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 47m(2)) 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.
Fleury
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A
Laboratory TIMCIMAG, UMR CNRS/UJF 5525, team AFIRM. Faculté de Médecine de Grenoble, bâtiment Jean Roget, 38706 La Tronche, France. Anthony.Fleury@imag.fr
Noury
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N
Vacher
M
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Glasson
H
H
Seri
J F
JF
D000203:Activities of Daily Living; D000465:Algorithms; D001185:Artificial Intelligence; D003201:Computers; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004779:Environment Design; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D018670:Monitoring, Ambulatory / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D013016:Sound; D013018:Sound Spectrography / Q000379:methods*; D049250:Speech Recognition Software; D017216:Telemedicine / Q000295:instrumentation / Q000379:methods*; D014584:User-Computer Interface