HLM-MOCAP: A Motion Capture Dataset of Context-Dependent Human Arm Motion for Human-Like Robot Motion Generation

HLM-MOCAP is a motion capture dataset of human upper-limb movements recorded to support data-driven generation of human-like trajectories for collaborative robots in assembly-like scenarios. Forty healthy adults performed a fixed set of seven single-arm tasks at a standardized table-top workstation. The task set includes point-to-point reaches to multiple targets, sequential reaching along a predefined path, planar tracing of zigzag and circular contours, grasp-and-place movements around an obstacle with and without arm crossing, transport of a weighted cylinder, and a precision placement task involving small screws. The dominant arm was tracked using an eight-camera Vicon MX T10 infrared motion capture system operating at 200 Hz with retro-reflective markers attached to the finger, wrist, elbow, and shoulder. Recordings were exported from Vicon software and organized into trial-wise CSV files. The repository provides raw and minimally processed trajectories, time-normalized position data, derived velocity and acceleration profiles, and aggregated averages per task and participant group. In addition, diagnostic plots and Python scripts are included to reproduce the preprocessing pipeline (gap filling, trimming, trimming of non-movement phases, time normalization, and smoothing) and to visualize the data. The dataset is suitable for research on human-like robot motion generation, learning from demonstration, human arm movement analysis, and benchmarking of trajectory generation methods in human–robot collaboration contexts.HLM-MOCAP是一款人类上肢运动动作捕捉（motion capture）数据集，旨在为类装配场景下协作机器人的数据驱动类人轨迹生成提供支撑。40名健康成年人在标准化台式工作站上完成了固定的7项单臂任务集。该任务集包含：指向多个目标的点对点伸手动作、沿预设路径的连续伸手动作、平面之字形与圆形轮廓描画动作、穿越与不穿越手臂情况下绕过障碍物的抓取放置动作、带负载圆柱的搬运动作，以及涉及小型螺钉的精密放置任务。 受试优势侧上肢通过8相机Vicon MX T10红外动作捕捉系统进行追踪，该系统运行帧率为200Hz，采集时在受试者手指、手腕、肘部及肩部粘贴逆向反射标记点（retro-reflective marker）。原始采集数据从Vicon软件导出，并整理为逐试次（trial）的CSV文件。 本数据集仓库提供原始轨迹与轻度处理后的轨迹、时间归一化位置数据、衍生的速度与加速度曲线，以及按任务与受试者群体汇总的平均数据。此外，仓库还附带诊断绘图脚本与Python代码，可复现预处理流水线（preprocessing pipeline）——包括间隙填充、序列裁剪、非运动阶段剔除、时间归一化与平滑处理——并实现数据可视化。本数据集适用于类人机器人运动生成、演示学习、人类上肢运动分析，以及人机协作场景下轨迹生成方法的基准测试等相关研究。