A novel sensor approach to advanced pedometry using hierarchical processing

Falls are a major cause of injuries in adults above the age of sixty-ve. The economic aftermath of falls and their consequent hospitalization can be extensive. A plausible way of mitigating this problem is accurate prediction of future falls and taking proactive remedial action. Problems in gait is a reliable indicator of a future fall, however, existing systems focus on gait analysis in clinical settings and are not tuned towards continuous gait analysis. This research presents the design of a novel textile capacitive sensor array-based system built into clothing that can reliably capture advanced pedometric parameters that can be used to determine gait attributes. The nal design utilizes hierarchical signal processing architecture that breaks down the signal processing algorithm into a hierarchy of processing elements. The system is prototyped using textile capacitive plates built into an elastic-bandage and a custom FPGA-based system and show that our system can accurately detect gait attributes that have high correlation with falls, while consuming minimal energy.