Soft robotic sensors (SRSs) are a class of pliable, passive sensors which vary by some electrical characteristic in response to changes in geometry. The properties of SRSs make them excellent candidates for use in wearable motion analysis technology. Wearable technology is a fast-growing industry, and the improvement of existing human motion analysis tools is needed. Prior research has proven the viability of SRSs as a tool for capturing motion of the foot-ankle complex; this work covers extensive effort to improve and ruggedize a lab tool utilizing this technology. The improved lab tool is validated against a camera-based motion capture system to show either improvement or equivalence to the previous prototype while introducing enhanced data throughput, reliability, battery life, and durability.

The ankle joint complex is a common source of injury for various demographics and is often observed during gait analysis. I investigate using soft robotic sensors as a means for collecting kinematic data at the ankle joint complex. I validate the linearity of these sensors by measuring stretch against extension and against stretch from frontal and sagittal planar foot movements using a wooden ankle mockup. I then conduct a study involving ten participants who perform repetitive trials of four foot movements (plantarflexion, dorsiflexion, inversion and eversion) using ten different locations. Four optimal locations were identified for these movements based on linearity, accuracy, robustness, and consistency. Lastly, I validated soft robotic sensors against the human gait cycle. Twenty participants were recruited and performed twelve trials, walking across a flat surface and a cross-sloped surface while motion capture data and soft robotic sensor data was collected.