ULTRA-MoCap: A Multimodal IMU and sEMG Dataset for Upper Body Joint Kinematics Analysis

Dataset files related to the paper ULTRA-MoCap: A Multimodal IMU and sEMG Dataset for Upper Body Joint Kinematics Analysis<b>Authors</b>:Oliver Fritsche, Steven Camacho, Md Sanzid Bin Hossain, Tyler Halpenny, Carlos Archniegas, Joseph Dranetz, Dexter Hadley, Zhishan Guo, and Hwan Choi<b>Abstract: </b>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._________________________________________________________________

与论文《ULTRA-MoCap：用于上肢关节运动学分析的多模态惯性测量单元（Inertial Measurement Unit，IMU）与表面肌电（surface electromyography，sEMG）数据集》相关的数据集文件
**作者**：Oliver Fritsche、Steven Camacho、Md Sanzid Bin Hossain、Tyler Halpenny、Carlos Archniegas、Joseph Dranetz、Dexter Hadley、Zhishan Guo以及Hwan Choi
**摘要：** 从多模态传感器数据中预测人体运动学，在康复医学、运动分析、辅助设备控制以及人机交互领域均有应用价值，可作为基于摄像头的运动捕捉（Motion Capture，MoCap）系统的低侵入性替代方案。然而，现有数据集往往缺乏同时集成惯性测量单元（IMU）与表面肌电（sEMG）传感器的完整多模态数据，且对上肢运动学的覆盖范围有限，难以精准捕捉高保真的肌肉激活与关节动力学特征，针对复杂肩部与肘部动作的采集尤为不足。这些缺陷制约了面向高级应用场景的高性能模型研发。为填补上述研究空白，本研究推出多模态捕捉上肢追踪数据集（Upper Limb Tracking with Multi-modal Capture，ULTra-MoCap，简称ULTRA-MoCap）。该数据集在手部、腕部与前臂部署了惯性测量单元（IMU），并在肱二头肌、肱三头肌与三角肌处集成了表面肌电/惯性测量单元（sEMG/IMU）复合传感器，可实现多自由度上肢运动学的精细化追踪。研究团队依托Vicon Vero运动捕捉摄像头与Delsys Trigno表面肌电/惯性测量单元（sEMG/IMU）传感器，从13名健康受试者身上采集了各类上肢动作数据。本文详细阐述了该数据集的数据采集流程、传感器部署方案与数据处理管线，旨在将ULTRA-MoCap打造为深度学习应用领域的基准数据集。
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**ULTra-MoCap-processed**：包含已预处理完成的数据，可直接用于下游深度学习任务。
**ULTra-MoCap-raw**：包含受试者[1-6]（分组0）与[7-13]（分组1）的原始数据，以保障研究可复现性。
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