System and method for determining the activity of a person lying down

摘要

The system for determining the activity of a person lying down has at least one motion sensor (CM) having at least one measurement axis, which is provided with a fastener (MF) for firmly connecting the motion sensor (CM) to a user. The system includes a filter (FILT) for selecting, for at least one measurement axis of the motion sensor (CM), a high-frequency signal (HF) and a low-frequency signal (BF); a first calculation unit (CALC1) for calculating a first variable (x(n)) representing a temporal variation of the low-frequency signal (BF); a second calculation means (CALC2) for calculating a second variable (y(n)) comprising the Euclidean norm, along at least one measurement axis, of the high-frequency signal (HF); and an analysis unit (AN) that determines an activity of prone user as a function of time using a hidden Markov model having N states corresponding to N activities respectively. The analysis unit (AN) also combines conjoint probability density functions of the first and second variables, said probability density functions being defined for each state of the model in question; and probabilities of transitions between two successive states.

主权项

1. A system for determining activity of a person lying down, comprising at least two processing pathways of signals at an output of at least one motion sensor substantially affixed to said person, wherein a first of said at least two processing pathways processes a first component comprising signals of low frequencies and a second processing pathway processes a second component of signals of high frequencies, said system further comprising:
one or more processors; a first calculation module configured to calculate, using said one or more processors, a first variable representing a temporal variation of said first component, for at least an axis of said motion sensor; a second calculation module configured to calculate, using said one or more processors, a second variable comprising a Euclidean norm, along at least one measurement axis, of said second component; and an analysis module configured to determine, using said one or more processors, an activity of said person as a function of time using a hidden Markov model having N states corresponding to N activities respectively, said analysis module being configured for combining: conjoint probability density functions of said first and second variables, said probability density functions being defined for each state of the model; and probabilities of transitions between at least two successive states.