Human Motion Data in Complex Construction Environments Setup with and without Knee Exoskeleton Assistance

Zhu, Chunchu;Huang, Xinyan;Yi, Jingang

Description

Construction work involves complex, non-repetitive tasks that increase risks of work-related musculoskeletal disorders (WMSDs) and productivity loss. This study presents an integrated evaluation framework combining wearable knee exoskeleton assistance, immersive virtual reality (VR) task scenarios, and synchronized multi-sensor measurements to quantify safety- and performance-related outcomes in construction-like settings. Using a modular stair–ramp–deck platform, twenty-one adults completed multi-task protocols with and without knee exoskeleton assistance in a randomized crossover design while the motion capture system, force plates, electromyography (EMG), inertial measurement units (IMUs), and metabolic sensing recorded biomechanics, stability, and effort. Assistance reduced lower-limb muscular demand and improved postural stability during kneeling and VR precision tasks. User surveys indicated perceived stability gains but highlighted comfort and weight concerns. The framework enables task-relevant, reproducible assessment of safety and productivity in realistic construction environments. All multimodal data and protocols are released to support benchmarking and future task-aware assistance systems.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.4

FAIR Score

69%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Mendeley Data

License

Creative Commons Attribution 4.0 International

Assigned Domain

Subfield

Social Psychology

Field

Psychology

Domain

Social Sciences

Confidence Score

51%

Source

Scholar Data Model

Keywords

Human-in-the-Loop

Normalization Factors

FT

63.46

CTw

1.00

MTw

1.00