Neural Network Gait Classification for On-Body Inertial Sensors

TitleNeural Network Gait Classification for On-Body Inertial Sensors
Publication TypeConference Paper
Year of Publication2009
AuthorsHanson MA, Powell, Jr HC, Barth AT, Lach J, Brandt-Pearce M
Conference Name2009 International Conference on Body Sensor Networks
PublisherIEEE Computer Society
Keywordsangular rate, artificial neural network, biomedical telemetry, body area networks, body area sensor network, body area sensor network platform, brain, cerebellar model arithmetic computers, cerebellar model articulation controller, feature extraction, gait analysis, gait classification, gait classification technique, linear acceleration, medical signal processing, neural network, neurophysiology, on-body inertial sensor, signal classification, signal processing, wavelet pre-processing, wavelet transforms, wireless transmission

Clinicians have determined that continuous ambulatory monitoring provides significant preventative and diagnostic benefit, especially to the aged population. In this paper we describe gait classification techniques based on data obtained using a new body area sensor network platform named TEMPO 3. The platform and its supporting infrastructure enable six-degrees-of-freedom inertial sensing, signal processing, and wireless transmission. The proposed signal processing includes data normalization to improve robustness, feature extraction optimized for classification, and wavelet pre-processing. The effectiveness of the platform is validated by implementing a binary classifier between shuffle and normal gait. Artificial neural networks and classifiers based on the Cerebellar Model Articulation Controller were tested and yielded classification accuracies (68%-98%) comparable to previous efforts that required more restrictive or intrusive apparatus. These results suggest a viable path to resource-constrained, on-body gait classification.