Physical Activity Recognition With Statistical-Deep Fusion Model Using Multiple Sensory Data for Smart Health

Huynh-The, Thien; Hua, Cam-Hao; Tu, Nguyen Anh; Kim, Dong-Seong

doi:10.1109/jiot.2020.3013272

Cited by 72Context-Aware Activity Recognition SystemsPhysical Activity and HealthGait Recognition and Analysis

Physical Activity Recognition With Statistical-Deep Fusion Model Using Multiple Sensory Data for Smart Health

Huynh-The, Thien, Hua, Cam-Hao, Tu, Nguyen Anh, Kim, Dong-Seong·2021·IEEE Internet of Things Journal

DOI 10.1109/jiot.2020.3013272

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Abstract

Nowadays, enhancing the living standard with smart healthcare via the Internet of Things is one of the most critical goals of smart cities, in which artificial intelligence plays as the core technology. Many smart services, deployed according to wearable sensor-based physical activity recognition, have been able to early detect unhealthy daily behaviors and further medical risks. Numerous approaches have studied shallow handcrafted features coupled with traditional machine learning (ML) techniques, which find it difficult to model real-world activities. In this work, by revealing deep features from deep convolutional neural networks (DCNNs) in fusion with conventional handcrafted features, we learn an intermediate fusion framework of human activity recognition (HAR). According to transforming the raw signal value to pixel intensity value, segmentation data acquired from a multisensor system are encoded to an activity image for deep model learning. Formulated by several novel residual triple convolutional blocks, the proposed DCNN allows extracting multiscale spatiotemporal signal-level and sensor-level correlations simultaneously from the activity image. In the fusion model, the hybrid feature merged from the handcrafted and deep features is learned by a multiclass support vector machine (SVM) classifier. Based on several experiments of performance evaluation, our fusion approach for activity recognition has achieved the accuracy over 96.0% on three public benchmark data sets, including Daily and Sport Activities, Daily Life Activities, and RealWorld. Furthermore, the method outperforms several state-of-the-art HAR approaches and demonstrates the superiority of the proposed intermediate fusion model in multisensor systems.

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One common way to improve robustness is to leverage the benefits of multiple types of sensors together to create multi-sensory systems.
“One common way to improve robustness is to leverage the benefits of multiple types of sensors together to create multi-sensory systems [249,310,311,312,313,314].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [311]

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Authors: Huynh-The, T., Hua, C.-H., Tu, N. A., & Kim, D.-S.
Affiliations: Kyung Hee University (KR)
Nazarbayev University (KZ)
Kumoh National Institute of Technology (KR)
ORCID: Huynh-The, Thien
Hua, Cam-Hao
Tu, Nguyen Anh
Kim, Dong-Seong
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-02-01
Volume: 8
Issue: 3
Pages: 1533-1543
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2020.3013272
OpenAlex: W3046507381
Cited by: 72
References: 45
Citations by year: 2026: 2 · 2025: 15 · 2024: 12 · 2023: 20 · 2022: 9 · 2021: 11 · 2020: 3
FWCI: 4.18
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Internet of Things Journal (publishedVersion)
Nazarbayev University Repository (Nazarbayev University) (submittedVersion)
Funders: National Research Foundation of Korea (NRF) through the Creativity Challenge Research-Based Project (2019R1I1A1A01063781)
Priority Research Centers Program through the NRF of Korea
Ministry of Education, Science, and Technology (2018R1A6A1A03024003)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Health Sciences › Medicine › Physiology › Physical Activity and Health
Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
Keywords: physical activity recognition, human activity recognition, sensor fusion, wearable sensors, spatiotemporal feature extraction, multi-class support vector machine, smart healthcare, Internet of Things

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Physical Activity Recognition With Statistical-Deep Fusion Model Using Multiple Sensory Data for Smart Health

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