A walk detection method using discrete wavelet transform (DWT) decomposition of kinematic sensor data achieved 78.5% sensitivity and 67.6% specificity in elderly people.
Observational (n=20)
Which algorithm is most efficient for walk detection using a chest-placed kinematic sensor in elderly people?
A kinematic sensor using DWT decomposition provides the most efficient walk detection in elderly people.
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)
Barralon et al. (Tue,) conducted a observational in Elderly people living independently (n=20). Walk detection algorithms (DWT, CWT, Fourier) using a kinematic sensor vs. Comparison among six algorithms was evaluated on Walk detection performance (sensitivity and specificity). A walk detection method using discrete wavelet transform (DWT) decomposition of kinematic sensor data achieved 78.5% sensitivity and 67.6% specificity in elderly people.
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