and turn on the TV to watch the news; (240 ). He lies down on the couch to take a nap. (240 ). These recordings were made by young researchers in the laboratory of Televigilance of Télécom SudParis that simulates a mini apartment of 25 m2 . These databases contain data of 33 critical scenarios, including 10 “hard” falls and 12 “soft” falls, and 5 normal recordings. We see as opportunities an expansion of these databases with more normal and critical situations. Then we want to allow open access of these databases to the scientific community. IV. DEMPSTER–SHAFER THEORY In this section, we describe in more detail the DST, also known as belief functions theory or even evidence theory as we use it in our framework of heterogeneous data fusion for fall detection. DST can be seen as a generalization of the Bayesian theory of subjective probability. It was introduced by Shafer in 1976 [15], based on Dempster’s previous works [14]. Smets [24] has contributed to the development of this theory with his transferable belief model (TBM). The basics of DST are summarized as follows. The frame of discernment Θ represents a finite set containing mutually exclusive and exhaustive events about a given knowledge. The power set of Θ, i.e., the set of subsets of Θ, is represented by 2Θ . A basic belief assignment (also called a belief mass function) of an event A ⊆ 2Θ is a function m(·) : 2Θ → [0, 1] assigning beliefs over the power set 2Θ , and not only to the singleton events of Θ, that must satisfy the following conditions: m () = 0

(1)

and A ⊆Θ

m(A) = 1.

(2)

AGUILAR et al.: DYNAMIC EVIDENTIAL NETWORK FOR FALL DETECTION

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The mass m(A) is a subjective nonadditive measure of the chances of occurrence of the event A; in other words, it quantifies to which extent the event A is credible. It represents the mass of belief exactly committed to A, and not to any of its subsets. An element A ∈ 2Θ , for which m(A) > 0, is called focal element. In axiom (1), the mass of empty set (impossible event) is zero (closed world assumption). Axiom (2) imposes a normalization condition which leads to a belief distribution. Different from the probability theory, DST uses an interval [Bel(A), Pl(A)] as opposed to a single number for representing our knowledge about a given event A. The lower and upper limits of a mass are represented by two nonadditive measures called belief and plausibility, respectively, defined as follows: m (B) (3) Bel (A) = B |B ⊆A

and Pl(A) =

m(B).

(4)

B |B ∩ A= 0

The belief function Bel (A) represents the whole mass of belief that comes from all subsets of Θ included in A. The plausibility function Pl (A) represents the whole mass of belief that comes from all subsets of Θ intersecting A. When the belief distributions come from different sources in the same frame of discernment Θ, a new belief distribution representing the consensus of such opinions can be obtained. Let m1 , . . . , mno be belief mass functions over Θ representing no independent sources. B, . . . , C represent the focal elements of m1 , . . . , mno , respectively. A new belief mass function m1,...,no is obtained by a conjunctive sum defined by the orthogonal sum m1,...,no (·) = m1 (·) ⊕ · · · ⊕ mno (·): m1,...,no (A) = m1 (B) · · · mno (C). (5) B ∩··· ∩ C =A

In the presence of conflicts, axioms (1) and (2) are in contradiction and might be reestablished through a normalization operation proposed by Dempster over the conjunctive sum (5), also called Dempster’s rule of combination: m1 (B) · · · · · mno (C) (6) m1,...,no (A) = B ∩··· ∩ C =A 1−K where K = m1,...,no () = m1 (B) · · · · · mno (C) (7) B ∩··· ∩ C =

measures the conflict degree between the combined sources. The 1 − K normalization factor allows removing the residual conflict in m () = 0 and distributing it among the events in the frame of discernment. K = 1 corresponds to a total conflict, which means no intersection between the focal elements of the belief distributions of the fused sources. The conjunctive combination rule (5) requires the sources to be reliable. A more prudent rule is that of the disjunctive sum defined as m1 (B) · · · · · mno (C). (8) m1,...,no (A) = B ∪··· ∪ C =A

The disjunctive sum is particularly relevant when at least one source is reliable. We find two main approaches for the decision step: by a maximum of plausibility or a maximum of pignistic probability criterion. The pignistic transformation, BetP (A) =

= B ⊆Θ

m(B)

|A ∩ B| |B|

(9)

allows transforming the basic belief function into a probability function by redirecting the ignorance to the singleton events. V. EVIDENTIAL NETWORKS Analogous to the Bayesian networks, the DST can be used in a graphical representation for modeling multisource systems called evidential networks [17]–[19]. The evidential networks are acyclic oriented graphs, which represent imperfect knowledge based on a hierarchical network of ontologies. An evidential network is defined as a couple: G = ((N, A) , M ), where (N, A) represents the graph with a set of nodes N , a set of edges A, and a set of belief masses associated with each node M . When a node is not a root node, i.e., when it has parent nodes, its belief mass distribution is defined by evidential operations of propagation that describes the compatibility relationship between the frames of discernment of this node and its parents. For a root node, an a priori belief mass table is defined in a belief mass assignment step. This evidential network model is static and it does not take into account the temporal evolution of the network parameters (sensors data, noise, mass estimations, model, etc.). A. Evidential Networks for Fall Detection The telemonitoring systems studied in this work (RFPAT and GARDIEN) provide both redundant and complimentary information and their fusion can yield more reliable measurements. The RFPAT system is already a fall detector. The goal of the fusion is to improve the RFPAT fall detection in the case of the so-called soft falls, i.e., with soft impact on the ground. We remind that the RFPAT system presented good performance in fall detection on previous experiments in a hospital environment; however, it is unable to detect soft falls. Contextual information (i.e., information coming from environment sensors), such as the interaction of the person with the environment (person’s activity and location) would be very useful for fall detection and should also be incorporated in the fusion process. Based on [18] and [19] that propose an evidential network for activity recognition in a smart home environment, in [20] we have proposed a fusion model of RFPAT and GARDIEN telemonitoring systems based on the static evidential network (SENRG ), shown in Fig. 1. In this section, we extend the formalism of [20] by introducing matrix notations, in order to improve their understanding and implementation compared to [20]. The proposed SEN allows for the introduction of a fusion model as a decision tree for the fall detection. Vital, actimetric, and contextual information extracted from different telemonitoring modalities are the input evidences to

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Fig. 1.


Evidential network proposed for fall detection.

this network. This network is an acyclic graph, the green nodes represent the GARDIEN system, the red ones represent the RFPAT system, and those in black represent the fusion of the two systems. The raw binary data from the IR sensors are represented by the round nodes, and the postprocessed RFPAT data, represented by the diamond nodes, are the inputs to this network. The square nodes represent the contextual information (the person location: “Room”), the rectangular nodes represent the activities (movement, posture and fall), and finally the elliptic nodes represent composite activities. The hierarchy and the link between nodes of the network create dependence relationships at several levels between the RFPAT and GARDIEN telemonitoring systems. The evidential operations [18], [19], [25]–[27] represent the compatibility relationships between the frames of discernment of the network nodes. In such a way, they allow propagating the belief mass functions throughout the network nodes. The steps of the SENRG inference process can be summarized as follows and are further detailed in [20]. B. Activity Evidence Inference In this study, we will use the following abbreviations in order to improve the readability of the equations: RFPAT = R, GARDIEN = G, Movement = M , Lying = L, Posture = P , Immobile = I, and Fall = F . A matrix formalism is also adopted. All the network nodes, also called evidence sources, have a binary frame of discernment containing two events with the same name as the node. An IR sensor node represents an evidence source ΘIR with a frame composed of discernment by the two possible events ΘIR = IR, IR , nonexcited IR , and excited {IR}. This source possesses then a belief distribution with three ΘIR = focal elements representedby the belief mass vectors m m IR m ({IR}) m IR, IR , representing the belief masses relative to the events, nonexcited IR , excited {IR},

and nonexcited or excited IR, IR (imprecision). On the next inference steps, the belief mass vector always have elements will ¯ A . this same order: mΘ A = m A¯ m ({A}) m A, We use two kinds of IR sensors (represented by the circular nodes): vertical beam detection (IRv ) for localization (“Room” node) and movement (“MovementG ” node) inference, and with horizontal beam detection (IRh ) aimed at posture (“LyingG ” node) inference [10]. The diamond nodes represent the measurements of the RFPAT system: the posture “RP ” (standing up/sitting down RP or lying down {RP }), the movement “RM ” (immobile RM or moving {RM }), and the fall “RF ” (no fall RF or fall {RF }). The following inference steps are computed for soft fall scenarios extracted from our database. Step 1 (Belief mass functions assignment): Since the main goal of the network is soft fall detection, this inference example is done the following values: through 1) RF = 1: no fall detected by RFPAT; 2) {RP }= 1: lying posture detected by RFPAT; 3) RM = 1: No movement detected by RFPAT; 4) IRh = 1: Lying posture detected by GARDIEN; 5) IRv = 1: No movement detected by GARDIEN; 6) {IRkitchen } = 1: The last excited sensor was in the kitchen. At the beginning of the inference process, we assume that each sensor is 100% reliable. From the sensor measurements, the belief distributions in each node are represented by the belief mass functions vectors, as follows: m ({IRv }) m IRv , IRv ] = [1 0 0] mΘ I R v =[ m IRv mΘ I R h =[ m IRh m ({IRh }) m IRh , IRh ] = [1 0 0] mΘ R P =[ m RP m ({RP }) m RP , RP ] = [0 1 0] mΘ R F =[ m RF m ({RF }) m RF , RF ] = [1 0 0] mΘ R M =[ m RM m ({RM }) m RM , RM ] = [1 0 0]. (10) For inferring the person’s activity in this evidential network, we start by a belief mass assignment step established through the interpretation of the sensors’ measurements. Different from our assumption, sensors are never 100% reliable. Indeed, a sensor provides a representation of physical quantities which usually present errors, bias, noise, delay, etc. It is, therefore important to take into account the inherent measurement imprecision and uncertainty when modeling sensors’ information. Beliefs are modified to take into account the credibility attributed to each sensor, in terms of the discounting rate (0 ≤ r ≤ 1). The discounted mass function is defined as

(1 − r) m(A) A⊂Θ (11) mr (A) = r + (1 − r) m (Θ) A = Θ where for r = 0, the source is fully reliable, for 0 ≤ r ≤ 1, the source is reliable with a discount rate r, and for r = 1 the source is completely unreliable. Manufacturers’ statistics show that their infrared movement sensors have a sensibility and specificity of 95%. Therefore, a


discount rate of 5% is attributed to the belief distributions of the IR sensors assigned in (10). The RFPAT sensor has an a priori sensibility and specificity of 92% [3]. An 8% discount rate is then attributed in (10). The discounted mass functions are then computed using (11) resulting in IRv = [ 0.95 mΘ r

0

ΘI R h

0.05 ] , mr

ΘR mr P

= [ 0 0.92

0.08 ] ,

ΘR mr M

= [ 0.92

0.08 ] .

0

ΘR mr F

= [ 0.95 = [ 0.92

0

0.05 ]

0 0.08 ] (12)

Step 2 (Translating mass functions): A multivalued mapping ΓΘ A →Θ B reflects the compatibility relationship between the focal elements of the frames of discernment of two different sources (ΘA , ΘB ). In fact, it describes which focal elements from the two frames of discernment can be true simultaneously, showing how the beliefs propagate throughout the network. Θ →Θ The evidential operation called translation Γm A B can be used to determine the impact of evidence between the focal elements from ΘA and ΘB compatible sources. In this way, the belief masses from ΘA source are translated to ΘB compatible source as ruled by the multivalued mapping ΓΘ A →Θ B . The belief mass functions previously assigned to the input nodes in (12) are then translated (translation operation) to the compatible activity nodes by using the multivalued mapping as follows: Γm Γm Γm Γm Γm

Θ I R v →Θ M

G

→ mΘ M G = [ 0.95

→Θ L

G

→ mΘ L G = [ 0 0.95

0.05 ]

→ mΘ L R = [ 0 0.92

0.08 ]

ΘIR

ΘR

ΘR

ΘR

h

P

→Θ L

R

F

→Θ F

R1

M

→Θ M

R

0 0.05 ]

→ mΘ F R 1 = [ 0.92

0

→ mΘ M R = [ 0.92

0 0.08 ] .

From the inferred beliefs in (13) and using (14), we have T 0.4 0 0.92 0.08 Θ · m (L R , F R 1 ) = 0.6 0.92 0 0.08 = 0.552 0.368 0.08 T 0 0.92 0.08 0.8 Θ m (L R , L G ) = · 0.2 0 0.95 0.05 = 0 0.926 0.074 T 0.92 0 0.08 0.8 Θ m (M R , M G ) = · 0.2 0.95 0 0.05 = 0.926 0 0.074 . In this weighted sum, the weights’ choice is done as a function of the importance and reliability of each node, as detailed in [20]. Step 4 (Uncertainty propagation of mass functions): In order to provide semantic information, based on the multivalued mapping, the belief distributions of the composite nodes “(LR , FR 1 )”, “(LR , LG ) ,” and “(MR , MG )” are translated to the “FR ”, “LRG ,” and “IRG ” nodes, respectively:

(13)

Step 3 (Composite nodes): On the composite nodes, a new belief distribution is created from the belief distributions of the redundant or complimentary nodes. The redundant information fusion allows removing doubts, increasing the reliability and decreasing the uncertainty of estimations inferred by the network. The fusion of complimentary information allows introducing new information in the network. For such a case, we use an evidential operation of weighted sum: mΘ B = w · MΘ A 1 ,...,Θ A M (14) where w = wΘ A 1 · · · wΘ A M is the weight vector associated with the M sources ΘA 1 , . . . , ΘA M in the sum, and ⎤ ⎡ mΘ A 1 ({A1 }) · · · mΘ A 1 AΘ A 1 ⎥ ⎢ .. .. .. MΘ A 1 ,...,Θ A M =⎣ ⎦ . . . mΘ A M ({A1 }) · · · mΘ A M AΘ A M is the belief distribution matrix of the M sources ΘA 1 , . . . , ΘA M , each with ΘA 1 = · · · = ΘA M focal elements, respectively. In this step, three composite nodes are created,

Θ

(L R , F R 1 )

Θ

(L R , L G )

Θ

(M R , M G )

Γm Γm Γm

0.08 ]

“ (LR , FR 1 ) ”, “ (LR , LG ) ”, and “ (MR , MG ) .”

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→Θ F

→Θ L

R

RG

→Θ I

RG

→ mΘ F R = 0.552 0.368 0.08 → mΘ L R G = 0 0.926 0.074 → mΘ I R G = 0.926 0 0.074 .

The “FR ” node represents the inference of the fall activity by the RFPAT system. As we can observe, there is anambiguity about the lying posture of the person: m FR = 0.552, m ({FR }) = 0.368, and m FR , FR = 0.08. Fusion with the GARDIEN system allows us to decrease this doubt and to confirm that the person fell. Activities such as “Lying” posture and “Immobility” might give evidences about a fall situation. The belief masses from the activity nodes “LRG ” and “IRG ” are then brought together in the composite activity node “(LRG , IRG )” through a weighted sum, as follows: T 0 0.926 0.074 0.8 Θ m (L R G , I R G ) = · 0.2 0 0.926 0.074 = 0 0.926 0.074 . The weights in this sum reflect a greater a priori belief attributed to the “LRG ” node because the “Immobile” activity is used here mostly for characterizing the gravity of the fall. Once on the ground, the person might be moving or immobile (unconscious state). The fact that the person is lying down and immobile does not necessarily mean that the person has fallen. The person might be lying down and immobile in his/her bed or sofa. It is therefore important to know the person’s location in order to decrease the uncertainty of the fall activity inference. Note that there is an ambiguity between “FRG ” and “(LRG , IRG )” activity nodes. The uncertain relationship between these two nodes is modeled by the evidential mapping Γ∗ and represents a

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TABLE I STATE TRANSITION MATRIX

Both methods agree with the superiority of the “Fall” event, which confirms the soft fall situation mentioned at the beginning of the network inference description.

VI. DYNAMIC EVIDENTIAL NETWORKS causality relationship expressed as the state transition matrix FΘ ( L , I ) →Θ F R G given in Table I. RG RG From the uncertain relationship, a new belief distribution of the source ΘF R G is obtained based on the distribution of the source Θ(L R G ,I R G ) and the evidential mapping Γ∗ through the following equation: mΘ F R G = m ⎡

Θ (L

RG ,IRG

0

)

⎤T ⎡

· FΘ ( L 1

⎢ ⎥ ⎢ mΘ F R G =⎣ 0.926 ⎦ · ⎣ 0 0.074

0

0 0.8 0

RG ,IRG

0

) →Θ F R G

⎤

⎥ 0.2 ⎦= [ 0 0.7408 0.2592 ] . 1 (15)

This operation is called propagation. The translation operation is a particular case of the propagation in which the relationship between the focal elements of ΘA and ΘB has no uncertainty. Step 5 (Dempster–Shafer’s combination rule): In the previous steps, fusion of the RFPAT and the GARDIEN systems establishes the following belief distributions: mΘ F R G = 0 0.7408 0.2592 mΘ F R = 0.552 0.368 0.08 which represents the beliefs in the inference of the person’s fall state: no-fall or fall. In order to get the consensus of such distributions and to remove the doubt by increasing reliability and decreasing uncertainty, the Dempster–Shafer combination rule (6) gives us a new belief distribution: mΘ F = [ 0.1318

0.8445

0.0286 ]

(16)

with K = 0.2763, which represents a weak conflict between the fused sources. The Dempster–Shafer combination rule provides a new belief distribution representing the consensus of the distributions inferred by the GARDIEN and RFPAT fused systems. The mass assigned to the focal element m ({F }) is reinforced here. Step 6 (Decision): Once done with the fusion and the final belief distribution as obtained in (16), we have to interpret it in order to make a decision concerning the person fall state. We propose to use two decision methods: maximum of pignistic probability and maximum plausibility. The maximum of pignistic probability (9) gives max BetPΘ F = max 0.1461 0.8588 = 0.8588. The maximum of plausibility (4) gives max PlΘ F = max 0.1604 0.8731 = 0.8731.

The fusion model based on SENs previously presented takes only into account the instantaneous measurements of each sensor in order to infer the person’s fall state. In favorable and stationary (time-invariant) experimental conditions such as those considered in our SEN evaluations it presents satisfactory performances [20]. However, the nonstationarity of the data acquired by the sensors might lead to a degradation of the experimental conditions. Measurement noise, signal variability, malfunctioning or unreliable sensors, might make SEN incoherent, since its parameters are not adaptive. In order to compensate for the effects of the nonstationarity of sensors’ data, we propose a fusion model with evidential networks that evolve in time. This led us to the introduction of the DEN fusion model [21] in our processing, and the subsequent evaluation over a range of simulated fall scenarios corresponding to different use cases. In fact, the DEN is a SEN with a time dimension. Based on this new dimension, the dynamic algorithms allow modeling and analyzing the influence of time and uncertainty on the system degradation. Based on dynamic algorithms such as Markov chains, different methods have been proposed in the literature for dealing with system degradation [22], [28]–[32]. Ramasso [22] has introduced the TBF model in order to avoid the undesirable effects of video quality and experimental condition variations in a video activity recognition framework. The TBF decreases the false activity detection rate due to the analysis of the conflict between the time evolution of the measured beliefs and the evolution model. In such way, the real state transitions are taken into account instead of the false activity detections. In [28], the TBF model is also applied to a sequence of activities recognition (belief scheduler). In [29], an extension of the hidden Markov models (HMM) to the belief functions (EvHMM) was developed. Serir et al. [30] proposed a generalization of the results presented in [29] with a method for automatic parameter training. In [31], Lee et al. proposed a dynamic-weighting-based evidential fusion process in order to improve the confidence level of contextual information. Marhic et al. [32] proposed an evidential approach for detecting abnormal behavior in the presence of unreliable sensors. We have seen in Section V that the SENs-based fusion model of the RFPAT and GARDIEN systems (SENRG ) allows confirming a fall detected by the RFPAT system and also permits detecting soft falls that cannot be detected by the RFPAT system alone. However, when the RFPAT system is not present in our telemonitoring system, the GARDIEN system has to be autonomous for detecting falls. In order to offer such ability to the GARDIEN system and also compensate the effects of the infrared sensor’s nonstationary nature and eventual sensor malfunction, in this section, we have proposed a dynamic evidential network (DENG ) model. This model is based on the addition of


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Fig. 3. Fig. 2.

Steps of the TBF model. TABLE II STATE TRANSITION MATRIX FOR TIME SAMPLES T − 1 AND T

Implementation of the DENG to the output of SENG .

the TBF to the output of the SEN composed by the GARDIEN system (SENG ). Fig. 2 illustrates this process. A. TBF Model The TBF model ensures the following: 1) Temporal Coherence: This avoids sudden changes on the network output between consecutive time samples. The time evolution of the fall is represented by rather slow transitions; fast transitions might be seen as noise on the sensors’ data. The time evolution of the fall is said to be slow because a validation period is considered after the fall occurrence. This validation period aims at decreasing false detections and avoids harmless fall detection, when the person is able to get up before the validation period. 2) Conflict Elimination: Conflicts might lead to erroneous fused information said to be “inconsistent.” A temporal analysis of the conflict allows identifying potentially malfunctioning sensors. 3) Exclusivity: Only one event or class is true at each time sample. This allows removing a maximum of uncertainty at the output of the evidential network. B. Steps of the TBF Model The TBF model, shown in Fig. 3, consists of three steps: prediction, fusion, and model update. These steps are now dethat isable to infer two scribed based on the SENG model possible states: fall ({fall}) or no-fall fall . These exclusive events are represented by the following frame of discernment: Θ = fall, fall . (17) We can then work over 2Θ events, 2Θ = , fall , {fall} , fall, fall .

(18)

1) Prediction: Based on the temporal coherence principle, this step ensures that the state at time sample t is partially equal to that in time sample t − 1. This principle is represented by a

fuzzy implication rule, such as fall rule: if mt−1 ({fall}) , then mt ({fall}) = mt−1 ({fall}) · wfall (19) fall rule : if mt−1 fall , then mt fall = mt−1 fall · wfall . (20) Implication rules are well managed in the TBF framework [33], [34]. Incorporation of such rules in the belief function theory allows adding a priori expert information, yielding a more flexible modeling. State transition between time samples t − 1 and t is represented by the state transition matrix Φ shown in Table II, where wfall ∈ [0, 1] is the belief mass of transition from state {fallt−1 } to {fall 1] is the belief mass of transition from t }, andwfall ∈ [0, state fallt−1 to fallt . In order to avoid conflict propagation that might show up at different TBF steps, only one event (fall or no-fall) is true at each time sample. Let us consider the belief distribution mΘ t−1 of the person’s fall state at time sample t − 1 as fall m ({fall}) m fall, fall ] (21) mΘ t−1 = [ m we can then define the first-order Markovian state prediction as Marhic in [32], Θ m ˆΘ t = mt−1 · Φ.

(22)

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Equation (22) can also be represented as a disjunctive sum (8) such as in [22]: Θ Θ m ˆΘ t = mt−1 ∪ mt,M

(23)

where mΘ t,M is the chosen evolution model M : fall or {fall}. In fact, these models are represented by the two first lines of the transition matrix Φ: mΘ = Φ1 = [ wfall t,fall

0

mΘ t,fall = Φ2 = [ 0 wfall

1 − wfall ]

(24)

1 − wfall ] .

(25)

Predictions (22) and (23) are equivalent. In fact, the disjunctive sum avoids the assignment of more belief than the previous belief distribution mΘ t−1 [22]. In this approach, we use the prediction (23) based on the disjunctive sum. Indeed, in order to assure temporal coherence, only one evolution model must be used at a time and then be modified when a state change is detected. This principle will be made clearer in the next steps. Note that when wM = 1 (i.e., wfall = 1 or wfall = 1), the state prediction is equal to the previous state: Θ m ˆΘ t = mt−1

(26)

which reflects a complete confidence in the state prediction model. Analogously, wM = 0 implies a total ignorance about the current state: m ˆΘ fall, fall = 1. (27) t 2) Fusion Between Prediction and Measurement: After predicting the current state m ˆΘ t , we have to compare it to the actual Θ measured state m ˜ t at the output of the REG in order to verify the coherence between these states. The conjunctive combination rule (5) is used: mΘ ˜Θ ˆΘ t,F = m t ∩m t .

(28)

The conflict measure of this fusion step can be used to quantify the coherence between these states: Θ εt = m () (29) ˜Θ ˆt ∩m t When εt = 0, the prediction and the measurement point to the same state, and they are therefore coherent and can be fused by (28) to produce the filtered output mΘ t . However, for εt = 0, the predicted and measured states are different. In this case, it is necessary to identify if the inconsistency comes from the prediction or from the measurement. Knowing that the fall and {fall} states cannot abruptly change between two consecutive time samples, we can suppose that the error comes from the state measured by SENG . This corresponds to the TBF principle that ensures temporal coherence and avoids abrupt changes on the network states. We chose then to trust the evolution model mΘ t,M and the prediction m ˆΘ t is used to compose the filtered output instead of taking into account a possible measurement error m ˜Θ t Θ in the mt,F fusion. This avoids the propagation of the conflict generated by the conjunctive rule (28) to the filtered output mΘ t :

Θ m ˆt if εt = 0 . (30) mΘ t = if εt = 0 mΘ t,F

When conflict situations are frequent, the disjunctive rule emphasizes the ignorance of the output system and can lead to a total ignorance situation: fall, fall = 1. (31) mΘ t→∞ The interest of working with only one event [ fall 0 fall, fall ] or 0 {fall} fall, fall is based on the fact that, when the predicted and measured states agree (εt = 0), the conwithout junctive rule yields a filtered output mΘ t conflict and only one true event (exclusivity principle: fall or {fall}). A conflict does not necessarily imply a measurement error. It is important to distinguish between measurement error and an actual state change. An analysis of the conflict can help us defining a validity space for the evolution model. 3) Model Update: A conflict analysis is proposed for defining the validation period of the evolution model. When the measured state is different from the prediction, we interpret it as an error. However, the persistence of this conflict might represent an actual state change. The cumulative measurement of the conflict in time indicates a possible state change. We define a quantity CUSSUM as: CUSSUMt = CUSSUMt−1 + εt

(32)

which represents the time cumulated conflict. When CUSSUMt reaches a given warning threshold τw , time sample t is kept in tw and the evolution model is considered as validated. When CUSSUMt reaches a stop threshold τs , the evolution model is changed and a new model is applied from time sample ts : Θ if CUSSUMt ∈ [0, τs ] → mΘ t,M = mt−1,M (33) if CUSSUMt > τs → change mΘ t,M Θ where change mt,M represents the evolution model change [switch between (24) and (25)]. To avoid the inflating effect of low conflicts cumulated over a long time, a fading memory process is proposed. This process allows progressively forgetting past events:

CUSSUMt = CUSSUMt−1 · λ + εt

(34)

where λ represents the fading factor. C. Implementation of the DEN In this section, we have detailed the implementation of the DENG depicted in Fig. 2. For that, we used the following training parameters: 1) state transition probability: wfall = wfall = 0.9; 2) warning threshold: τw = 60; 3) stop threshold: τs = 110; 4) fading factor: λ = 0.9. These values have been empirically optimized after a series of experiments. The temporal evolution of the different stages of the TBF model using a fall scenario extracted from our database is shown in Fig. 4. Fig. 4 presents the comparison of the implementation steps of the TBF model [Fig. 4(b)–(d)] on the output of the SENG


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TABLE III CONFUSION MATRIX OF SENR G AND DENG

TABLE IV PERFORMANCE OF SENR G AND DENG

Fig. 4.

Temporal evolution stages of the TBF model.

[Fig. 4(a)]. Fig. 4(a) shows the temporal evolution of the beliefs in the output of the SENG in a fall scenario of our database. The belief evolution of the fall state is rep in green, m ({fall}) resented by three curves: m fall in blue, and m fall, fall in red. It can be seen that SENG presents false detections m fall on time intervals t ∈ ([0, 18] , [24, 25] , [35] , [43, 48]). The actual fall happens on t ∈ [56, 196], represented by the dotted line (fall label). The proposed TBF model corrects the false detections of SENG . Fig. 4(b)–(d) represent the TBF step of prediction, CUSSUM, and the filtered output, respectively. Fig. 4(b) presents the state prediction based on the evolution model. On the false detection intervals of SENG (t ∈ [0, 18], [24, 25], [35], [43, 48])), we can notice zones where the green and red curves start to switch places. In fact,the disjunctive fall, fall = sum increases the prediction ignorance m ˆΘ t→∞ 1, due to the presence of conflicts [Fig. 4(c)] between SENG measurements and TBF predictions. The CUSSUM, shown in Fig. 4(c), accumulates the conflict between measured [Fig. 4(a)] and predicted [Fig. 4(b)] states. When it crosses the stop threshold τs , the evolution model changes and CUSSUM is reset to zero. We can notice that when we have no conflict, the forgetting factor allows CUSSUM to progressively forget the errors generated by the conflict. Fig. 4(d) shows the output of the TBF model that corrects the false detections of SENG and allows modifying the inference of the actual state when the evolution model changes. D. Experimental Results In order to evaluate the fall detection performance of the GARDIEN system, in the case of the absence of the RFPAT system, we compare the performances between the DENG (TBF model on the GARDIEN system) and the SENRG presented in

Section V (fusion of the GARDIEN and RFPAT systems through a SEN). The DENG was evaluated over the same fall database as SENRG , presented in [20] and described in Section III. This database is composed of 33 fall scenarios (16 hard falls and 17 soft falls) and 5 normal situations (no-fall). The confusion matrix of the DENG was then computed and compared to the results of SENRG as shown in Table III. From these confusion matrices, we obtained the performances of SENRG and DENG in terms of sensibility, specificity, error rate, and correct classification rate, as shown in Table IV. In Table III, we notice that DENG has detected also a soft fall which was not detected by SENRG . In these first experiments, the GARDIEN system seems sufficiently autonomous for fall detection (hard and soft). Thanks to the use of the dynamic model (DENG ), capable of treating the infrared sensors’ nonstationarity. However, we perceive a decrease in the specificity of the system. The fall detection principle of the GARDIEN system, detailed in [20], is based on the analysis of the person’s movement, which might produce false alarms, especially in daily situations of immobility such as sleeping or fainting. The proposed solution to this problem is offering more precise person localization in the house, distinguishing zones with lower movement probability (e.g., sofas, chairs, beds) and zones with higher movement probability (e.g., corridors, kitchen, stairs). In addition to the RFPAT system, the addition of other modalities of vital signal sensors, such as ECG, blood pressure, and oximetry, allows for the identification of fainting therefore detecting other distress situation. VII. CONCLUSIONS AND PERSPECTIVES The concept and implementation of evidential networks for home healthcare telemonitoring systems seem to be a very promising approach in particular for fall detection. This kind of network is suitable for data fusion because it provides a framework relying on human expertise, which allows coping with the lack of real fall situations data making it impossible to learn reliable statistical models. Moreover, it has the ability of treating and fusing heterogeneous data. We have shown that using an evidential network for fusing the GARDIEN and

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RFPAT systems has improved the overall fall detection sensibility as compared to the isolated systems. To avoid the incoherent decisions of the SEN, due to the nonstationary nature of the sensors, a DEN based on the TBF model has been proposed. This network allows detecting system degradation and also offers fall detection autonomy to the GARDIEN system, as shown through the experimental results. One advantage of this approach is to permit the indication of a fall situation when the person does not wear the RFPAT device. Another one refers to the unique utilization of the GARDIEN system which is nonintrusive and can be installed in the entire house, including bathrooms. From our first experiments, we conclude that the GARDIEN system seems sufficiently capable of overall fall detection (hard and soft). However, it is very sensitive to immobility situations, such as sleep, that might produce false alarms. We propose two main directives for future works. 1) Network extension: add more contextual information concerning the person and its habitation, allowing for the inference of other distress situations and a better tracking of the home activities for improving the representation of distress situations. 2) Network adaptivity: based on dynamic algorithms such as those in [22] and [28]–[32]. The study and implementation of new DENs will allow simultaneously parameter adaptation (weights and mass functions) in the function of sensor measurements and architecture reconfiguration, aiming at fusion optimization. Moreover, the automatic detection of nonreliable sensors will be proposed. REFERENCES ´ [1] N. Noury, P. Rumeau, A. K. Bourke, G. OLaighin, and J. E. Lundy, “A proposal for the classification and evaluation of fall detectors,” IRBM, vol. 29, no. 6, pp. 340–349, 2008. ´ Estudillo-Valderrama, L. M. Roa, J. Reina-Tosina, and D. Naranjo[2] M. A. Hernández, “Design and implementation of a distributed fall detection system—Personal server,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 6, pp. 874–881, Nov. 2009. [3] J.-L. Baldinger, J. Boudy, B. Dorizzi, J.-P. Levrey, R. Andreao, C. Perpère, F. Delavault, F. Rocaries, C. Dietrich, and A. Lacombe, “Tele-surveillance system for patient at home: the MEDIVILLE system,” in Computers Helping People With Special Needs. New York, NY, USA: Springer, 2004, pp. 400–407. [4] M. Kangas, A. Konttila, I. Winblad, and T. Jamsa, “Determination of simple thresholds for accelerometry-based parameters for fall detection,” in Proc. IEEE 29th Ann. Int. Conf. Eng. Med. Biol. Soc., (EMBS 2007), 2007, pp. 1367–1370. [5] A. Sarela, I. Korhonen, J. Lotjonen, M. Sola, and M. Myllymaki, “IST R Vivago —An intelligent social and remote wellness monitoring system for the elderly,” in Proc. 4th Int. IEEE EMBS Special Topic Conf. Inform. Technol. Appl. Biomed., 2003, pp. 362–365. [6] R. Cucchiara, A. Prati, and R. Vezzani, “A multi-camera vision system for fall detection and alarm generation,” Expert Syst., vol. 24, no. 5, pp. 334– 345, 2007. [7] S. J. Ruiz, J. Moya, and T. I. Parra, “Fall detection and management in biped humanoid robots,” in Proc. IEEE Int. Conf. Robotics and Automation, 2010, pp. 3323–3328. [8] M. Grassi, A. Lombardi, G. Rescio, P. Malcovati, M. Malfatti, L. Gonzo, A. Leone, G. Diraco, C. Distante, and P. Siciliano, “A hardware-software framework for high-reliability people fall detection,” in Proc. IEEE Sensors, 2008, pp. 1328–1331. [9] H. Medjahed, “Distress situation identification by multimodal data fusion for home healthcare telemonitoring,” Institut Mines Télécom–Télécom SudParis, Evry, France, 2010.

[10] F. Steenkeste, H. Bocquet, M. Chan, and B. Vellas, “Remote monitoring system for elders in a geriatric hospital,” in Proc. Promoting Independence Quality Life Older Persons: Int. Conf. Aging, Arlington, TX, USA, 1999, pp. 2–4. [11] N. Xiong and P. Svensson, “Multisensor management for information fusion: issues and approaches,” Inf. Fusion, vol. 3, no. 2, pp. 163–186, 2002. [12] P. Na¨ım, P.-H. Wuillemin, P. Leray, O. Pourret, and A. Becker, Réseaux Bayésiens. Paris, France: Eyrolles, 2011. [13] L. A. Zadeh, “Fuzzy sets,” Inform. Control, vol. 8, no. 3, pp. 338–353, 1965. [14] A. P. Dempster, “Upper and lower probabilities induced by multivalued mapping,” Ann. Math. Statis., vol. 38, no. 2, pp. 325–339, 1967. [15] G. Shafer, A Mathematical Theory of Evidence. vol. 1, Princeton, NJ, USA: Princeton Univ. Press, 1976. [16] M.-H. Masson, in Apports de la théorie des possibilités et des fonctions de croyance a` l’analyse de données imprécises. Compiègne, France: Université de Technologie de Compiègne, 2005. [17] C. Simon and P. Weber, “Evidential networks for reliability analysis and performance evaluation of systems with imprecise knowledge,” IEEE Trans. Rel., vol. 58, no. 1, pp. 69–87, Mar. 2009. [18] H. Lee, J. S. Choi, and R. Elmasri, “Sensor data fusion using dsm theory for activity recognition under uncertainty in home-based care,” in Proc. Int. Conf. Adv. Inform. Netw. Appl., 2009, pp. 517–524. [19] X. Hong, C. Nugent, M. Mulvenna, S. McClean, B. Scotney, and S. Devlin, “Evidential fusion of sensor data for activity recognition in smart homes,” Pervasive Mobile Comput., vol. 5, no. 3, pp. 236–252, 2009. [20] P. A. C. Aguilar, J. Boudy, D. Istrate, H. Medjahed, B. Dorizzi, J. C. M. Mota, J.-L. Baldinger, T. Guettari, and I. Belfeki, “Multimodal fusion for fall detection,” Int. J. E-Health Med. Commun., vol. 4, no. 1, pp. 46–60, 2013. [21] P. Weber and C. Simon, “Dynamic evidential networks in system reliability analysis: A Dempster Shafer approach,” in Proc. 16th Mediterranean Conf. Control Autom., 2008, pp. 603–608. [22] E. Ramasso, M. Rombaut, and D. Pellerin, “A temporal belief filter improving human action recognition in videos,” presented at the IEEE Int. Conf. Acoustics, Speech Signal Processing, Toulouse, France, 2006. [23] D. Istrate, E. Castelli, M. Vacher, L. Besacier, and J.-F. Serignat, “Information extraction from sound for medical telemonitoring,” IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 2, pp. 264–274, 2006. [24] P. Smets and R. Kennes, “The transferable belief model,” Artif. Intell., vol. 66, no. 2, pp. 191–234, 1994. [25] T. M. Strat, “The generation of explanations within evidential reasoning systems,” in Proc. Lentil Int. Joint Conf. Artif. Intell., 1987, vol. 2, pp. 1097–1104. [26] W. Liu, J. Hong, and M. F. McTear, “An extended framework for evidential reasoning systems,” in Proc. IEEE 2nd Int. Conf. Tools Artif. Intell., 1990, pp. 731–737. [27] X. Hong, Heuristic Knowledge Representation and Evidence Combination Parallelization. Londonberry, U.K.: University of Ulster, 2001. [28] E. Ramasso, C. Panagiotakis, M. Rombaut, and D. Pellerin, “Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition,” Int. J. Approximate Reasoning, vol. 51, no. 7, pp. 846–865, 2010. [29] E. Ramasso, “Contribution of belief functions to HMM with an application to fault diagnosis,” in Proc. IEEE Int. Workshop Mach. Learning Signal Process., 2009, pp. 1–6. [30] L. Serir, E. Ramasso, and N. Zerhouni, “Time-sliced temporal evidential networks: The case of evidential HMM with application to dynamical system analysis,” in Proc. IEEE Conf. Prognostics Health Manage., 2011, pp. 1–10. [31] H. Lee, J. S. Choi, and R. Elmasri, “A dynamic normalized weighting based context reasoning in home-based care,” in Proc. 24th IEEE Int. Conf. Adv. Inf. Netw. Appl., 2010, pp. 804–811. [32] B. Marhic, L. Delahoche, C. Solau, A. M. Jolly-Desodt, and V. Ricquebourg, “An evidential approach for detection of abnormal behaviour in the presence of unreliable sensors,” Inf. Fusion, vol. 13, no. 2, pp. 146–160, 2012. [33] B. Ristic and P. Smets, “Target identification using belief functions and implication rules,” IEEE Trans. Aerosp. Electron. Syst., vol. 41, no. 3, pp. 1097–1103, Jul. 2005. [34] P. Smets, “Imperfect information: Imprecision and uncertainty,” in Uncertainty Management in Information Systems. New York, NY, USA: Springer, 1997, pp. 225–254.


Paulo Armando Cavalcante Aguilar was born in Fortaleza, Brazil, in 1982. He received the B.S. degree in telecommunications engineering from the University of Fortaleza, Fortaleza, Brazil, in 2008, the M.Sc. degree in signal processing from the University of Nice–Sophia Antipolis, Nice, France, in 2009, and the Ph.D. degree in informatics applied to remote healthcare monitoring from the Telecom SudParis, Evry, France, in 2012. He took part in the European project CompanionAble, where he was the in-charge of the state detection module, based on an evidencial Network, and has also worked on an indoor localization module for the same project.

Jerome Boudy received the Ph.D. degree in adaptive filtering for underwater acoustics signal processing from the Université de Nice, Nice, France, in 1988. He joined the Department of Speech Processing, Matra Nortel Communications in 1988 where he worked on speech recognition area and adaptive filtering applied to hands-free communications. In February 2001, he joined the Telecom branch, Philips Group in Le Mans, for R&D on speech recognition. In January 2002, he joined Telecom SudParis as an Associate Professor in signal processing in the Department of Electronics and Physics. He was actively involved in French ANR TecSan-funded projects on remote Healthcare vigilance projects (TelePat, TANDEM, and QuoVadis) for elderly persons. His research interests include biomedical signal processing, pattern recognition, and multichannel data fusion. He was also involved in the European IST-FP7 CompanionAble project (2008–2012), where he developed and assessed a combined concept of CompanionRobot and smart home sensors network for elderly persons. He is currently involved in the AAL vAssist project and is the Vice-Coordinator of the Digital Healthcare network of Institute Mines-Telecom.

Dan Istrate received the Ph.D. degree in signal processing from the Institut National Polytechnique de Grenoble (INPG), Grenoble, France, in 2003. He is currently the Research Laboratory Head (16 researchers) at “Ecole Supérieure d’Informatique et Génie des Télécommunications” (ESIGETEL), Villejuif, France, responsible also for the embedded system for e-Health teaching specialty. He is involved in embedded systems for sound analysis and processing (sound recognition) and in multimodal data fusion. He has participated in six National, one European, and two International Projects like: CompanionAble (FP7), QuoVADis (TecSan2008), and Sweet-Home (VERSO2009). He supervises five Ph.D. students.

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Bernadette Dorizzi received the Ph.D. degree (Thèse d’état) in theoretical physics from the University of Orsay (Paris XI-France), Orsay, France, in 1983, in the field of integrability of dynamical systems. She has been a Professor at Télécom SudParis (ex INT), Evry, France, since September 1989. She led the Department of Electronics and Physics between 1995 and 2009. She is in charge of the Intermedia (Interaction for Multimedia) research team. Her research interests include pattern recognition and machine learning applied to activity detection, surveillance-video, and biometrics. She is the Coordinator of the Bio-Identity Institut Telecom research project (http://www.int-evry/biometrics) and of the BioSecure Foundation (http://biosecure.info). She is the author of more than 300 research papers and has supervised more than 15 Ph.D. theses.

João César Moura Mota received the B.S. degree in physics from the Federal University of Ceará, Fortaleza, Brazil, in 1978, the M.Sc. degree in electrical engineering from the Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, in 1984, and the Ph.D. degree in electrical engineering from the State University of Campinas, Campinas, Brazil, in 1992. He is currently a Professor at the Federal University of Ceará and the Assistant Director for Interinstitutional Relationships of its Technology Center, founding member of the Brazilian Society of Telecommunications, member of the Brazilian Society of Health Informatics, adviser to the Student Branch of the Institute of Electrical and Electronics Engineers (IEEE) in FUC, member of the Signal Processing Society and IEEE Communications Society.

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

Pre-impact fall detection.

An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

A knowledge network for a dynamic taxonomy of psychiatric disease.

Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.

Minimal Increase Network Coding for Dynamic Networks.

Dynamic binding in a neural network for shape recognition.

Analysis of Android Device-Based Solutions for Fall Detection.

A depth-based fall detection system using a Kinect® sensor.

Fall detection for multiple pedestrians using depth image processing technique.

Analysis of Public Datasets for Wearable Fall Detection Systems.

Development of a wearable-sensor-based fall detection system.

Fall Detection Using Smartphone Audio Features.

Predictive structural dynamic network analysis.

Shaping the dynamic mitochondrial network.

Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection.

Noninvasive fetal QRS detection using an echo state network and dynamic programming.

Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.

Efficient source separation algorithms for acoustic fall detection using a microsoft kinect.

Survey on fall detection and fall prevention using wearable and external sensors.

An approximation method for improving dynamic network model fitting.

Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis.

Dynamic network data envelopment analysis for university hospitals evaluation.