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Announced dataset of raw 36-channel insole plantar-pressure recordings from 5 subjects lifting 2-10 kg loads, collected to train lifting-load regression models for adaptive control of industrial lifting exoskeletons (IEEE AIRC 2026; arXiv 2503.07527). The paper states data and code are public on GitHub, but the linked repository is still empty as of September 2026 - listed as coming soon until the deposit is populated.

Details

Subjects

n = 5

Setting

Laboratory (controlled or simulated)

Protocol / load

Static box lifting with 2-10 kg dumbbell loads in 0.5 kg steps, 3 sessions per subject

Equipment

Pair of 18-channel RX-ES42-18 insole pressure sensors (36 channels total), Bluetooth, 20 Hz

Sampling

pressure 20 Hz

License

TBD

Keywords

pressure-insoles, load-estimation, exoskeleton-control, lifting, machine-learning

Added to catalog

2026-09-01

Publications

• Wu K., Xiang P., Lin C., Bai O. (2026). Load Estimation for Industrial Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning. IEEE AIRC 2026Companion to the wu-insole-load-estimation-exo entry in the Models library.

Exoskeleton

Record type

Controller training data

Body region

Back, Shoulder

Study design

Data collected to develop load-estimation input for adaptive lifting-exoskeleton assistance control; no device worn during capture

This record is part of the exoskeleton collection.

Cite

Cite the dataset as its authors request (check the source page for an official citation); this BibTeX is a convenience starting point.

@misc{wu-insole-lifting-load-estimation,
  author = {Wu, K. and Xiang, P. and Lin, C. and Bai, O.},
  title  = {{Insole Plantar-Pressure Data for Lifting-Load Estimation (FIU)}},
  year   = {2026},
  url    = {https://github.com/Kaida-Wu/insole-pressure-load-estimation},
}