MoCaPose
Bo Zhou, Daniel Geißler, Marc Faulhaber, Clara Gleiß, Esther Friederike Zahn, Lala Shakti Swarup Ray, David Fosca Gamarra, Vítor Fortes Rey, Sungho Suh, Sizhen Bian, Gesche Joost, Paul Lukowicz
Abstract
We present MoCaPose, a novel wearable motion capturing (MoCap) approach to continuously track the wearer's upper body's dynamic poses through multi-channel capacitive sensing integrated in fashionable, loose-fitting jackets. Unlike conventional wearable IMU MoCap based on inverse dynamics, MoCaPose decouples the sensor position from the pose system. MoCaPose uses a deep regressor to continuously predict the 3D upper body joints coordinates from 16-channel textile capacitive sensors, unbound by specific applications. The concept is implemented through two prototyping iterations to first solve the technical challenges, then establish the textile integration through fashion-technology co-design towards a design-centric smart garment. A 38-hour dataset of synchronized video and capacitive data from 21 participants was recorded for validation. The motion tracking result was validated on multiple levels from statistics (R2 ~ 0.91) and motion tracking metrics (MP JPE ~ 86mm) to the usability in pose and motion recognition (0.9 F1 for 10-class classification with unsupervised class discovery). The design guidelines impose few technical constraints, allowing the wearable system to be design-centric and usecase-specific. Overall, MoCaPose demonstrates that textile-based capacitive sensing with its unique advantages, can be a promising alternative for wearable motion tracking and other relevant wearable motion recognition applications.