Characterization of a Multi-User Indoor Positioning System Based on Low Cost Depth Vision (Kinect) for Monitoring Human Activity in a Smart Home

An increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community

A Data Fusion System to Study Synchronization in Social Activities

As the world population gets older, the healthcare system must be adapted, among others by providing continuous health monitoring at home and in the city. The social activities have a significant role in everyone health status. Hence, this paper proposes a system to perform a data fusion of signals sampled on several subjects during social activities. This study implies the time synchronization of data coming from several sensors whether these are embedded on people or integrated in the environment. The data fusion is applied to several experiments including physical, cognitive and rest activities, with social aspects. The simultaneous and continuous analysis of four subjects cardiac activity and GPS coordinates provides a new way to distinguish different collaborative activities comparing the measurements between the subjects and along time.Comment: Healthcom 201

EFFECT OF THE PLACEMENT OF THE INERTIAL SENSOR ON THE HUMAN MOTION DETECTION

There are numerous possibilities of assessments of the human activity, offered by the ActimedARM -a wearable inertial sensor we developed. This device features a triaxial magnetometer, a trixial accelerometer, a micro-processing unit, a Zigbee module and a μSD card. Its embedded algorithms make it able to compute postures, transfers of the subject and also to characterize the walking episodes. We recently succeeded in computing the relative displacements of the sensors, from double integration of the acceleration signals, in order to qualify specific physical activities such as rising from chairs or stools. The experiments highlighted the impact of the location of the sensor on the body on the correlation between objective motion and signals processed from acceleration measurements, showing a better correlation coefficient of 11.41% when the sensor is located on the navel

Traitement de signaux issus de magnétomètres embarqués : Application à la détection des changements de direction d'une personne

Cet article présente une méthode de classification des changements de direction d'une personne à partir des signaux d'un magnétomètre tri-axes embarqué. L'algorithme proposé permet de détecter, rapidement et de manière causale, les changements d'orientation par analyse de fenêtres temporelles du signal. Après seuillage, une première analyse de la norme euclidienne permet de déterminer grossièrement l'amplitude et le sens du changement. Cette première décision est ensuite affinée par le calcul du quaternion de la rotation dans R3. Cette méthode permet de réduire la puissance de calcul nécessaire dans un système embarqué puisque le calcul du quaternion ne s'effectue que sur les fenêtres d'intérêt du signal

Evolution of Activities of Daily Living using Inertia Measurements: The Lunch and Dinner Activities

In the context of designing eHealth services for fragile people, we propose to monitor Activities of Daily Living (ADL) in order to anticipate the potential loss of autonomy by behaviour changes. Nowadays, the availability of non-stigmatising sensors such as inertial sensors embedded on Smartphones allows the estimation of people’s postures in real time in order to evaluate their autonomy in daily life. Our aim is to propose an unconstrained and non-intrusive method based on inertial sensors, which gives an indicator about a person’s autonomy. This method determines the correlation between people’s postures and activities over time in order to compute an index of ADL (IndexADL), specific to each person. The IndexADL variation over time is then a useful feature for positively or negatively evaluating people’s autonomy.  Our experiment, based on data collection of eight elderly people over a 3-month period, analyses the Lunch and Dinner activities with promising performances

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

Non

Design and Optimization of an Autonomous, Ambulatory Cardiac Event Monitor

International audienceWearable sensors for health monitoring can enable the early detection of various symptoms, and hence rapid remedial actions may be undertaken. In particular, the monitoring of cardiac events by using such wearable sensors can provide real-time and more relevant diagnosis of cardiac arrhythmias than classical solutions. However, such devices usually use batteries, which require regular recharging to ensure long-term measurements. We therefore designed and evaluated a connected sensor for the ambulatory monitoring of cardiac events, which can be used as an autonomous device without the need of a battery. Even when using off-the-shelf, low-cost integrated circuits, by optimizing both the hardware and software embedded in the device, we were able to reduce the energy consumption of the entire system to below 0.4 mW while measuring and storing the ECG on a non-volatile memory. Moreover, in this paper, a power-management circuit able to store energy collected from the radio communication interface is proposed, able to make the connected sensor fully autonomous. Initial results show that this sensor could be suitable for a truly continuous and long-term monitoring of cardiac events

Characterization of a multi-user indoor positioning system based on low cost depth vision (Kinect) for monitoring human activity in a smart home

International audienceAn increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community