The way that we move has long been recognized as a window into human health; our gait is affected by numerous pathologies, including arthritis, diabetes, and Parkinson's and Alzheimer's diseases. Having access to quantitative gait analysis could be an important tool for pathology prevention, diagnosis, and management. However to date, quantitative gait analysis has been largely confined to the laboratory, due to the cumbersome and expensive nature of multi-camera motion tracking lab equipment. This project uses novel sensors that employ materials that emit a voltage when subjected to mechanical stress and has the objective of bringing mobile gait analysis to the general public, in their real-world environment. These wearable sensors are inexpensive, consume little power, and can wirelessly communicate with a nearby smartphone. In addition, the sensor technology itself promises to be transformational across a broad cross-section of applications including clothing-integrated wearable sensors, self-sensing furniture, smart mounts for vibrating equipment, advanced airbag deployment systems, integrated helmet concussion sensors and self-sensing shoes. Students from both mechanical engineering and exercise sciences are intimately involved in all aspects of the research to provide a unique cross-disciplinary educational environment. Furthermore, high school students can apply to receive gait analysis systems to solve interesting science and technology problems.
The piezoresponsive nano-composite sensors used in this project are comprised of a dielectric polymer matrix impregnated with a network of conductive nano-particles. The mechanical and electrical responses of the sensors are independently tailored to provide an optimized system for multifunctional implementation in numerous systems requiring large-deflection sensing capabilities. When connected to a voltage-sensing device (for example, a blue-tooth transmitting Arduino microcomputer) the sensors can track deformation and impact energy and relay it to a nearby smartphone. In order to effectively leverage these sensors for mobile gait analysis, several scientific barriers need to be overcome. The piezoresponse of the sensors will be modeled using a machine-learning algorithm to enable accurate, continuous self-calibration of the sensors. An optimized mesh network of sensors will be designed to allow mobile gait analysis and will be validated against traditional gait lab instruments. Finally, the optimized sensors will be utilized to measure gait in a cross-section of subjects to establish a public baseline database of gait behavior in a variety of real-life environments (i.e., outside of the laboratory).
