Article | REF: MED4200 V1

Distress situations detection. Embedded and environmental sensors

Authors: Jean-Louis BALDINGER, Jérôme BOUDY, Yannick FOUQUET, Dan ISTRATE

Publication date: December 10, 2015

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ABSTRACT

Automatic detection of distress (fall, faintness, etc.) in elderly people at home or in institutions (nursing homes, retirement homes, etc.) is an important issue. It requires different types of sensors, some wearable, and information integration and data analysis systems. The first analysis is a gait analysis to compute a balance quality index that could indicate a risk factor for falls in the future. A second analysis is an evaluation of different types of commercial sensors through the BiVACS methodology. A distress detection sensor is then presented that uses the analysis of the sound environment through everyday life sounds.

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AUTHORS

  • Jean-Louis BALDINGER: Associate Professor EPH Department, Télécom SudParis (Institut Mines-Télécoms), Evry, France

  • Jérôme BOUDY: Professor, EPH Department, Télécom SudParis (Institut Mines-Télécoms), Evry, France

  • Yannick FOUQUET: TASDA Project Manager, Grenoble, France

  • Dan ISTRATE: Contract teacher-researcher UTC Laboratoire BMBI, Compiègne, France

 INTRODUCTION

In industrialized countries, a third of people aged 65 and over living at home fall every year. These falls are major causes of morbidity and mortality. In fact, falls are the main cause of death in the over-65s, accounting for twelve to thirteen thousand deaths a year. The number of deaths increases every year as the population ages.

According to specialists, one third of people over 65 and one in two people over 85 living at home suffer a fall in the course of a year. For elderly people living in institutions, the proportion is even higher.

Statistics provided by the French Institut de veille sanitaire place this cause of death ahead of road accidents. In fact, 30% of people over 65 fall at least once a year, and 50% of those over 80. Falls are one of the main causes of death in France and throughout the world.

The main factors that increase the risk of a fall are various types of illness (neurological, muscular, sensory or psychological) and the environment (clothing, furniture, carpets, unsuitable lighting, etc.).

The consequences of a fall can include fractures (hip, ankle, femoral neck, etc.) and loss of independence. The fall itself is the cause of further falls, with the risk multiplied by 20 .

In addition to prevention workshops to help people maintain their balance, France has some 472,000 telealarm users, according to AFRATA, nearly half of whom don't use them because they forget, "don't want to disturb me", fear the stigma, or because "I didn't choose it"... and half of all calls are "involuntary". For several years now, automatic fall detection systems have been appearing in response to these oversights or inabilities to press an alert button. While they may seem useful at first glance, we still need to better define their targets and limitations.

The issues with automatic systems are, on the one hand, their sensitivity and specificity in relation to the many different types of fall, and on the other, their ease of use. The fact that certain sensors, such as video cameras or microphones, are more or less intrusive into people's lives plays an important role in their acceptance. We can identify three types of system:

  • portable systems generally based on accelerometers ;

  • systems based on environmental sensors ;

  • mixed systems merging information.

In this article, we present these three types of systems through :

...

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KEYWORDS

accelerometer   |   gyroscope   |   microphone   |   medical remote monitoring   |   fall detection   |   Gait analysis   |   pattern recognition   |   gait assessment   |   acceleration parameters extraction


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