research-article

MFE-HAR: multiscale feature engineering for human activity recognition using wearable sensors

Authors:

Wanlei ZhouAuthors Info & Claims

MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 180 - 189

https://doi.org/10.1145/3360774.3360787

Published: 03 February 2020 Publication History

Abstract

Human activity recognition plays a key role in the application areas such as fitness tracking, healthcare and aged care support. However, inaccurate recognition results may cause an adverse effect on users or even an unpredictable accident. In order to improve the accuracy of human activity recognition, multi-device and deep learning based approaches have been proposed. However, they are not practical on a daily basis due to the limitations that devices are difficult to wear, and deep learning requires large training dataset and incurs expensive computational costs. To address this problem, we propose a novel approach, multiscale feature engineering for human activity recognition (MFE-HAR), which exploits the properties of arm movement from global and local scales using the accelerometer and gyroscope sensors on a single wearable device. Our method takes advantage of having important features at multiple scales over previous single-scale methods. We evaluated the performance of the proposed method on two public datasets and achieved the mean classification accuracy of 93% and 98% respectively. Our proposed system performs better than the state of the art multi-device based approaches, and is more practical for real-world applications.

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Cited By

Ge JXu GLu JXu XLi LMeng X(2024)SensorNet: An Adaptive Attention Convolutional Neural Network for Sensor Feature LearningSensors10.3390/s2411327424:11(3274)Online publication date: 21-May-2024
https://doi.org/10.3390/s24113274
Nisar MShirahama KIrshad MHuang XGrzegorzek M(2023)A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable SensorsSensors10.3390/s2319823423:19(8234)Online publication date: 3-Oct-2023
https://doi.org/10.3390/s23198234
Yu HMo L(2021)Research on Feature Selection of Human Physical Activity Recognition for IOT Wearable Devices2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)10.1109/ICSMD53520.2021.9670841(1-5)Online publication date: 21-Oct-2021
https://doi.org/10.1109/ICSMD53520.2021.9670841
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MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2019

545 pages

ISBN:9781450372831

DOI:10.1145/3360774

General Chairs:
Vincent Poor
Princeton University
,
Zhu Han
University of Houston

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 February 2020

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MobiQuitous

MobiQuitous: Computing, Networking and Services

November 12 - 14, 2019

Texas, Houston, USA

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Cited By

Ge JXu GLu JXu XLi LMeng X(2024)SensorNet: An Adaptive Attention Convolutional Neural Network for Sensor Feature LearningSensors10.3390/s2411327424:11(3274)Online publication date: 21-May-2024
https://doi.org/10.3390/s24113274
Nisar MShirahama KIrshad MHuang XGrzegorzek M(2023)A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable SensorsSensors10.3390/s2319823423:19(8234)Online publication date: 3-Oct-2023
https://doi.org/10.3390/s23198234
Yu HMo L(2021)Research on Feature Selection of Human Physical Activity Recognition for IOT Wearable Devices2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)10.1109/ICSMD53520.2021.9670841(1-5)Online publication date: 21-Oct-2021
https://doi.org/10.1109/ICSMD53520.2021.9670841
Hussain ZWaterworth DAldeer MZhang WSheng QOrtiz J(2021)Do You Brush Your Teeth Properly? An Off-body Sensor-based Approach for Toothbrushing Monitoring2021 IEEE International Conference on Digital Health (ICDH)10.1109/ICDH52753.2021.00018(59-69)Online publication date: Sep-2021
https://doi.org/10.1109/ICDH52753.2021.00018
Hussain ZWaterworth DAldeer MZhang WSheng Q(2020)Toothbrushing data and analysis of its potential use in human activity recognition applicationsProceedings of the Third Workshop on Data: Acquisition To Analysis10.1145/3419016.3431489(31-34)Online publication date: 16-Nov-2020
https://dl.acm.org/doi/10.1145/3419016.3431489
Lu JZheng XSheng MJin JYu S(2020)Efficient Human Activity Recognition Using a Single Wearable SensorIEEE Internet of Things Journal10.1109/JIOT.2020.29959407:11(11137-11146)Online publication date: Nov-2020
https://doi.org/10.1109/JIOT.2020.2995940
Kongsil KSuksawatchon JSuksawatchon U(2020)Wrist-worn Physical Activity Recognition: A Fusion Learning Approach2020 - 5th International Conference on Information Technology (InCIT)10.1109/InCIT50588.2020.9310980(116-121)Online publication date: 21-Oct-2020
https://doi.org/10.1109/InCIT50588.2020.9310980
Dehkordi MZaraki ASetchi R(2020)Feature extraction and feature selection in smartphone-based activity recognitionProcedia Computer Science10.1016/j.procs.2020.09.301176(2655-2664)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.09.301

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