Indoor Human Localization and Gait Analysis using Machine Learning for In-home Health Monitoring

Homes equipped with ambient sensors can measure physiological signals correlated with the resident's health without requiring a wearable device. Gait characteristics may reveal physical imbalances or recognize changes in cognitive health. In this paper, we use the physical interactions with floor to both localize the resident and monitor their gait. Accelerometers are placed at the corners of the room for sensing. Gradient boosting regression was used to perform localization with an accuracy of 82%, reasonably accounting for inhomogeneity in the floor with just 3 sensors. A method using step time variance is proposed to detect gait imbalances; results on induced limps arc presented.