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arXiv Machine Learning By Ihsan Ul Haq (Centre for Computational Science and Mathematical Modelling, Coventry University, United Kingdom, School of Architecture and Built Environment Deakin University, Australia), James Brusey (Centre for Computational Science and Mathematical Modelling, Coventry University, United Kingdom), Farnad Nasirzadeh (School of Architecture and Built Environment Deakin University, Australia), Chandan Karmakar (School of Information Technology Deakin University, Australia)

Towards Field-Deployable Wearable IMU Systems for Occupational Activity Recognition

Read the original on arXiv Machine Learning →

The study evaluates wearable inertial measurement unit (IMU) systems for construction worker activity recognition using the VTT‑ConIoT benchmark. It systematically analyzes six classifiers, five sensor placements, and three window sizes under a leave‑one‑subject‑out protocol, revealing per‑subject accuracy ranging from 61.9 % to 88.7 % (mean 77.5 %). Findings show that multi‑sensor fusion and longer windows improve accuracy, standard augmentation offers modest gains, CTGAN augmentation degrades performance, and personalized calibration can recover up to 16.8 pp for the weakest participants.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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