Wavelet based approach for posture transition estimation using a waist worn accelerometer.

The ability to rise from a chair is considered to be important to achieve functional independence and quality of life. This sit-to-stand task is also a good indicator to assess condition of patients with chronic diseases. We developed a wavelet based algorithm for detecting and calculating the durations of sit-to-stand and stand-to-sit transitions from the signal vector magnitude of the measured acceleration signal. The algorithm was tested on waist worn accelerometer data collected from young subjects as well as geriatric patients. The test demonstrates that both transitions can be detected by using wavelet transformation applied to signal magnitude vector. Wavelet analysis produces an estimate of the transition pattern that can be used to calculate the transition duration that further gives clinically significant information on the patients condition. The method can be applied in a real life ambulatory monitoring system for assessing the condition of a patient living at home.