Portable, non-invasive fall risk assessment in end stage renal disease patients on hemodialysis
WH '10 Wireless Health 2010
Application-Focused Energy-Fidelity Scalability for Wireless Motion-Based Health Assessment
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on CAPA'09, Special Section on WHS'09, and Special Section VCPSS' 09
Review: Wireless sensor networks for rehabilitation applications: Challenges and opportunities
Journal of Network and Computer Applications
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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.