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... In the following, each activity is associated with two key criteria, also used in reference [62]: ...
A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly’s behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity
... Change-point detection is one of the fundamental problems in time series analysis and has many applications including in environmental science [1], healthcare [2], financial analysis [3] and human activity analysis [4]. The goal of changepoint detection is to identify the time points at which the underlying physical process that generates the time series data has changed, by analyzing only the observed time series data, without knowing the underlying process. ...
Change-point detection in a time series aims to discover the time points at which some unknown underlying physical process that generates the time-series data has changed. We found that existing approaches become less accurate when the underlying process is complex and generates large varieties of patterns in the time series. To address this shortcoming, we propose Shape-CD, a simple, fast, and accurate change point detection method. Shape-CD uses shape-based features to model the patterns and a conditional neural field to model the temporal correlations among the time regions. We evaluated the performance of Shape-CD using four highly dynamic time-series datasets, including the ExtraSensory dataset with up to 2000 classes. Shape-CD demonstrated improved accuracy (7-60% higher in AUC) and faster computational speed compared to existing approaches. Furthermore, the Shape-CD model consists of only hundreds of parameters and require less data to train than other deep supervised learning models.
... Our notion of activity comprises two key criteria used also in [10] that are at the basis of our verification process: ...
Smart environments and technology used for elder care, increases independent living time and cuts long-term care costs. A key requirement for these systems consists in detecting and informing about abnormal behavior in users’routines. In this paper, our objective is to automatically observe the elderly behavior over time and detect anomalies that may occur on the long term. Therefore, we propose a learning method to formalize a normal behavior pattern for each elderly people related to his Activities of Daily Living (ADL). We also adopt a temporal similarity score between activities that allows to detect behavior changes over time. In change behavior period we focus on each activity to detect anomalies. A use case with real datasets are promising.
... Our notion of activity comprises two key criteria used also in [10] that are at the basis of our verification process: ...
... Proposed methodology in this thesis targets effective translation of geriatric needs by employing unobtrusive monitoring technologies (e.g., environmental sensors and sensor-enhanced devices) for early detection of possible changes in health status [20][21][22][23][24]. 1 1. 2 ...
... This enables early detection of possible changes in physical and cognitive abilities. This chapter introduces proposed methodology for translating geriatric needs via unobtrusive monitoring technologies [20][21][22][23][24]. ...
... Implementation: ChangeTracker Service for Online Behavior Analysis and Continuous Change Detection Developed web-based service ChangeTracker in this thesis is integrated in ambient assisted living platform UbiSMART [20][21][22][23][24]. UbiSMART platform uses environmental sensor data to infer activities of daily living in real time using semantic reasoning [63,64]. ...
Aging process is associated with serious decline in physical and cognitive abilities. Aging-related health problems present growing burden on public health and economy. Nowadays, existing geriatric services have limitations in terms of early detecting possible health changes toward better adaptation of medical assessment and intervention for elderly people. Bridging the gap between these geriatric needs and existing services is a major enabler to improve their impact. In this thesis, proposed technological approach employs unobtrusiveInternet of Things (IoT) technologies for long-term behavior monitoring and early detection of possible changes. Proposed methodology identifies geriatric indicators that can be monitored via unobtrusive IoT technologies, and are associated with physical and cognitive problems. This thesis develops data processing algorithms that convert raw sensor data into geriatric indicators. These geriatric indicators are analyzed on a daily basis, in order to early detect possible changes. This thesis evaluates and adapts further statistical, probabilistic and machine-learning techniques for long-term change detection. Adapting these techniques discards transient deviations, and retains permanent changes in monitored behavior. Real 3-year deployments in nursing home and individual houses validate proposed approach. Medical clinic geriatrician and nursing home team validate medical relevance of detected changes.
... A simplification of both the installation and the maintenance made the platform suitable for large scale deployments [1,4,2]. Resulting collected data was processed in order to build a human frailty model [3] and detect change of behaviour [8]. ...
... The purpose of the framework is to collect quality of life indicators and use them to detect risky situations [3,15], long-term evolution [8] and to enhance the quality of life by an intervention. The main monitored target group is ageing and frail population, although caregivers benefit as well [10]. ...
Urban Living environments put technologies at disposal of citizens to simplify their daily outdoor activities. Understanding mobility and adjusting it are major enablers to assist the elderly, dependent and frail people while they are outdoors. We propose in our research to extend our indoor Ambient Assisted Living framework by integrating mobility detection using a smartphone. We aim at understanding the outdoor behaviour of an individual and classify their mobility in terms of active and passive mobility. We present a mobility classifier following paradigm of Smart Mobility; definition of active and passive mobility; materials and experiments for training the model reaching accuracy of 95% for specific activities; and the future perspectives to integrate this model within a real deployment pilot site.
... The purpose of the platform is to collect quality of life indicators, using them to detect risky situations [9,10], long-term evolution [11], and to enhance the quality of life by an intervention. The main monitored target group is the ageing and frail population, although caregivers can benefit as well [12]. ...
Decision making by real time rule-based reasoners is prone to errors. Modern systems must implement decisions based on partial, statistical, or approximate information. If a part of the information is missing or corrupted, the reasoner might be misled into reaching wrong conclusions. Systems using monotonic static logic are less affected, as they must be more careful in their decisions by design. On the other hand, human reasoning would prompt for some conclusions even if the conclusions may be wrong, and later rectify them if a new fact was discovered. To mimic similar reasoning in order to achieve users' satisfaction, the use of non-monotonic reasoning is preferred as it allows us to easily model a situation whereby a missing piece of information completely changes the outcome of the reasoner. However, detecting reasoning incoherence is very challenging, and thus there is a need to formulate strategies to deal with reasoning defaulting. In this paper, we propose the use of a replay mode on a part of the dataset, including ground truth data. This would also address the problem of late data arrival. As a case study, this paper discusses the implementation of a decision rectification in our ontology-based tracking system that is currently deployed in real conditions.
Behavior change is associated with important decrease of cognitive and physical capacities among elderly people. Therefore, a proactive detection of long-term behavior changes in early stages of their evolution is a keystone to improve elderly healthcare services. In fact, nowadays’ geriatric methods mainly rely on scales and questionnaires, and are inconvenient to investigate long-term changes on a daily basis. Therefore, our proposed approach for behavior change detection analyzes elderly people behavior over long periods via ambient technologies. In fact, employed technologies are unobtrusive, do not interfere with the natural behavior of elderly people and do not affect their privacy. Furthermore, our long-term behavior analysis is based on the identification of significant behavior change indicators (e.g., mobility, memory, nutrition and social life indicators significantly correlate with cognitive and physical diseases), and the application of efficient statistical techniques that differentiate long-term and short-term changes in analyzed behavior. In addition, our two-year deployment validates our objective technological observations through real correlations with medical observations of nursing-home team.
Aging process is related to serious decline in physical and cognitive functions. Thus, early detection of these health changes is important to improve classical assessments that are mainly based on interviews, and are insufficient to early diagnose all possible health changes. Therefore, we propose a technological approach that analyzes elderly people behavior on a daily basis, employs unobtrusive monitoring technologies, and applies statistical techniques to identify continuous changes in monitored behavior. We detect significant long-term changes that are highly related to physical and cognitive problems. We also present a real validation through data collected from 3-year deployments in nursing-home rooms.
Urban Living environments put technologies at disposal of citizens to simplify their daily outdoor activities. Understanding mobility and adjusting it are major enablers to assist the elderly, dependent and frail people while they are outdoors. We propose in our research to extend our indoor Ambient Assisted Living (AAL) framework by integrating mobility detection using a smartphone. We aim at understanding the outdoor behaviour of an individual and classify their mobility in terms of active and passive mobility. We present a mobility classifier following paradigm of Smart Mobility; definition of active and passive mobility; materials and experiments for training the model reaching accuracy of 95% for specific activities; and the future perspectives to integrate this model within a real deployment pilot site.
