Conception matérielle et logicielle d’un kit de capteurs facilement déployable pour enrichir un environnement en le rendant capable d’intelligence ambiante

Depuis plusieurs décennies, l’espérance de vie moyenne mondiale n’a jamais cessé d’augmenter. Le dernier rapport de l’ONU a notamment indiqué que la quantité de personnes âgées a considérablement augmenté. On peut, par exemple, observer que la proportion de personnes de 65 ans et plus est devenue plus grande que celle des enfants de moins de 5 ans.Cette tendance ne semblant pas prête à se freiner, il est nécessaire de trouver un moyen de s’occuper de ces aînés et des différents problèmes de santé y étant reliés. Comme ces individus désirent plus que tout rester dans leur domicile, il va alors falloir considérer la mise en place d’une aide à domicile, qui est soit onéreuse, soit exigeante pour la famille. Le domaine de l’informatique a énormément progressé depuis les dernières années, notamment avec l’évolution de la microélectronique. En effet, les dispositifs sont de plus en plus performants, avec un prix, une taille et une consommation énergétique qui diminuent considérablement. Ces changements ont rendu possible la création de l’Intelligence Ambiante (IAm), dont une des applications principales est l’habitat intelligent. Celui-ci permet de surveiller les activités réalisées par l’habitant dans le but de l’aider. Pourtant, ce type d’installation est encore très peu utilisé, surtout à cause de son prix souvent élevé et des connaissances nécessaires à sa mise en place. Ce sont des défis auxquels plusieurs chercheurs ont décidé de répondre avec la création de kits de capteurs pour transformer un habitat commun en un habitat intelligent. Cette thèse va apporter des réponses au problème de coût des habitats intelligents en proposant de nouveaux capteurs répondant à des problématiques précises dans un environnement. De plus, nous proposons aussi une structure logicielle capable de prendre en charge beaucoup de protocoles de communication différents afin d’amoindrir considérablement la problématique d’adaptabilité des habitats intelligents existants. Enfin, nous proposons une méthode pour gérer plusieurs résidents sous le même toit, pour mieux associer la personne qui éxecute une activité dans l’habitat. Ces objectifs ont été atteints et ont permis de publier trois contributions majeures : « Highly accurate bathroom activity recognition using infrared proximity sensors », « Real-time gait speed evaluation at home in a multi-residents context » et « A new device to track and identify people in a multi-residents context »

Méthode hybride pour la reconnaissance d’activités de la vie quotidienne et d’exercices physiques en temps réel

Dans le monde de la réadaptation, un souhait commun est d’utiliser un programme de plusieurs exercices physiques pour diminuer les troubles reliés à une maladie musculaire. C’est en particulier vrai pour les patients concernés par les maladies neuromusculaires. Ce programme d’exercices à réaliser chez soi est envisagé en moyen terme par les thérapeutes, étant donné que les patients atteints de maladies neuromusculaires ont de grandes contraintes de déplacement. Cependant, le thérapeute n’a aucune preuve que le patient réalise ses exercices chez lui. Pour se faire, la technologie peut reconnaître des activités, et notamment les exercices physiques. De nouveaux capteurs voient le jour de moins en moins chers et de plus en plus performants. Ces capteurs sont entre autres des accéléromètres, des gyroscopes, des magnétomètres. Ils sont très puissants et très utilisés pour confectionner des montres, des téléphones intelligents, des bracelets et des ceintures. Pour permettre de reconnaître les exercices physiques prescrits par le thérapeute, et pour pouvoir quantifier le niveau d’exercice global d’un patient, une solution algorithmique a été développée à l’aide de matériel existant. Cette solution est composée de plusieurs périphériques communiquant entre eux par les moyens de protocoles sans fil. Les résultats obtenus par cette méthode de reconnaissance hybride sont excellents. De plus, malgré qu’elle ait été développée pour une population atteinte de dystrophie myotonique de type 1, elle peut être utilisée potentiellement pour différents types de patients

A new device to track and identify people in a multi-residents context

In recent years, technologies for monitoring people inside a house lead to the development of smart home. However, the vast majority of works deals only in monitoring the activities of a single inhabitant. Nevertheless, most of the people in the current context of ageing population does not live alone. Recognizing the activities performed by each inhabitant in a house is an important challenge. A first step to achieve this is to be able to distinguish where each inhabitant is in the house. In this paper, we present a new device to track and identify people in a multi-residents context. Experiments have been conducted to validate the reliability and accuracy of the proposed device

Monitoring changes in physical activity data during strength training of people with myotonic dystrophy type 1

Myotonic dytrophy type 1 (DM1) is an incurable neuromuscular disease and muscle weakness is a prominent symptom. Research has shown that strength training can be an interesting solution to help with this symptom. Therefore an assistive technology aiming at supervising strength training at home for people with DM1 has been developed and tested in the home of 10 patients for 10 weeks. As many change point detection (CPD) techniques have been used for monitoring change in activity data in the past, no one applied these techniques to physical activities of people with DM1 disease. Hence, physical activity data have been collected during the 10-week experiment and state-of-the-art CPD algorithm has been used to analyze changes in physical activity during the strength training program at home. The results prove that many challenges need to be addressed in this context and could act as a guideline for future works

Real-time gait speed evaluation at home in a multi residents context

In recent years, the aging of the population has attracted considerable attention in the scientific community. An important fact is an increasing number of senior people will suffer from cognitive decline and there is almost no existing way to detect it early without the intervention of a clinician. Indeed, the sooner the cognitive decline is detected, the professional can elaborate a more adequate strategy to slow it down. In fact, Mild Cognitive Impairments (MCI) have been strongly correlated to a decreasing gait speed over the time. However, it would take a lot of human resources to carry out a standardized walking speed test every year to follow the evolution of this one. In fact, it is unthinkable in the current context of healthcare economics scarcity, thus finding a way of measuring it automatically at home could be a promising solution. This ambient sensor should be able to measure the gait speed of an inhabitant and automatically associate it to the right resident in a multi-resident context. In this paper, we present a new prototype to monitor gait speed continuously at home non intrusively. When coupled with a wristband capable of communicating through BLE, the gait speed can then be associated with the right person in a multi-resident context. The proposed prototype was tested in a realistic smart home context and results obtained are very encouraging

Real-time gait speed evaluation at home

In the last decades, aging population has raised substantial awareness by the scientific communities. An important part of these populations experience cognitive decline, without any method to threat it. However, the gait speed has been revealed as a great predictor of Mild Cognitive Impairments, making its monitoring very interesting for early detection. Since it would require a lot of human resources to do it at this scale, an ambient sensor would be a perfect solution for this task. It would be necessary to be able to associate the gait speed evaluated with the user who walked at this speed. This paper deals with our new conceptualized prototype to monitor gait speed in a smart home and automatically associate it with the right person. The method induced promising results

Transportable and scalable system for activities and exercises recognition in real-time

Patients concerned with neuromuscular disorders are usually given a program of multiple physical exercises to do. These exercises must be recognized by devices to ensure the therapist that the patient did it well. To this end, we developed a specific method allowing the recognition of two types of activities on different window sizes. The method consists of two devices communicating over Wi-Fi, to use both wearable components and powerful embedded computer. Data is collected during fixed windows by the first device. Then features are calculated on the retrieved data for the recognition algorithm. Participants took part in an experiment based on four physical exercises and six daily activities. The method induced good results

A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition

Many people in the world are affected by muscle wasting, especially the population hits by myotonic dystrophy type 1 (DM1). Those people are usually given a program of multiple physical exercises to do. While DM1 and many other people have difficulties attending commercial centers to realize their program, a solution is to develop such a program completable at home. To this end, we developed a portable system that patients could bring home. This prototype is an improved version of the previous one using Wi-Fi, as this new prototype runs on BLE technology. This new prototype conceptualized induces great results

LIPSHOK: LIARA Portable Smart Home Kit

Several smart home architecture implementations have been proposed in the last decade. These architectures are mostly deployed in laboratories or inside real habitations built for research purposes to enable the use of ambient intelligence using a wide variety of sensors, actuators and machine learning algorithms. However, the major issues for most related smart home architectures are their price, proprietary hardware requirements and the need for highly specialized personnel to deploy such systems. To tackle these challenges, lighter forms of smart home architectures known as smart homes in a box (SHiB) have been proposed. While SHiB remain an encouraging first step towards lightweight yet affordable solutions, they still suffer from few drawbacks. Indeed, some of these kits lack hardware support for some technologies, and others do not include enough sensors and actuators to cover most smart homes’ requirements. Thus, this paper introduces the LIARA Portable Smart Home Kit (LIPSHOK). It has been designed to provide an affordable SHiB solution that anyone is able to install in an existing home. Moreover, LIPSHOK is a generic kit that includes a total of four specialized sensor modules that were introduced independently, as our laboratory has been working on their development over the last few years. This paper first provides a summary of each of these modules and their respective benefits within a smart home context. Then, it mainly focus on the introduction of the LIPSHOK architecture that provides a framework to unify the use of the proposed sensors thanks to a common modular infrastructure capable of managing heterogeneous technologies. Finally, we compare our work to the existing SHiB kit solutions and outline that it offers a more affordable, extensible and scalable solution whose resources are distributed under an open-source license

Effects and acceptability of an individualized home-based 10-week training program in adults with myotonic dystrophy Type 1

Background : Muscle weakness is a cardinal sign of myotonic dystrophy type 1, causing important functional mobility limitations and increasing the risk of falling. As a non-pharmacological, accessible and safe treatment for this population, strength training is an intervention of choice. Objective : To document the effects and acceptability of an individualized semi-supervised home-based exercise program on functional mobility, balance and lower limb strength, and to determine if an assistive training device has a significant impact on outcomes. Methods : This study used a pre-post test design and men with the adult form of DM1 were randomly assigned to the control or device group. The training program was performed three times a week for 10 weeks and included three exercises (sit-to-stand, squat, and alternated lunges). Outcome measures included maximal isometric muscle strength, 10-Meter Walk Test, Mini-BESTest, 30-Second Chair Stand Test and 6-minute walk test. Results : No outcome measures showed a significant difference, except for the strength of the knee flexors muscle group between the two assessments. All participants improved beyond the standard error of measurement in at least two outcome measures. The program and the device were well accepted and all participants reported many perceived improvements at the end of the program. Conclusions : Our results provide encouraging data on the effects and acceptability of a home-based training program for men with the adult form of DM1. These programs would reduce the financial burden on the health system while improving the clinical services offered to this population