Citation

Lim, Zheng You and Raja Sekaran, Sarmela and Pang, Ying Han and Ooi, Shih Yin and Wang, Lillian Yee Kiaw (2025) Light-PTNet: A lightweight parallel temporal network for smartphone-based human motion classification. PLOS One, 20 (9). e0331135. ISSN 1932-6203

Text article_id=10.1371%2Fjournal.pone.0331135 - Published Version Restricted to Repository staff only Download (261kB)

Abstract

The increased popularity of smartphone-based human activity recognition (HAR) in recent decades has been driven by its low computational requirements and user privacy protection. Yet, developing a reliable smartphone-based HAR still presents several challenges. For example, handcrafted feature-based approaches highly depend on laborious feature engineering/selection techniques that require human intervention. Implementing conventional Convolutional Neural Networks may result in unsatisfactory performance in time series classification as they cannot effectively extract time-dependent features. Although recurrent models excel at extracting temporal information, they require extensive computational resources to attain high performance, limiting their practicality for real-time applications. Thus, we propose a lightweight smartphone-based HAR architecture called Lightweight Parallel Temporal Network (Light-PTNet) for reliable classification. Light-PTNet comprises parallelly organised Light Spatial-Temporal Heads (LSTC Heads) that capture underlying patterns at various scales of the inertial signals. These heads utilise dilations and residual connections to preserve longer-term dependencies without increasing the model parameters. This work assesses the proposed Light-PTNet’s performance on open-access HAR datasets: UCI HAR, WISDM V1, and UniMiB SHAR, following a user-independent protocol. The results reveal that our proposed Light-PTNet achieves 98.03% accuracy on UCI HAR, 81.58% on UniMiB SHAR and 97.02% on WISDM V1 with fewer model parameters (lower than 0.1 million parameters). © 2025 Raja Sekaran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Item Type:	Article
Uncontrolled Keywords:	Algorithms, Humans, Neural Networks, Computer, Smartphone
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	04 Nov 2025 08:46
Last Modified:	07 Nov 2025 04:07
URII:	http://shdl.mmu.edu.my/id/eprint/14692

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