Michel Vacher

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

By 2050, about one third of the French population will be over 65. Our laboratorys 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.

Information extraction from sound for medical telemonitoring

Today, the growth of the aging population in Europe needs an increasing number of health care professionals and facilities for aged persons. Medical telemonitoring at home (and, more generally, telemedicine) improves the patients comfort and reduces hospitalization costs. Using sound surveillance as an alternative solution to video telemonitoring, this paper deals with the detection and classification of alarming sounds in a noisy environment. The proposed sound analysis system can detect distress or everyday sounds everywhere in the monitored apartment, and is connected to classical medical telemonitoring sensors through a data fusion process. The sound analysis system is divided in two stages: sound detection and classification. The first analysis stage (sound detection) must extract significant sounds from a continuous signal flow. A new detection algorithm based on discrete wavelet transform is proposed in this paper, which leads to accurate results when applied to nonstationary signals (such as impulsive sounds). The algorithm presented in this paper was evaluated in a noisy environment and is favorably compared to the state of the art algorithms in the field. The second stage of the system is sound classification, which uses a statistical approach to identify unknown sounds. A statistical study was done to find out the most discriminant acoustical parameters in the input of the classification module. New wavelet based parameters, better adapted to noise, are proposed in this paper. The telemonitoring system validation is presented through various real and simulated test sets. The global sound based system leads to a 3% missed alarm rate and could be fused with other medical sensors to improve performance

Development of Audio Sensing Technology for Ambient Assisted Living: Applications and Challenges

One of the greatest challenges in Ambient Assisted Living is to design health smart homes that anticipate the needs of its inhabitant while maintaining their safety and comfort. It is thus essential to ease the interaction with the smart home through systems that naturally react to voice command using microphones rather than tactile interfaces. However, efficient audio analysis in such noisy environment is a challenging task. In this paper, a real-time audio analysis system, the AuditHIS system, devoted to audio analysis in smart home environment is presented. AuditHIS has been tested thought three experiments carried out in a smart home that are detailed. The results show the difficulty of the task and serve as basis to discuss the stakes and the challenges of this promising technology in the domain of AAL.

The sweet-home project: Audio technology in smart homes to improve well-being and reliance

The Sweet-Home project aims at providing audio-based interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the multimodal sound corpus acquisition and labelling and on the investigated techniques for speech and sound recognition. The user study and the recognition performances show the interest of this audio technology.

Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living

This chapter presents the AUDITHIS system which performs real-time sound analysis from eight microphone channels in Health Smart Home associated to the autonomous speech analyzer RAPHAEL. The evaluation of AUDITHIS and RAPHAEL in different settings showed that audio modality is very promising to acquire information that are not available through other classical sensors. Audio processing is also the most natural way for a human to interact with his environment. Thus, this approach particularly fits Health Smart Homes that include home automation (e.g., voice command) or other high level interactions (e.g., dialogue). The originality of the work is also to include sounds of daily living as indicators to distinguish distress from normal situations. First development gave acceptable results for the sound recognition (72% correct classification) and we are working on the reduction of missed-alarm rate to improve performance in the near future. Although the current system suffers a number of limitations and that we raised numerous challenges that need to be addressed, the pair AUDITHIS and RAPHAEL is, to the best of our knowledge, one of the first serious attempts to build a real-time system that consider sound and speech analysis for ambient assisted living. This work also includes several evalu- ations on data acquired from volunteers in a real health smart home condition. Further work will include refinement of the acoustic models to adapt the speech recognition to the aged population as well as connexion to home automation systems.

Sound and speech detection and classification in a Health Smart Home

Improvements in medicine increase life expectancy in the world and create a new bottleneck at the entrance of specialized and equipped institutions. To allow elderly people to stay at home, researchers work on ways to monitor them in their own environment, with non-invasive sensors. To meet this goal, smart homes, equipped with lots of sensors, deliver information on the activities of the person and can help detect distress situations. In this paper, we present a global speech and sound recognition system that can be set-up in a flat. We placed eight microphones in the Health Smart Home of Grenoble (a real living flat of 47m2) and we automatically analyze and sort out the different sounds recorded in the flat and the speech uttered (to detect normal or distress french sentences). We introduce the methods for the sound and speech recognition, the post-processing of the data and finally the experimental results obtained in real conditions in the flat.

Making Context Aware Decision from Uncertain Information in a Smart Home: A Markov Logic Network Approach

This research addresses the issue of building home automation systems reactive to voice for improved comfort and autonomy at home. The focus of this paper is on the context-aware decision process which uses a dedicated Markov Logic Network approach to benefit from the formal logical representation of domain knowledge as well as the ability to handle uncertain facts inferred from real sensor data. The approach has been experiemented in a real smart home with naive and users with special needs.

Supervised classification of activities of daily living in health smart homes using SVM

By 2050, about a third of the French population will be over 65. To face this modification of the population, the current studies of our laboratory focus on the monitoring of elderly people at home. This aims at detect, as early as possible, a loss of autonomy by objectivizing criterions such as the international ADL or the French AGGIR scales implementing automatic classification of the different Activities of Daily Living. A Health Smart Home is used to achieve this goal. This flat includes different sensors. The data from the various sensors were used to classify each temporal frame into one of the activities of daily living that has been previously learnt (seven activities: hygiene, toilets, eating, resting, sleeping, communication and dressing/undressing). This is done using Support Vector Machines. We performed an experimentation with 13 young and healthy subjects to learn the model of activities and then we tested the classification algorithm (cross-validation) on real data.

Evaluation of a Context-Aware Voice Interface for Ambient Assisted Living: Qualitative User Study vs. Quantitative System Evaluation

This article presents an experiment with seniors and people with visual impairment in a voice-controlled smart home using the Sweet-Home system. The experiment shows some weaknesses in automatic speech recognition that must be addressed, as well as the need for better adaptation to the user and the environment. Users were disturbed by the rigid structure of the grammar and were eager to adapt it to their own preferences. Surprisingly, while no humanoid aspect was introduced in the system, the senior participants were inclined to embody the system. Despite these aspects to improve, the system has been favorably assessed as diminishing most participant fears related to the loss of autonomy.

Challenges in the processing of audio channels for Ambient Assisted Living

One of the greatest challenges in Ambient Assisted Living is to design health smart homes that could be able to anticipate the needs of its inhabitant while maintaining their comfort and their safety with an adaptation of the house environment and a facilitation of the connections to the outside world. The most likely to benefit from these smart homes are people in loss of autonomy such as the disabled people or the elderly with cognitive deficiencies. But it becomes essential to ease the interactions with the smart home through dedicated interfaces, in particular, thanks to systems reactive to vocal orders. Audio recognition is also a promising way to ensure more safety by contributing to detection of distress situations. This paper presents the stakes and the challenges of this domain based on some experiments carried out concerning distress call recognition and sound classification at home.