Towards Field-Deployable Wearable IMU Systems for Occupational Activity Recognition

Ihsan Ul Haq; James Brusey; Farnad Nasirzadeh; Chandan Karmakar

cs.LGOct 22, 2026

Towards Field-Deployable Wearable IMU Systems for Occupational Activity Recognition

Authors: Ihsan Ul Haq, James Brusey, Farnad Nasirzadeh, Chandan Karmakar

Organizations: Centre for Computational Science and Mathematical Modelling, Coventry University, United Kingdom · School of Architecture and Built Environment Deakin University, Australia · School of Information Technology Deakin University, Australia

Abstract

Wearable inertial measurement unit (IMU) systems for construction worker human activity recognition (HAR) routinely report aggregate accuracies above 95 %, rarely under genuine subject-independent (leave-one-subject-out (LOSO)) evaluation, masking the per-individual variance that determines real-world deployment performance. Using the VTT-ConIoT benchmark, we conduct, to our knowledge, the first systematic configuration sensitivity analysis of this dataset: six classifiers across five sensor placements and three window sizes under a single LOSO protocol, a personalised-calibration pilot, and a validated comparison of tabular GAN-based and standard time-series augmentation. Per-subject LOSO accuracy for the main multi-sensor, 16-class configuration ranges from 61.9 % to 88.7 % (mean 77.5 %), a 27 pp spread, though all data are laboratory-collected. Multi-sensor fusion and longer windows both improve accuracy; standard augmentation gives small, consistent gains, while Conditional Tabular GAN (CTGAN) augmentation actively harms accuracy with a demonstrated distributional-fidelity mechanism; and personalised calibration recovers 16.8 pp for the weakest participants from just 30 % of their own data. These findings provide evidence-grounded configuration guidance for scaling occupational body sensor networks towards field deployment

Figures & tables

Fig. 1: Per-subject LOSO accuracy, ranked lowest to highest, main configuration (SVM, all sensors,

5\text{\,}\mathrm{s}

); dashed line marks an illustrative

75\text{\,}\mathrm{\%}

grouping aid, not a deployment threshold.

Clf.	All	Back	Hand	Trouser	Trsr-Accel
SVM	77.5	67.9	66.4	53.1	41.8
ET	76.6	66.8	64.9	52.1	37.4
RF	76.4	67.1	65.1	55.0	38.2
XGB	75.9	67.0	66.6	54.3	38.4
LDA	73.3	59.8	60.2	49.4	42.7
LR	70.8	63.3	62.8	53.3	46.9

TABLE I: LOSO Mean Accuracy (

\mathrm{\%}

) over 13 folds, by Sensor Configuration and Classifier (

5\text{\,}\mathrm{s}

window; std

6\text{\,}\mathrm{pp}11\text{\,}\mathrm{pp}

)

Fig. 2: LOSO accuracy vs. window length (all sensors), one line per classifier; SVM (bold) is the main-text classifier.

Clf.	Base	+StdAug	$\Delta$	+CTGAN	$\Delta$
RF	73.3	73.5	+0.2	72.1	-2.0
ET	73.0	73.6	+0.6	73.6	-0.1
LDA	69.7	69.5	-0.1	63.6	-5.5
LR	66.1	66.8	+0.7	64.5	-1.4
SVM	73.3	74.1	+0.7	72.4	-1.4

TABLE II: Augmentation Delta (6-of-13-fold LOSO subset, users 8,5,1,9,2,6,

5\text{\,}\mathrm{s}

window)

Human Activity Recognition

Human activity recognition classifies actions and behaviors from wearable sensors, video, radio signals, and other measurements.

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Sep 15, 2026J/K move · Enter open · S save

Coverage-Aware Virtual IMU Augmentation for Low-Resource Human Activity Recognition

Jiayuan Gao, Yingwei Zhang, Ziyao Tang, Yuejia Ma+3

Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Nanyang Technological University, Singapore+2

Human Activity Recognition Synthetic Data Augmentation Data Augmentation Wearable Sensing

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IMU-based human activity recognition (HAR) enables continuous, privacy-friendly monitoring of daily activities using wearable sensors. However, building reliable HAR models that generalize across diverse users and real-world conditions requires large amounts of labeled IMU data, which are expensive and difficult to collect. Existing approaches mainly rely on augmentation or synthesis to expand available data, but indiscriminately adding virtual samples may provide little new coverage and introduce unreliable supervision. To overcome these challenges, we propose a novel coverage-aware virtual IMU augmentation framework that decides where to supplement real data, how to generate and select virtual candidates, and how strongly to weight them during training. Specifically, we select diversity and scarcity anchors in a learned sensor embedding space, convert anchor dynamics into prompts, and generate virtual IMU candidates for each anchor. We then rank candidates by a selection cost combining anchor proximity and label consistency, and incorporate the selected candidates into HAR training with reliability-based weights. Experiments on public HAR benchmarks show that our method consistently improves recognition performance over competitive baselines, and ablation studies confirm the effectiveness of the proposed framework design.

Abstract

Human Activity Recognition

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