By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.

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SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results

Smart homes and home health monitoring technologies for older adults: a systematic review

Results. M ean age was 83.3 years, mean years of education was 15.5, and 73% of cohort were women. During a 4-week snapshot, participants left their home twice a day on average for a total of 208 min per day. Mean in-home walking speed was 61.0 cm/s. Participants spent 43% of days on the computer averaging 76 min per day. Discussion. These results demonstrate for the first time the feasibility of engaging seniors in a large-scale deployment of in-home activity assessment technology and the successful collection of these activity metrics. We plan to use this platform to determine if continuous unobtrusive monitoring may detect incident cognitive decline.

Intelligent Systems for Assessing Aging Changes: Home-Based, Unobtrusive, and Continuous Assessment of Aging

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: a systematic review.

In the framework of context awareness within the home, our team is currently assessing the unobtrusive detection of inhabitants' activity through the monitoring of their use and consumption of electricity. The objective is to develop a system for the remote monitoring of large populations of elderly people living independently at home. To be readily deployable on the field, such a system must be minimally intrusive both for the home environment and for the field professionals (paramedics and social workers) visiting the patients at home. We carried out two successive field experiments to evaluate and to improve our system designed to deliver a single index of daily activity. The first experiment involved 13 elderly persons over a nine-month period (84 240 h data recorded) and the second one 12 elderly over six months (51 840 h). We evaluated both the relevance of the index and the acceptability of the system as a whole. We discovered that electrical activity is a kind of unique “signature” of each person's activity. Moreover, this profile provides unexpected information on the health status of the subject. We confirmed that the system was unobtrusive and well accepted both by the subjects and by the professionals involved. Our unique index of activity, and its trend over time, can provide timely information to the professionals on the patient.

Building an Index of Activity of Inhabitants From Their Activity on the Residential Electrical Power Line

The objective of this project is the remote follow up of the activities of patients living independently in their own home, with the help of ICT. The sensing of activity in the home has a variety of important applications, including home automation, energy monitoring and healthcare. Most of the existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. We present an approach that uses a single sensor to detect the electrical events throughout the home. This sensor detects and identifies the electrical impulses on residential power lines created by the on-off switching of electrical devices. The electrical events are regularly downloaded to a central data base with a web access.

Automatic detection of activities of daily living from detecting and classifying electrical events on the residential power line

This work takes place in the framework of context awareness, with a particular objective to remotely follow up the activities of elderly persons living independently in their own home. Whereas many existing systems for detecting the activity of occupant require large numbers of sensors, invasive vision systems, or extensive installation procedures, we present an approach to extract information from the electrical appliances activities in home. Furthermore, we build a unique indicator which integrates all the activity of the person. Our system was experimented during 6 months within 12 flats occupied by single elderly persons.

Remote follow up of health through the monitoring of electrical activities on the residential power line - preliminary results of an experimentation

We want to objectivize the level of activity of elderly persons living independently at home. Most existing monitoring systems are intrusive and/or require a large number of sensors. We hope the “ubiquitous computing” concept could find an application in this context. We proposed to monitor the use of electrical appliances. We built a unique “activity indicator” which integrates all the activities of the person. This was assessed during 6 months within 12 flats occupied by single elderly persons.

Use of electrical devices reveals our well being

In the context of ubiquitous computing, there is a particular interest in objectivizing the level of activity of elderly persons living independently in their own home. Whereas many existing monitoring systems are very intrusive and/or require a large number of sensors, an indirect approach based on the monitoring of the electrical appliances activities in home seems very attractive. We evaluated this approach and built a unique activity indicator which integrates all the activity of the person. This was experimented during 6 months within 12 flats occupied by single elderly persons.

Ubiquitous but non invasive evaluation of the activity of a person from a unique index built on the electrical activities on the residential power line

M. Giordani

Papers

Building an Index of Activity of Inhabitants From Their Activity on the Residential Electrical Power Line

Automatic detection of activities of daily living from detecting and classifying electrical events on the residential power line

Remote follow up of health through the monitoring of electrical activities on the residential power line - preliminary results of an experimentation

Use of electrical devices reveals our well being

Ubiquitous but non invasive evaluation of the activity of a person from a unique index built on the electrical activities on the residential power line