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Title: Analysis and detection of human motion in time-frequency domain
Keywords: Wavelet transform, Discrete dyadic wavelet transform, Time-frequency analysis, Activities of daily living, falls, elderly;
Issue Date: 2-Apr-2007
Citation: NYAN MYO NAING (2007-04-02). Analysis and detection of human motion in time-frequency domain. ScholarBank@NUS Repository.
Abstract: The emphasis of this study is to develop a wearable fall and ADL detection system that can detect a broad range of ADL using relatively fewer sensors for the comfort of the user in long term application. To provide long term comfort for the wearer, we use a garment as a wearable platform. A triaxial accelerometer is attached at the shoulder position of the garment. ADL detected in our studies are vital daily activities such as sitting, standing, lying down, lying to sitting, level walking, ascending stairs and descending stairs. A new method of time-frequency based ADL detection using two acceleration signals, vertical acceleration signal and antero-posterior acceleration signal, is proposed. High sensitivity (94.98 per cent) and specificity (98.83 per cent) were achieved in detection of 1495 activities conducted by six subjects. A novel fall notification system that can summon medical assistances via SMS (Short Messaging Service) was also developed.
Appears in Collections:Ph.D Theses (Open)

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