Wearable and ambient sensor fusion for the characterisation of human motion

Home monitoring plays an important role within pervasive healthcare, particularly for monitoring the elderly and patients with chronic disease. For assessing activities of daily living, one of the most challenging problems for research remains that of accurate transition detection and characterisation. Early detection of a change in these transitions, such as difficulty getting up from a seated position, can be an indicator of further complications which often precede a fall. Such changes can also accompany early stage neurological disorders which can be treated effectively to improve quality of life. In this paper, we present a system for the accurate characterisation of motion based upon the fusion of ambient and wearable sensors. A probabilistic, privacy respectful method for the extraction of detailed 3D posture information is proposed and fusion with an ear-worn accelerometer and gyroscope is discussed. We present results detailing high accuracy in the recognition of complex motions over four subjects.