ULTra-MoCap - Oliver Fritsche (21009857) (2025)
Dataset files related to the paper ULTRA-MoCap: A Multimodal IMU and sEMG Dataset for Deep Learning-Based Upper Body Joint Kinematic Authors:Oliver Fritsche, Steven Camacho, Md Sanzid Bin Hossain, Tyler Halpenny, Carlos Archniegas, Joseph Dranetz, Dexter Hadley, Zhishan Guo, and Hwan Choi Abstract: Predicting human kinematics from multi-modal sensor data has applications in rehabilitation, sports analysis, assistive device control, and human-computer interaction, offering less intrusive alternatives to camera-based motion capture (MoCap) systems. However, current datasets often lack comprehensive multi-modal data integrating both IMU and sEMG sensors, with limited coverage for upper limb kinematics and insufficient capture of high-fidelity muscle activation and joint dynamics, especially for complex shoulder and elbow movements. These limitations impede the development of robust models for advanced applications. To address these gaps, we introduce the Upper Limb Tracking with Multi-modal Capture (ULTra-MoCap) dataset. It includes IMUs on the hand, wrist, and forearm, and combined sEMG/IMU sensors on the biceps brachii, triceps brachii, and deltoid, enabling detailed tracking of multiple degrees of freedom upper limb kinematics. Data was collected from 13 healthy subjects using Vicon Vero motion capture cameras and Delsys Trigno sEMG/IMU sensors with various upper limb movements. This paper details the data collection, sensor placement, and processing pipeline, establishing ULTRA-MoCap as a benchmark for deep learning applications. ________________________________________________________ ULTra-MoCap-processed contains already processed data reading for downstream deep learning. ULTra-MoCap-raw contains subjects [1-6](0) and [7-13](1) raw data for reproducibility. _________________________________________________________________