Monitoring and analyzing sit-to-stand movements performed by an older person when rising from their bed could be used as a measure of health and mobility. Pressure patterns for four adult test groups were collected using a pressure sensitive mat technology and analyzed using image processing and signal processing techniques.
Pressure images were generated from the raw sensor data and analyzed using four region of interest algorithms. The hip and hand regions were successfully extracted and the best results were obtained using the binary cluster detection algorithm. The sensor data was summed to create a sit-to-stand time pressure signal and an automated algorithm was proposed to segment the signal into three phases. The results extracted individual phase times as well as total sit-to-stand times. The algorithm performance was verified using video analysis data.
The timing results were used to evaluate the health and mobility of different participant groups and could be used to monitor changes in sit-to-stand movements performed by occupants living in smart homes.