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Knowledge and Information Systems
Published in: Knowledge and Information Systems 10/2021

07-09-2021 | Regular Paper

HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Authors: Yu Luan, Yuhang Shi, Wanyin Wu, Zhiyao Liu, Hai Chang, Jun Cheng

Published in: Knowledge and Information Systems | Issue 10/2021

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Abstract

In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have witnessed the emergence of health care and exoskeleton robotics which make use of wearable suits, human–machine interaction based on action recognition performs an important role in multimedia applications. Considering the limitations of the application scenario, the surface electromyography (sEMG) signal stands out in many wearable data collection devices for HAR. That is because: (1) timely feedback; (2) no damage to the human body; and (3) the wide range of recognizable actions. However, existing public datasets of sEMG contained relatively few activities, and several large-scale datasets only collected the action of the hand. In addition, the processing of sEMG signals is a new field with no effective evaluation system for it. To tackle these problems, we establish a novel dataset for HAR on lower-limb sEMG named “HAR-sEMG,” using 6 sEMG signal sensors attached to the left leg. A benchmark summarizing experiments with many combinations of existing high-dimensional signal processing algorithms-based manifold learning on our dataset is also provided for a performance analysis.
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Literature
1.
Abeywickrama T, Cheema MA, Taniar D (2016) k-nearest neighbors on road networks: a journey in experimentation and in-memory implementation. Proce Vldb Endow 9(6):492–503CrossRef
2.
Ai H, Cheng X (2018) Research on embedded access control security system and face recognition system. Measurement 123:309–322CrossRef
3.
Al-Timemy, (2015) Improving the performance against force variation of emg controlled multifunctional upper-limb prostheses for transradial amputees. IEEE Trans Neural Syst Rehabil Eng 24(6):650–661
4.
Atzori M, Gijsberts A, Kuzborskij I (2015) Characterization of a benchmark database for myoelectric movement classification. IEEE Trans Neural Syst Rehabil Eng 23(1):73–83CrossRef
5.
Atzori M, Gijsberts A, Heynen S, Building the Ninapro Database: A Resource for the Biorobotics Community. In Proceedings of the 4th IEEE RAS EMBS international conference on biomedical robotics and biomechatronics, pp 1258–1265 (2012)
6.
Belkin M (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inf Process Syst 14(6):585–591
7.
Bonomi A, Goris A, Yin B (2009) Detection of type, duration, and intensity of physical activity using an accelerometer. Med Sci Sports Exercise 41(9):1770–1777CrossRef
8.
Chaaraoui A, Florez-Revuelta F (2014) Adaptive human action recognition with an evolving bag of key poses. IEEE Trans Auton Mental Develop 6(2):139–152CrossRef
9.
Clancy E, Morin E, Merletti R (2002) Sampling, noisereduction and amplitude estimation issues in surface electromyography. J Electromyogr Kinesiol 12(1):1–16CrossRef
10.
11.
Du S, Vuskovic M, Temporal VS (2004) Spectral Approach to Feature Extraction from Prehensile EMG Signals. In Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, pp 344–350
12.
Fathalla A, Salah A, Li K (2020) Deep end-to-end learning for price prediction of second-hand items. Knowl Inf Syst 62(4):4541–4568CrossRef
13.
Feng N, Shi Q, Wang H (2018) A soft robotic hand: design, analysis, sEMG control, and experiment. Int J Adv Manufact Technol 97(1–4):319–333CrossRef
14.
Fisher R (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188CrossRef
15.
Gao N, Zhao L (2016) A Pedestrian Dead Reckoning system using SEMG based on activities recognition. In: 2016 IEEE Chinese Guidance. Navigation and Control Conference 2361–2365
16.
Hasic F, Corea C, Blatt J (2020) Decision model change patterns for dynamic system evolution. Knowl Inf Syst
17.
He X, Niyogi P (2003) Locality preserving projections. Adv Neural Inf Process Syst 16(1):186–197
18.
Hermens H, Freriks B, Disselhorstklug C (2000) Development of recommendations for sEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 10(5):361–374CrossRef
19.
Hernandez F, Suarez L, Villamizar J (2019) Human Activity Recognition on Smartphones Using a Bidirectional LSTM Network, 2019 XXII Symposium on Image. Signal Processing and Artificial Vision
20.
Hong C, Yeung D (2006) Robust locally linear embedding. Pattern Recognit 39(6):1053–1065CrossRef
21.
22.
Jalal A, Quaid M, Hasan A (2018) Wearable Sensor-Based Human Behavior Understanding and Recognition in Daily Life for Smart Environments. In: IEEE Conference on International Conference on Frontiers of information technology,
23.
Jie L, Qiao L, Xinhui S (2019) Action parsing driven video summarization based on reinforcement learning. IEEE Trans Circuits Syst Video Technol 29(7):2126–2137
24.
Jun-Young J, Wonho H, Hyundae Y (2015) A neural network-based gait phaseclassification method using sensors equipped on lower limb exoskeleton robots. Sensors 15(11):27738–27759CrossRef
25.
Kase N, Babaee M, Rigoll G (2017) Multi-view Human Activity Recognition Using Motion Frequency. Image Processing, pp 3963–3967
26.
Khushaba R, Kodagoda S, Takruri M (2012) Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst Appl 39(12):10731–10738CrossRef
27.
Khushaba R, Kodagoda S, Liu D (2013) Muscle computer interfaces for driver distraction reduction. Comput Methods Programs Biomed 110(2):137–149CrossRef
28.
Krasoulis A, Kyranou I, Erden M (2017) Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements. J NeuroEng Rehabil 14(1):71CrossRef
29.
Kwapisz J, Weiss G, Moore S (2010) Activity recognition using cell phone accelerometers. ACM SIGKDD Explor Newslett 12(2):74–82CrossRef
30.
Lee S, Yoon S, Cho H (2017) Human Activity Recognition from Accelerometer Data Using Convolutional Neural Network. Big Data and Smart Computing, pp 131–134
31.
Liu F, Zhang W, Gu S (2016) Local linear laplacian eigenmaps: a direct extension of LLE. Pattern Recognit Lett 75:30–35CrossRef
32.
Lu J, Tong K (2019) Robust single accelerometer-based activity recognition using modified recurrence plot. IEEE Sensors 19(15):6317–6324CrossRef
33.
34.
Morel J, Chenu C, Lorenz K (2015) Ecosystem services provided by soils of urban, industrial, traffic, mining, and military areas. J Soils Sediments 15(8):1659–1666CrossRef
35.
Nurhanim K, Elamvazuthi I, Izhar L (2017) Classification of human activity based on smartphone inertial sensor using support vector machine. Robotics and manufacturing automation (ROMA), pp 1–5
36.
Park S, Lee D, Chung WK (2019) Hierarchical motion segmentation through sEMG for continuous lower limb motions. IEEE Robot Autom Lett 99:1–1
37.
Paul RJ (2010) What an information system is, and why is it important to know this. Iti international conference on information technology interfaces
38.
Pizzolato S, Tagliapietra L, Cognolato M (2017) Comparison of six electromyography acquisition setups on hand movement classification tasks. PLoS ONE 12(10):e0186132CrossRef
39.
Prates R, Schwartz W (2016) Kernel Hierarchical PCA for Person Re-identification. International Conference on Pattern Recognition, pp 2091–2096
40.
Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRef
41.
Ryu J, Ryu J, Ryu J (2017) sEMG signal-based lower limb human motion detection using a top and slope feature extraction algorithm. IEEE Signal Process Lett 24:929–932932CrossRef
42.
43.
Sapsanis C, Georgoulas G, Tzes A (2013) Improving EMG based classification of basic hand movements using EMD. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 5754 - 5757
44.
Shen Y, Phan NH, Xiao X (2016) Dynamic socialized gaussian process models for human behavior prediction in a health social network. Knowl Inf Syst 49(2):455–479CrossRef
45.
Shi X, Qin P, Zhu J (2020) Feature extraction and classification of lower limb motion based on sEMG signals. IEEE Access 99:1–1
46.
Szabó C, Morgan L, Karkar K (2015) Electromyography-based seizure detector: preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings. Epilepsia 56(9):1432–1437CrossRef
47.
Tao D, Jin L, Wang Y (2014) Rank preserving discriminant analysis for human behavior recognition on wireless sensor networks. IEEE Trans Ind Inf 10(1):813–823CrossRef
48.
Tenenbaum J, Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRef
49.
Theodoridis T, Agapitos A, Hu H (2011) A gaussian groundplan projection area model for evolving probabilistic classifiers. In: 13th annual genetic and evolutionary computation conference, pp 1339–1346
50.
Toch E, Lerner B, Ben-Zion E (2019) Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowl Inf Syst 58(3):501–523CrossRef
51.
Umakanthan S, Denman S, Fookes C (2014) Multiple Instance Dictionary Learning for Activity Representation. Pattern Recognition, pp 1377–1382
52.
Vemulapalli R, Arrate F, Chellappa R (2014) Human action recognition by representing 3d skeletons as points in a lie group. Computer Vision and Pattern Recognition 588–595
53.
Wei W, Wong Y, Du Y (2017) A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface. Pattern Recognit Lett
54.
Wold S (1987) Principal component analysis. Chemometr Intell Lab Syst 2(1):37–52CrossRef
55.
Wu J, Tian Z, Sun L (2015) Real-time american sign language recognition using wrist-worn motion and sEMG sensors. In: International conference on wearable and implantable body sensor networks, pp 1–6
56.
Xugang X, Minyan T, Miran S (2017) Evaluation of feature extraction and recognition for activity monitoring and fall detection based on wearable sEMG sensors. Sensors 17(6):1229CrossRef
57.
Yu Y, Fan L, Kuang S (2015) The research of sEMG movement pattern classification based on multiple fused wavelet function. In: IEEE International Conference on Cyber Technology in Automation IEEE
58.
Zhang Z, Sup F (2014) Activity recognition of the torso based on surface electromyography for exoskeleton control. Biomed Signal ProcessControl 10:281–288CrossRef
59.
Zhang H, Yoshie O (2012) Improving human activity recognition using subspace clustering. Mach Learn Cybern 3:1058–1063
60.
Zhang S, Zhang W, Niu J (2018) Improving short-text representation in convolutional networks by dependency parsing. Knowl Inf Syst 61:463–484CrossRef
61.
Zhu Z, Mingyao Z, Qi G (2019) A phase congruency and local laplacian energy based multi-modality medical image fusion method. IEEE Access 20811–20824
62.
Zhu Z, Yin H, Chai Y (2018) A novel multi-modality image fusion method based on image decomposition and sparse representation. Information Sciences, pp 516–529
Metadata
Title
HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG
Authors
Yu Luan
Yuhang Shi
Wanyin Wu
Zhiyao Liu
Hai Chang
Jun Cheng
Publication date
07-09-2021
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 10/2021
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-021-01598-w

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