A comparative home activity monitoring study using visual and inertial sensors

• Published in: 2015 17th International Conference on E-health Networking, Application & Services (HealthCom) pp. 644 - 647
• Main Authors: Tao, L., Burghardt, T., Hannuna, S., Camplani, M., Paiement, A., Damen, D., Mirmehdi, M., Craddock, I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2015
• Subjects: Accelerometers; Cameras; Feature extraction; Image color analysis; Monitoring; Sensors; Visualization
• DOI: 10.1109/HealthCom.2015.7454583

Monitoring actions at home can provide essential information for rehabilitation management. This paper presents a comparative study and a dataset for the fully automated, sample-accurate recognition of common home actions in the living room environment using commercial-grade, inexpensive inertial and visual sensors. We investigate the practical home-use of body-worn mobile phone inertial sensors together with an Asus Xmotion RGB-Depth camera to achieve monitoring of daily living scenarios. To test this setup against realistic data, we introduce the challenging SPHERE-H130 action dataset containing 130 sequences of 13 household actions recorded in a home environment. We report automatic recognition results at maximal temporal resolution, which indicate that a vision-based approach outperforms accelerometer provided by two phone-based inertial sensors by an average of 14.85% accuracy for home actions. Further, we report improved accuracy of a vision-based approach over accelerometry on particularly challenging actions as well as when generalising across subjects.