Wearable-Gait-Analysis-Based Activity Recognition: A Review

Stella Ansah; Diliang Chen

doi:10.2478/ijssis-2022-0021

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Wearable-Gait-Analysis-Based Activity Recognition: A Review

International Journal on Smart Sensing and Intelligent Systems

Volume 15 (2022): Issue 1 (January 2022)

By: Stella Ansah and Diliang Chen

Open Access

|Jan 2023

Abstract

Gait analysis has been recognized as an efficient method to help realize human activity recognition; however, there is currently no existing review study focused on wearable activity recognition methods that employ gait analysis in the recognition process. In this study, different wearable-gait-analysis-based (WGA-based) activity recognition methods were summarized and compared from the aspects of wearable sensor types, data segmentation, feature extraction, and classification methods. The limitations of the current research and potential opportunities for future research in this field are also discussed.

References

high-precision-pressure-sensor-height-sensor-module. https://robu.in/product/gy-63ms5611-01ba03-high-precision-pressure-sensor-height-sensor-module/.
Search in Google Scholar Back to article
“Similar gait action recognition using an inertial sensor”, Pattern Recognition, 48(4):1289–1301, 2015.
Search in Google Scholar Back to article
R. Abdel-Salam, R. Mostafa, and M. Hadhood, “Human activity recognition using wearable sensors: Review, challenges, evaluation benchmark”, ArXiv, abs/2101.01665, 2021.
Search in Google Scholar Back to article
J. K. Aggarwal and L. Xia, “Human activity recognition from 3d data: A review”, Pattern Recognition Letters, 48 (Celebrating the life and work of Maria Petrou.):70–80, 2014.
Search in Google Scholar Back to article
N. Ahmad, R. Ariffin Bin Raja Ghazilla, N. Mohd Khairi, and V. Kasi, “Reviews on various inertial measurement unit (imu) sensor applications”, SiPS 2013, 2013.
Search in Google Scholar Back to article
O. C. Ann and L. B. Theng, “Human activity recognition: A review”, 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), pp. 389–393, 2014.
Search in Google Scholar Back to article
D. J. Beddiar, B. Nini, M. Sabokrou, and A. Hadid, “Vision-based human activity recognition: A survey”, Multimedia Tools Appl., 79(41–42):30509–30555, Nov. 2020.
Search in Google Scholar Back to article
L. C. Benson, C. A. Clermont, S. T. Osis, D. Kobsar, and R. Ferber, “Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods”, Journal of Biomechanics, 71:94–99, 2018.
Search in Google Scholar Back to article
A. Bux, P. Angelov, and Z. Habib, “Vision based human activity recognition: A review”, in Plamen Angelov, Alexander Gegov, Chrisina Jayne, and Qiang Shen, editors, Advances in Computational Intelligence Systems, pp. 341–371, Cham, 2017.
Search in Google Scholar Back to article
J. Camargo, W. Flanagan, N. Csomay-Shanklin, B. Kanwar, and A. Young, “A machine learning strategy for locomotion classification and parameter estimation using fusion of wearable sensors”, IEEE Transactions on Biomedical Engineering, 68(5):1569–1578, 2021.
Search in Google Scholar Back to article
D. Castro, W. Coral, C. Rodriguez, J. Cabra, and J. Colorado, “Wearable-based human activity recognition using an iot approach”, Journal of Sensor and Actuator Networks, 6(4), 2017.
Search in Google Scholar Back to article
D. Chen, G. Asaeikheybari, H. Chen, W. Xu, and M.-C. Huang, “Ubiquitous fall hazard identification with smart insole”, IEEE journal of biomedical and health informatics, 2020.
Search in Google Scholar Back to article
D. Chen, Y. Cai, J. Cui, J. Chen, H. Jiang, and M.-C. Huang, “Risk factors identification and visualization or work-related musculoskeletal disorders with wearable and connected gait analytics system and Kinect skeleton models”, SmartHealth, 7:60–77, 2018.
Search in Google Scholar Back to article
D. Chen, Y. Cai, X. Qian, R. Ansari, W. Xu, K.-C. Chu, and M.-C. Huang, “Bring gait lab to everyday life: Gait analysis in terms of activities of daily living”, IEEE Internet of Things Journal, 7(2):1298–1312, 2020.
Search in Google Scholar Back to article
D. Chen, H. Cao, H. Chen, Z. Zhu, X. Qian, W. Xu, and M.-C. Huang, “Smart insole-based indoor localization system for internet of things applications”, IEEE Internet of Things Journal, 6(4):7253–7265, 2019.
Search in Google Scholar Back to article
D. Chen, J. Chen, H. Jiang, and M.-C. Huang, “Risk factors identification for work-related musculoskeletal disorders with wearable and connected gait analytics system”, in 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 330–339, IEEE, 2017.
Search in Google Scholar Back to article
L. Chen and C. D. Nugent, Sensor-Based Activity Recognition Review, Springer International Publishing, Cham, pp. 23–47, 2019.
Search in Google Scholar Back to article
T. Chu, A. Chua, and E. Secco, “A wearable myo gesture armband controlling sphero bb-8 robot”, HighTech and Innovation Journal, 1, 10, 2020.
Search in Google Scholar Back to article
S. Eisa and A. Moreira, “A behaviour monitoring system (bms) for ambient assisted living”, Sensors, 17(9), 2017.
Search in Google Scholar Back to article
G. Ershadi, M. Gwak, A. Aminian, R. Soangra, M. GrantBeuttler, and M. Sarrafzadeh, “Smart insole: Remote gait detection algorithm using pressure sensors for toe walking rehabilitation”, in 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), pp. 332–337, 2021.
Search in Google Scholar Back to article
A. Gupta, K. Gupta, K. Gupta, and K. Gupta, “A survey on human activity recognition and classification”, in 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 0915–0919, 2020.
Search in Google Scholar Back to article
M. M. Hamdi, M. I. Awad, M. M. Abdelhameed, and F. A. Tolbah, “Lower limb gait activity recognition using inertial measurement units for rehabilitation robotics”, in 2015 International Conference on Advanced Robotics (ICAR), pp. 316–322, 2015.
Search in Google Scholar Back to article
G.-M. Jeong, P. H. Truong, and S.-I. Choi, “Classification of three types of walking activities regarding stairs using plantar pressure sensors”, IEEE Sensors Journal, 17(9):2638–2639, 2017.
Search in Google Scholar Back to article
E. Kantoch, “Human activity recognition for physical rehabilitation using wearable sensors fusion and artificial neural networks”, in 2017 Computing in Cardiology (CinC), pp. 1–4, 2017.
Search in Google Scholar Back to article
O. D. Lara and M. A. Labrador, “A survey on human activity recognition using wearable sensors”, IEEE Communications Surveys Tutorials, 15(3):1192–1209, 2013.
Search in Google Scholar Back to article
R. Liu, A. A. Ramli, H. Zhang, E. Datta, and X. Liu, “An overview of human activity recognition using wearable sensors: Healthcare and artificial intelligence”, CoRR, abs/2103.15990, 2021.
Search in Google Scholar Back to article
X. Liu and Q. Wang, “Incrementally classifying different walking activities based on wearable sensors”, in 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 699–704, 2021.
Search in Google Scholar Back to article
Y. Liu, J. Huang, G. Ding, and Z. Yang, “High-performance and wearable strain sensors based on graphene microfluidics and serpentine microchannels for human motion detection”, Microelectronic Engineering, 231:111402, 2020.
Search in Google Scholar Back to article
I. H. López-Nava, M. Garcia-Constantino, and J. Favela, “Recognition of gait activities using acceleration data from a smartphone and a wearable device”, in UCAmI, 2019.
Search in Google Scholar Back to article
I. H. López-Nava, A. Muñoz-Meléndez, A. I. Pérez Sanpablo, A. Alessi Montero, I. Quiñones Urióstegui, and L. Núñez Carrera, “Estimation of temporal gait parameters using bayesian models on acceleration signals”, Computer Methods in Biomechanics and Biomedical Engineering, 19(4):396–403, 2016. PMID: 25876180
Search in Google Scholar Back to article
C. F. Martindale, V. Christlein, P. Klumpp, and B. M. Eskofier, “Wearables-based multi-task gait and activity segmentation using recurrent neural networks”, Neurocomputing, 432:250–261, 2021.
Search in Google Scholar Back to article
U. Martinez-Hernandez and A. A. Dehghani-Sanij, “Adaptive bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors”, Neural Networks, 102:107–119, 2018.
Search in Google Scholar Back to article
U. Martinez-Hernandez, I. Mahmood, and A. A. Dehghani-Sanij, “Simultaneous bayesian recognition of locomotion and gait phases with wearable sensors”, IEEE Sensors Journal, 18(3):1282–1290, 2018.
Search in Google Scholar Back to article
O. Mazumder, A. S. Kundu, P. K. Lenka, and S. Bhaumik, “Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton”, Gait Posture, 50:53–59, 2016.
Search in Google Scholar Back to article
G. McCalmont, P. Morrow, H. Zheng, A. Samara, S. Yasaei, H. Wang, and S. McClean, “ezigait: Toward an ai gait analysis and assistant system”, in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2280–2286, 2018.
Search in Google Scholar Back to article
L. M. Dang, K. Min, H. Wang, Md. JalilPiran, C. Lee, and H. Moon, “Sensor-based and vision-based human activity recognition: A comprehensive survey”, Pattern Recognition, 108:107561, 2020.
Search in Google Scholar Back to article
L. M. Dang, K. Min, H. Wang, Md. JalilPiran, C. Lee, and H. Moon, “Sensor-based and vision-based human activity recognition: A comprehensive survey”, Pattern Recognition, 108:107561, 2020.
Search in Google Scholar Back to article
C. M. el Achkar, C. Lenoble-Hoskovec, A. Paraschiv-Ionescu, K. Major, C. Büla, and K. Aminian, “Physical behavior in older persons during daily life: Insights from instrumented shoes”, Sensors, 16:1225, August 2016.
Search in Google Scholar Back to article
C. M. el Achkar, C. Lenoble-Hoskovec, A. Paraschiv-Ionescu, K. Major, C. Büla, and K. Aminian, “Instrumented shoes for activity classification in the elderly”, Gait Posture, 44:12–17, 2016.
Search in Google Scholar Back to article
S. C. Mukhopadhyay, “Wearable sensors for human activity monitoring: A review”, IEEE Sensors Journal, 15(3):1321–1330, 2015.
Search in Google Scholar Back to article
S. C. Mukhopadhyay and T. Islam, “Wearable sensors; applications, design and implementation”, 2017.
Search in Google Scholar Back to article
Y. Ng, X. Jiang, Y. Zhang, S. Shin, and R. Ning, “Automated activity recognition with gait positions using machine learning algorithms”, Engineering, Technology Applied Science Research, 9:4554–4560, August 2019.
Search in Google Scholar Back to article
T. F. Novacheck, “The biomechanics of running”, Gait & posture, 7(1):77–95, 1998.
Search in Google Scholar Back to article
C. I. Nwakanma, F. B. Islam, M. P. Maharani, J.-M. Lee, and D.-S. Kim, “Detection and classification of human activity for emergency response in smart factory shop floor”, Applied Sciences, 11(8), 2021.
Search in Google Scholar Back to article
M. N. Orlin and T. G McPoil, “Plantar Pressure Assessment”, Physical Therapy, 80(4):399–409, April 2000.
Search in Google Scholar Back to article
S. Paraschiakos, R. Cachucho, M. Moed, D. van Heemst, S. Mooijaart, E. Slagboom, A. Knobbe, and M. Beekman, “Activity recognition using wearable sensors for tracking the elderly”, User Modeling and User-Adapted Interaction, July 2020.
Search in Google Scholar Back to article
X. Qian, H. Cheng, D. Chen, Q. Liu, H. Chen, H. Jiang, and M.-C. Huang, “The smart insole: A pilot study of fall detection”, in EAI International Conference on Body Area Networks, pp. 37–49, 2019.
Search in Google Scholar Back to article
J. Rafferty, C. D. Nugent, J. Liu, and L. Chen, “From activity recognition to intention recognition for assisted living within smart homes”, IEEE Transactions on Human-Machine Systems, 47(3):368–379, 2017.
Search in Google Scholar Back to article
E. Ramanujam, T. Perumal, and S. Padmavathi, “Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review”, IEEE Sensors Journal, 21(12):13029–13040, 2021.
Search in Google Scholar Back to article
S. Ranasinghe, F. Al Machot, and H. C. Mayr, “A review on applications of activity recognition systems with regard to performance and evaluation”, International Journal of Distributed Sensor Networks, 12(8):1550147716665520, 2016.
Search in Google Scholar Back to article
R. Riener, M. Rabuffetti, and C. Frigo, “Stair ascent and descent at different inclinations”, Gait & posture, 15(1):32–44, 2002.
Search in Google Scholar Back to article
D. Rodríguez-Martín, A. Samà, C. Pérez-López, A. Català, and J. Cabestany, “Posture transition analysis with barometers: contribution to accelerometer based algorithms”, Neural Computing and Applications, 32:335–349, 2018.
Search in Google Scholar Back to article
A. Sarabu and A. Santra, “Human action recognition in videos using convolution long short-term memory network with spatio-temporal networks”, Emerging Science Journal, 5:25–33, February 2021.
Search in Google Scholar Back to article
A. B. Sargana, P. Angelov, and Z. Habib, Vision Based Human Activity Recognition: A Review, vol. 513, pp. 341–371. January 2017.
Search in Google Scholar Back to article
S. Sharif, I. Murray, and G. Lee, “Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3d motion capture system”, Biomedical Engineering Letters, 8, May 2018.
Search in Google Scholar Back to article
N. K. Suryadevara and S. C. Mukhopadhyay, “Assistive Technology for the Elderly”, Academic Press, 2020.
Search in Google Scholar Back to article
W. Tao, T. Liu, R. Zheng, and H. Feng, “Gait analysis using wearable sensors”, Sensors, 12(2):2255–2283, 2012.
Search in Google Scholar Back to article
Tina, A. K. Sharma, S. Tomar, and K. Gupta, “Various approaches of human activity recognition: A review”, In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1668–1676, 2021.
Search in Google Scholar Back to article
P. H. Truong, S. You, S.-H. Ji, and G.-M. Jeong, “Adaptive accumulation of plantar pressure for ambulatory activity recognition and pedestrian identification”, Sensors, 21:3842, June 2021.
Search in Google Scholar Back to article
M. Vrigkas, C. Nikou, and I. A. Kakadiaris, “A review of human activity recognition methods”, Frontiers in Robotics and AI, 2, 2015.
Search in Google Scholar Back to article
C. Wang, J. Z. Zhang, Z. Wang, and J. Wang, “Position-independent activity recognition model for smartphone based on frequency domain algorithm”, in Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, pp. 396–399, 2013.
Search in Google Scholar Back to article
S. K. Yadav, K. Tiwari, H. M. Pandey, and S. Ali Akbar, “A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions”, Knowledge-Based Systems, 223:106970, 2021.
Search in Google Scholar Back to article
S. Yang, C. Li, X. Chen, Y. Zhao, H. Zhang, N. Wen, Z. Fan, and L. Pan, “Facile fabrication of high-performance pen ink-decorated textile strain sensors for human motion detection”, ACS Applied Materials & Interfaces, 12(17):19874–19881, 2020. PMID: 32253911
Search in Google Scholar Back to article
S. Zhang, Y. Li, S. Zhang, F. Shahabi, S. Xia, Y. Deng, and N. Alshurafa, “Deep learning in human activity recognition withwearable sensors: A review on advances” Sensors, 22(4), Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland, February 2022.
Search in Google Scholar Back to article
S. Zhang, Z. Wei, J. Nie, L. Huang, S. Wang, and Z. Li, “A review on human activity recognition using vision-based method”, Journal of Healthcare Engineering, 2017:1–31, July 2017.
Search in Google Scholar Back to article
Y. Zhao, J. Wang, Y. Zhang, H. Liu, Z. Chen, Y. Lu, Y. Dai, L. Xu, and S. Gao, “Flexible and wearable emg and psd sensors enabled locomotion mode recognition for ioht-based in-home rehabilitation”, IEEE Sensors Journal, 21(23):26311–26319, 2021.
Search in Google Scholar Back to article
J. Zheng, H. Cao, D. Chen, R. Ansari, K.-C. Chu, and M.-C. Huang, “Designing deep reinforcement learning systems for musculoskeletal modeling and locomotion analysis using wearable sensor feedback”, IEEE Sensors Journal, 20(16):9274–9282, 2020.
Search in Google Scholar Back to article
Z. Zhuang and Y. Xue, “Sport-related human activity detection and recognition using a smartwatch”, Sensors, 19(22):5001, Nov. 2019.
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.2478/ijssis-2022-0021 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Mar 31, 2022

Published on: Jan 4, 2023

Published by: Macquarie University, Australia

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

human activity recognition,

gait analysis,

wearable sensors,

data segmentation,

knowledge-driven feature extraction

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2023 Stella Ansah, Diliang Chen, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 15 (2022): Issue 1 (January 2022)