TY - GEN
T1 - Body sensor networks for human activity recognition
AU - CHETTY, Girija
AU - White, Matthew
PY - 2016
Y1 - 2016
N2 - Body Sensor Networks aim to capture the state of the user and its environment by utilizing from information heterogeneous sensors, and allow continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in activity recognition for identity verification, health and ageing and sport and exercise monitoring applications. In this paper, the application of body sensor networks for automatic and intelligent daily activity monitoring for elderly people, using wireless body sensors and smartphone inertial sensors has been reported. The scheme uses information theory-based feature ranking algorithms and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments using different publicly available datasets of human activity show that the proposed approach can assist in the development of intelligent and automatic real time human activity monitoring technology for eHealth application scenarios for elderly, disabled and people with special needs.
AB - Body Sensor Networks aim to capture the state of the user and its environment by utilizing from information heterogeneous sensors, and allow continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in activity recognition for identity verification, health and ageing and sport and exercise monitoring applications. In this paper, the application of body sensor networks for automatic and intelligent daily activity monitoring for elderly people, using wireless body sensors and smartphone inertial sensors has been reported. The scheme uses information theory-based feature ranking algorithms and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments using different publicly available datasets of human activity show that the proposed approach can assist in the development of intelligent and automatic real time human activity monitoring technology for eHealth application scenarios for elderly, disabled and people with special needs.
KW - body-sensor-networks
KW - activity-recognition
KW - data-mining
UR - http://www.scopus.com/inward/record.url?scp=84991669655&partnerID=8YFLogxK
U2 - 10.1109/SPIN.2016.7566779
DO - 10.1109/SPIN.2016.7566779
M3 - Conference contribution
SN - 9781467391979
T3 - 3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016
SP - 660
EP - 665
BT - Proceedings Signal Processing and Information Networks (SPIN 2016)
A2 - Dutta, M. K.
A2 - Banerjee, J. K. Rai P.
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States of America
T2 - 3rd International Conference on Signal Processing and Information Networks (SPIN 2016)
Y2 - 11 February 2016 through 12 February 2016
ER -