Estimation of Indoor Physical Activity Level Based on Footstep Vibration Signal Measured by MEMS Accelerometer for Personal Health Care Under Smart Home Environments

A smart home environment based on pervasive networkedsensors enables us to measure and analyze various vital signals related to personal health care. For example, the vital signals on footstep, gait pattern, and posture can be used for assessing the health state among the elderly and disabled people. In this manuscript, we use footstep vibration signals measured by network-based MEMS accelerometers attached on the bottom of wall for estimating the level of indoor physical activity that elevating the activity level is conducive to maintaining better health state of the elderly and disable people. The footstep vibration signal measured by MEMS accelerometer includes vital sign induced by human activities such as walking, sitting, sleeping, cooking and personal hygiene. With the purpose of developing a smart-home-based healthcare system for the elderly and disabled people, this paper deals with on estimation of energy expenditure level in human body, extraction of statistical parameters of daily living pattern, and identification of pathological gait pattern. Different from image sensor and sound sensor, MEMS accelerometer sensor can measure some biomedical signatures without entailing personal privacy problem.