title: Walk detection with a kinematic sensor: frequency and wavelet comparison.
abstract: This study is included in the framework of Health Smart Homes which monitor some physiological or not physiological parameters of elderly people living independently at home. In this study we will focus on the walk detection. Walk activity is one parameter to evaluate the health of patient. For example, the total time of walk during a day allows assessing quickly if the subject is mobile rather than immobile. To reach this goal we used a kinematic sensor placed on the chest recording the movements of the subject. The data are analyzed by six algorithms to detect walk phases: two based on Fourier analysis and the others using a wavelet decomposition (DWT and CWT). All algorithms are described and the performances are evaluated on real data recorded with 20 elderly people. Results show that the method using the DWT decomposition is the most efficient (78.5% in sensitivity and 67.6% in specificity).
authors.affiliation: Lab. TIMC-IMAG, CNRS, La Tronche, France. Pierre.barralon@imag.fr
authors.forename: Pierre
authors.initials: P
authors.lastname: Barralon
authors.forename: Nicolas
authors.initials: N
authors.lastname: Vuillerme
authors.forename: Norbert
authors.initials: N
authors.lastname: Noury
mesh_terms: D000054:Acceleration; D000368:Aged; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014159:Transducers; D016138:Walking / Q000502:physiology*