Link to Code: https://github.com/opearl-cmu/CapacitiveSensingKinematics

The included codebase illustrates how to use capacitive sensing data within two different
wearable kinematics algorithms, CSInverseKinematics and CSOptimalControl. It shows how to load raw CS signals, process them, analyze them, learn from them, and predict kinematics with them on their own or in combination with other wearables.

Link to Dataset: https://github.com/opearl-cmu/CapacitiveSensingDataset

The following dataset comprises data from two experiments. The first dataset includes time-synchronized measurements of (1) muscle bulging acquired via a wearble lower limb capacitive sensing sleeve at the shank, (2) neural excitation measurements from electromyography, and (3) inferred muscle moments from static optimization performed in OpenSim with optical motion capture and instrumented treadmill data. 20 participants were recorded walking normally and with a 5-degree toe-in foot progression angle, a therapeutic modification used to mitigate progression of knee osteoarthritis. Measurements for CS and EMG were taken both inside a traditional motion capture laboratory environment and outside in natural environments.

The second dataset includes measurements of (1) muscle bulging acquired via wearable lower limb capacitive sensing sleeves located at both the shank and thigh of both legs, (2) neural excitation measurements from electromyography, (3) optical motion capture and instrumented treadmill data, (4) XSens inertial measurement unit data, and (5) magnetic resonance imaging (MRI) body composition scan results. 10 healthy participants were recorded walking normally and with a mock impaired stiff-knee gait, along with 1 total knee arthroplasty patient. Measurements for CS, IMUs, and mocap were taking simultaneously, as well as measurements of EMG, IMUs, and mocap inside of the lab on an instrumented treadmill. The provided dataset enables the comparison of CS data with any biomarker in a consistent OpenSim/MATLAB ready formatting.

Please cite the following when using this code or data:
Owen Pearl, Nataliya Rokhmanova, Louis Dankovich, Summer Faille, Sarah Bergbreiter, Eni Halilaj. (2022) Capacitive Sensing for Natural Environment Rehabilitation Monitoring, Nature (under review). https://doi.org/10.21203/rs.3.rs-1902381/v.