Modeling a Risk Detection System for Elderly’s Home-Care with a Network of Timed Automata

Navarrete, Isabel; Rubio, José Antonio Rabadán; Botía, Juan A.; Palma, José; Campuzano, Francisco

doi:10.1007/978-3-642-35395-6_11

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…Some efforts in AAL consider finite state machines for modelling elders' behaviour and patterns [7]. These approaches visualise patterns as state diagrams [24,25]: a state diagram is graph where nodes are states and edges are transitions between them.…”

Section: Related Workmentioning

confidence: 99%

“…Most systems include a data-processing layer to abstract temporal information (from time points to intervals) and graphical visualisation layer for easier interpretation, usually as time lines or Gantt charts [4][5][6]. To act as automatic alarm, some authors propose AI techniques to provide an a-priori description of abnormal behaviour (risk scenario description) in the form of rules, temporal causal patterns or timed automata [7]. Due to false positives, these systems usually combine AI techniques with visualisation of the pieces of data that triggered the alarm.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal data visualisation for homecare monitoring of elderly people

Juárez

Ochotorena

Combi

2015

Artificial Intelligence in Medicine

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal data visualisation for homecare monitoring of elderly people

Juárez

Ochotorena

Combi

2015

Artificial Intelligence in Medicine

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“…However, in our domain, spatial data refer to a few locations visited frequently, describing simpler spatial-temporal patterns. Some efforts done in AAL consider finite state machines for modelling elder behaviour and patterns [7]. These approaches visualise patterns as state diagrams [19], [20]: a state diagram is graph where nodes are states and edges are transitions between them.…”

Section: ) Aberrant Behaviourmentioning

confidence: 99%

“…Indeed, most systems include a data-processing layer to abstract temporal information (from time points to intervals) and a graphical visualization for an easier interpretation, usually as time lines or Gantt charts [4], [5], [6]. For an automatic alarm, some authors propose AI techniques, providing an a-priori description of the abnormal behaviour (risk scenario description) in the form of rules, temporal causal patterns or timed automata [7]. Due to false positives, these systems usually combine AI techniques with the visualization of those pieces of data that triggered the alarm.…”

Section: Introductionmentioning

confidence: 99%

Multiple Temporal Axes for Visualising the Behaviour of Elders Living Alone

Juárez

Ochotorena

Combi

2013

2013 IEEE International Conference on Healthcare Informatics

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Elders who live alone are vulnerable population at risk. Despite telemedicine and clinical home monitoring are effective to provide health care, accidents at home are still one of the major risk factors. Important efforts have been done in home monitoring systems to detect potential scenarios of risk. However, the complex technological interaction has a negative impact on the user and intrusive or wearable devices are not recommended. Sensorized houses solve this problem, recording the behaviour of elders at home without a direct interaction. However, the detection of health-risk scenarios is a complex issue, since the large amount temporal data registered by sensors must be correctly interpreted. In such a case, Artificial Intelligence techniques have been used to automatically identify abnormal behaviors. Due to the number false positives of these techniques, the human intervention is still necessary. We consider that a semiautomatic approach, abstracting and visualizing the recorded temporal information, is a more suitable approach. In this work, we propose a specialised visualization model that helps system supervisors to identify through visual mining techniques most common scenarios of risk. We illustrate our proposal in a specific home monitoring system.

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“…Indeed, in disease management, the medical staff does not focus on which activities are performed but rather on deviations of some particular activities, symptomatic of improvements or deteriorations of the health of the inhabitant. Behaviour deviations have been studied in some works like [36], [14] or [37], where the authors use the localization of the inhabitant to estimate a potential risk if he stays too long in a room. The authors of [38], [39], [40] can detect behaviour deviations but use mainly multi-context information.…”

Section: Behaviour Deviation Detectionmentioning

confidence: 99%

Activity Discovery and Detection of Behavioral Deviations of an Inhabitant From Binary Sensors

Saives

Pianon

Faraut

2015

IEEE Trans. Automat. Sci. Eng.

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The aim of this paper is to improve the autonomy of medically monitored patients in a smart home instrumented only with binary sensors; overwatching the disease evolution, that can be characterized by behaviour changes, is helped by detecting the activities the inhabitant performs. Two contributions are presented. On one hand, using sequence mining methods in the flow of sensor events, the most frequent patterns mirroring activities of the inhabitant are discovered; these activities are then modeled by an extended finite automaton, which can then be used for activity recognition and generate activity events. On the other hand, given the set of activities that can be recognized, another automaton is built to model requirements from the medical staff supervising the inhabitant; it accepts activity events, and residuals are defined to detect any behaviour deviation. The whole method is applied to the dataset of Domus, an instrumented smart home. Note to Practitioners-This paper was motivated by the will of providing a model of the behaviour of the inhabitant at home, from which formal methods can be applied to verify some properties, namely detect behaviour deviations. The input of the framework is a log file of binary sensor events (rising and falling edges); a pre-filtering of the log might be required to get rid of repetitive, noisy events (namely spurious events generated by motion sensors, for which only the first rising edge and last falling edges are relevant). This recorded log is assumed to be recorded without sensor faults, and to be characteristic of a typical routine of the inhabitant. In order to build this model, based on the Extended Finite Automata (EFA) formalism, sequence mining techniques (first contribution of the article) are adapted: the more frequent the sequence is, the more important for the inhabitant the habit is. The second contribution of the paper is the ability to detect behaviour deviations, which is criticial for healthcare at home and disease management. The technique of "residuals", mainly used in manufacturing, has been adapted to EFAs. It requires medical requirements, defined by the medical staff, based on the activities it wants to keep an eye on. A suiting log of sensors events and medical requirements are the only prerequisite to the application of the method.

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Modeling a Risk Detection System for Elderly’s Home-Care with a Network of Timed Automata

Cited by 5 publications

References 9 publications

Spatiotemporal data visualisation for homecare monitoring of elderly people

Spatiotemporal data visualisation for homecare monitoring of elderly people

Multiple Temporal Axes for Visualising the Behaviour of Elders Living Alone

Activity Discovery and Detection of Behavioral Deviations of an Inhabitant From Binary Sensors

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