HLM-MOCAP is a motion capture dataset of context-dependent human arm movements designed to support the generation of human-like robot trajectories in industrial and collaborative settings. Data were recorded in a laboratory environment using an eight-camera infrared motion capture system (Vicon MX T10) operating at 200 Hz with 1120 × 896 px resolution and passive 1.5 mm retroreflective markers. Forty healthy adults (29 male, 11 female; broad age range from late adolescence to adulthood) performed a set of seven upper-limb tasks at a standardized workstation with marked start and target locations and physical objects of varying weight and size. The tasks comprise point-to-point transport to different distances and directions, sequential reaching to multiple targets, two trajectory-tracing tasks (zigzag and circle) with the index finger, a grasp-and-place task with arm crossing, a reaching task with load, and a high-precision placement task using small screws. Each participant completed 54 movement executions (with task-specific repetition counts), yielding 2,160 recorded movement trials in total. The dataset includes raw 3D marker trajectories for all trials, segmented to active movement phases based on an objective velocity threshold, and processed versions after gap filling of short occlusions, time normalization to 0–100% movement progress, and Savitzky–Golay filtering for derivation of stable velocity and acceleration profiles. Data are organized as comma-separated files with participant identifiers, task labels, target indices, object properties, and repetition indices, accompanied by scripts for loading and basic preprocessing. The resource can be reused for developing and benchmarking methods for human-like robot motion planning, learning from demonstration, and trajectory generation, as well as for analyzing how distance, sequence structure, load, and precision demands shape human arm kinematics in the context of human–robot collaboration.
Karbouj et al. (Fri,) studied this question.