Identifying The Arm Joint Dynamics Using Muscle Synergy Patterns and SVMD-BiGRU Hybrid Mechanism
-
Seyyed Ali Zendehbad
,
Hamid Reza Kobravi
,
Mohammad Mahdi Khalilzadeh
,
Athena Sharifi Razavi
,
Payam Sasan Nezhad
Abstract
Purpose: In this study, we propose a novel generalizable hybrid underlying mechanism m for mapping Human Pose Estimation (HPE) data to muscle synergy patterns, which can be highly efficient in improving visual biofeedback.
Materials and Methods: In the first step, Electromyography (EMG) data from the upper limb muscles of twelve healthy participants are collected and pre-processed, and muscle synergy patterns are extracted from it. Concurrently, kinematic data are detected using the OpenPose model. Through synchronization and normalization, the Successive Variational Mode Decomposition (SVMD) algorithm decomposes synergy control patterns into smaller components. To establish mappings, a custom Bidirectional Gated Recurrent Unit (BiGRU) model is employed. Comparative analysis against popular models validates the efficacy of our approach, revealing the generated trajectory as potentially ideal for visual biofeedback. Remarkably, the combined SVMD-BiGRU model outperforms alternatives.
Results: the results show that the trajectory generated by the model is potentially suitable for visual biofeedback systems. Remarkably, the combined SVMD-BiGRU model outperforms alternatives. Furthermore, empirical assessments have demonstrated the adept ability of healthy participants to closely adhere to the trajectory generated by the model output during the test phase.
Conclusion: Ultimately, the incorporation of this innovative mechanism at the heart of visual biofeedback systems has been revealed to significantly elevate both the quantity and quality of movement.
1- World Health Organization, Global report on health equity for persons with disabilities. World Health Organization, (2022).
2- Hojjat Allah Haghgoo, Elmira Saed Pazuki, Ali S Hosseini, and Mehdi Rassafiani, "Depression, activities of daily living and quality of life in patients with stroke." Journal of the neurological sciences, Vol. 328 (No. 1-2), pp. 87-91, (2013).
3- Neilzo Nunes Oliveira, Érica Midori Ikegami, Nayara Gomes Nunes Oliveira, and Darlene Mara dos Santos Tavares, "Factors associated with functional disability in older adults with cataract: integrative review." Revista Brasileira de Geriatria e Gerontologia, Vol. 24p. e220076, (2022).
4- William D Spector and John A Fleishman, "Combining activities of daily living with instrumental activities of daily living to measure functional disability." The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, Vol. 53 (No. 1), pp. S46-S57, (1998).
5- John W Krakauer, "Motor learning: its relevance to stroke recovery and neurorehabilitation." Current opinion in neurology, Vol. 19 (No. 1), pp. 84-90, (2006).
6- Julie Moreland and Mary Ann Thomson, "Efficacy of electromyographic biofeedback compared with conventional physical therapy for upper-extremity function in patients following stroke: a research overview and meta-analysis." Physical therapy, Vol. 74 (No. 6), pp. 534-43, (1994).
7- Ana Cisnal, Paula Gordaliza, Javier Pérez Turiel, and Juan Carlos Fraile, "Interaction with a hand rehabilitation exoskeleton in EMG-driven bilateral therapy: Influence of visual biofeedback on the users’ performance." Sensors, Vol. 23 (No. 4), p. 2048, (2023).
8- Yukihiro Hara, "Neurorehabilitation with new functional electrical stimulation for hemiparetic upper extremity in stroke patients." Journal of Nippon Medical School, Vol. 75 (No. 1), pp. 4-14, (2008).
9- Lonnie A Nelson, "The role of biofeedback in stroke rehabilitation: past and future directions." Topics in stroke rehabilitation, Vol. 14 (No. 4), pp. 59-66, (2007).
10- Aaron R Seitz, "Cognitive neuroscience: targeting neuroplasticity with neural decoding and biofeedback." Current Biology, Vol. 23 (No. 5), pp. R210-R12, (2013).
11- Sisi Feng et al., "EMG biofeedback combined with rehabilitation training may be the best physical therapy for improving upper limb motor function and relieving pain in patients with the post-stroke shoulder-hand syndrome: a Bayesian network meta-analysis." Frontiers in Neurology, Vol. 13p. 1056156, (2023).
12- He Huang, Steven L Wolf, and Jiping He, "Recent developments in biofeedback for neuromotor rehabilitation." Journal of neuroengineering and rehabilitation, Vol. 3pp. 1-12, (2006).
13- Robert Teasell et al., "Developing a framework for utilizing adjunct rehabilitation therapies in motor recovery of upper extremity post stroke." Topics in Stroke Rehabilitation, Vol. 30 (No. 5), pp. 493-500, (2023).
14- Oonagh M Giggins, Ulrik McCarthy Persson, and Brian Caulfield, "Biofeedback in rehabilitation." Journal of neuroengineering and rehabilitation, Vol. 10pp. 1-11, (2013).
15- Sophie Hall, Fridolin Wild, and Tjeerd olde Scheper, "Real-time auditory biofeedback system for learning a novel arm trajectory: a usability study." Perspectives on Wearable Enhanced Learning (WELL) Current Trends, Research, and Practice, pp. 385-409, (2019).
16- EV Isakova and Yu V Egorova, "Visual and acoustic feedback on the support reaction for upper and lower extremities: a case study of a female patient after a stroke." Almanac of Clinical Medicine, Vol. 49 (No. 6), pp. 435-42, (2021).
17- Katherine Hankinson, Alex Shaykevich, Ann-Maree Vallence, Jennifer Rodger, Michael Rosenberg, and Christopher Etherton-Beer, "A tailored music-motor therapy and real-time biofeedback mobile phone app (‘GotRhythm’) to promote rehabilitation following stroke: a pilot study." Neuroscience Insights, Vol. 17p. 26331055221100587, (2022).
18- Tormene Giorgino, Paolo Tormene, G Maggioni, Davide Capozzi, Silvana Quaglini, and C Pistarini, "Assessment of sensorized garments as a flexible support to self-administered post-stroke physical rehabilitation." European Journal of Physical and Rehabilitation Medicine, Vol. 45 (No. 1), pp. 75-84, (2009).
19- Chang-Yong Kim et al., "Effect of spatial target reaching training based on visual biofeedback on the upper extremity function of hemiplegic stroke patients." Journal of physical therapy science, Vol. 27 (No. 4), pp. 1091-96, (2015).
20- Elizabeth B Brokaw, Emily Eckel, and Bambi R Brewer, "Usability evaluation of a kinematics focused Kinect therapy program for individuals with stroke." Technology and Health Care, Vol. 23 (No. 2), pp. 143-51, (2015).
21- Li-Fong Lin, Yi-Jia Lin, Zi-Hao Lin, Li-Yun Chuang, Wei-Chun Hsu, and Yuan-Hsiang Lin, "Feasibility and efficacy of wearable devices for upper limb rehabilitation in patients with chronic stroke: a randomized controlled pilot study." European journal of physical and rehabilitation medicine, Vol. 54 (No. 3), pp. 388-96, (2017).
22- Muhammed Nur Ögün, Ramazan Kurul, Mustafa Fatih Yaşar, Sule Aydin Turkoglu, Şebnem Avci, and Nebil Yildiz, "Effect of leap motion-based 3D immersive virtual reality usage on upper extremity function in ischemic stroke patients." Arquivos de neuro-psiquiatria, Vol. 77 (No. 10), pp. 681-88, (2019).
23- PK Paul, Ritam Chatterjee, Sucheta Chakraborty, KS Tiwary, PS Aithal, and S Sharma, "Advanced Healthcare Informatics Practice Vis-À-Vis Virtual Reality Implications—a Scientific Overview." Immersive Virtual and Augmented Reality in Healthcare, pp. 28-49, (2024).
24- Philémon Marcel-Millet, Philippe Gimenez, Alain Groslambert, Gilles Ravier, and Sidney Grospretre, "The type of visual biofeedback influences maximal handgrip strength and activation strategies." European Journal of Applied Physiology, Vol. 121pp. 1607-16, (2021).
25- Yusha Liu, Rafael ML Silva, Jeffrey B Friedrich, Dennis S Kao, Pierre D Mourad, and Aaron E Bunnell, "Surface electromyography-driven therapeutic gaming for rehabilitation of upper extremity weakness: a pilot study." Plastic and reconstructive surgery, Vol. 150 (No. 1), pp. 125-31, (2022).
26- Vivianne F Cardoso et al., "Towards an upper limb rehabilitation tool after stroke based on surface electromyography biofeedback and virtual reality." Research on Biomedical Engineering, Vol. 38 (No. 3), pp. 1017-25, (2022).
27- Onur Armagan, Funda Tascioglu, and Cengiz Oner, "Electromyographic biofeedback in the treatment of the hemiplegic hand: a placebo-controlled study." American Journal of Physical Medicine & Rehabilitation, Vol. 82 (No. 11), pp. 856-61, (2003).
28- Steven L Wolf, "Electromyographic biofeedback applications to stroke patients: a critical review." Physical therapy, Vol. 63 (No. 9), pp. 1448-59, (1983).
29- Fatemeh Davarinia and Ali Maleki, "SSVEP-gated EMG-based decoding of elbow angle during goal-directed reaching movement." Biomedical Signal Processing and Control, Vol. 71p. 103222, (2022).
30- Liwei Cheng, Duanling Li, Gongjing Yu, Zhonghai Zhang, and Shuyue Yu, "Robotic arm control system based on brain-muscle mixed signals." Biomedical Signal Processing and Control, Vol. 77p. 103754, (2022).
31- Stephen L Toepp, Martin V Mohrenschildt, and Aimee J Nelson, "An EMG-Based Biofeedback System for Tailored Interventions Involving Distributed Muscles." IEEE Sensors Journal, (2023).
32- Juri Taborri et al., "Feasibility of muscle synergy outcomes in clinics, robotics, and sports: a systematic review." Applied bionics and biomechanics, Vol. 2018 (No. 1), p. 3934698, (2018).
33- Vincent CK Cheung et al., "Muscle synergy patterns as physiological markers of motor cortical damage." Proceedings of the national academy of sciences, Vol. 109 (No. 36), pp. 14652-56, (2012).
34- Jenny C Castiblanco, Steffen Ortmann, Ivan F Mondragon, Catalina Alvarado-Rojas, Michael Jöbges, and Julian D Colorado, "Myoelectric pattern recognition of hand motions for stroke rehabilitation." Biomedical Signal Processing and Control, Vol. 57p. 101737, (2020).
35- Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, and Hamid Azadeh, "Handwritten signatures verification based on arm and hand muscles synergy." Biomedical Signal Processing and Control, Vol. 76p. 103697, (2022).
36- Filip Gasparic et al., "Nonlinear mapping from EMG to prosthesis closing velocity improves force control with EMG biofeedback." IEEE Transactions on Haptics, (2023).
37- Dario Farina, Roberto Merletti, and Roger M Enoka, "The extraction of neural strategies from the surface EMG." Journal of applied physiology, Vol. 96 (No. 4), pp. 1486-95, (2004).
38- Seyyed Ali Zendehbad, Hamid Reza Kobravi, Mohammad Mahdi Khalilzadeh, Athena Sharifi Razavi, and Payam Sasan Nezhad, "Presenting a New Muscle Synergy Analysis Based Mechanism to Design a Trackable Visual Biofeedback Signal: Applicable to Arm Movement Recovery After Ischemic Stroke." IEEE Access, Vol. 11pp. 70190-202, (2023).
39- Ali Zendehbad, Hamid Reza Kobravi, Mohammad Mahdi Khalilzadeh, Athena Sharifi Razavi, and Payam Sasannejad, "A New Visual Biofeedback Protocol Based on Analyzing the Muscle Synergy Patterns to Recover the Upper Limbs Movement in Ischemic Stroke Patients: A Pilot Study." The Neuroscience Journal of Shefaye Khatam, Vol. 11 (No. 3), pp. 11-24, (2023).
40- Seyedeh Somayeh Naghibi, Ali Fallah, Ali Maleki, and Farnaz Ghassemi, "Elbow angle generation during activities of daily living using a submovement prediction model." Biological Cybernetics, Vol. 114pp. 389-402, (2020).
41- Zonglei Chen, Minbo Ma, Tianrui Li, Hongjun Wang, and Chongshou Li, "Long sequence time-series forecasting with deep learning: A survey." Information Fusion, Vol. 97p. 101819, (2023).
42- Sabeen Ahmed, Ian E Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ravi P Ramachandran, and Ghulam Rasool, "Transformers in time-series analysis: A tutorial." Circuits, Systems, and Signal Processing, Vol. 42 (No. 12), pp. 7433-66, (2023).
43- Siyuan Huang, Yepeng Liu, Fan Zhang, Yue Li, Jinjiang Li, and Caiming Zhang, "CrossWaveNet: A dual-channel network with deep cross-decomposition for Long-term Time Series Forecasting." Expert Systems with Applications, Vol. 238p. 121642, (2024).
44- Weiping Jiang, Jian Wang, Zhao Li, Wudong Li, and Peng Yuan, "A new deep self-attention neural network for GNSS coordinate time series prediction." GPS Solutions, Vol. 28 (No. 1), p. 3, (2024).
45- Roberto Merletti and PJJEK Di Torino, "Standards for reporting EMG data." J Electromyogr Kinesiol, Vol. 9 (No. 1), pp. 3-4, (1999).
46- Roberto Merletti, Marco Knaflitz, and Carlo J DeLuca, "Electrically evoked myoelectric signals." Crit Rev Biomed Eng, Vol. 19 (No. 4), pp. 293-340, (1992).
47- Roberto Merletti and Loredana R Lo Conte, "Surface EMG signal processing during isometric contractions." Journal of Electromyography and Kinesiology, Vol. 7 (No. 4), pp. 241-50, (1997).
48- Edward A Clancy and Neville Hogan, "Multiple site electromyograph amplitude estimation." IEEE Transactions on Biomedical Engineering, Vol. 42 (No. 2), pp. 203-11, (1995).
49- Claudio Orizio, Renza Perini, and Arsenio Veicsteinas, "Muscular sound and force relationship during isometric contraction in man." European journal of applied physiology and occupational physiology, Vol. 58pp. 528-33, (1989).
50- Carlo J De Luca, Alexander Adam, Robert Wotiz, L Donald Gilmore, and S Hamid Nawab, "Decomposition of surface EMG signals." Journal of neurophysiology, Vol. 96 (No. 3), pp. 1646-57, (2006).
51- Gabriel Rilling, Patrick Flandrin, and Paulo Goncalves, "On empirical mode decomposition and its algorithms." in IEEE-EURASIP workshop on nonlinear signal and image processing, (2003), Vol. 3 (No. 3): Grado: IEEE, pp. 8-11.
52- Yun Yang, ChongJun Fan, and HongLin Xiong, "A novel general-purpose hybrid model for time series forecasting." Applied Intelligence, Vol. 52 (No. 2), pp. 2212-23, (2022).
53- Konstantin Dragomiretskiy and Dominique Zosso, "Variational mode decomposition." IEEE transactions on signal processing, Vol. 62 (No. 3), pp. 531-44, (2013).
54- Mojtaba Nazari and Sayed Mahmoud Sakhaei, "Variational mode extraction: A new efficient method to derive respiratory signals from ECG." IEEE journal of biomedical and health informatics, Vol. 22 (No. 4), pp. 1059-67, (2017).
55- Qing Zhu, Fan Zhang, Shan Liu, Yiqiong Wu, and Lin Wang, "A hybrid VMD–BiGRU model for rubber futures time series forecasting." Applied Soft Computing, Vol. 84p. 105739, (2019).
56- Mojtaba Nazari and Sayed Mahmoud Sakhaei, "Successive variational mode decomposition." Signal Processing, Vol. 174p. 107610, (2020).
57- http://www.seniam.org/
58- David C Preston and Barbara E Shapiro, Electromyography and neuromuscular disorders e-book: clinical-electrophysiologic correlations (Expert Consult-Online). Elsevier Health Sciences, (2012).
59- Ce Zheng et al., "Deep learning-based human pose estimation: A survey." ACM Computing Surveys, Vol. 56 (No. 1), pp. 1-37, (2023).
60- Tausif Diwan, G Anirudh, and Jitendra V Tembhurne, "Object detection using YOLO: Challenges, architectural successors, datasets and applications." multimedia Tools and Applications, Vol. 82 (No. 6), pp. 9243-75, (2023).
61- Muhammad Hussain, "YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection." Machines, Vol. 11 (No. 7), p. 677, (2023).
62- Nobuyasu Nakano et al., "Evaluation of 3D markerless motion capture accuracy using OpenPose with multiple video cameras." Frontiers in sports and active living, Vol. 2p. 50, (2020).
63- Reuben Moyo, Stanley Ndebvu, Michael Zimba, and Jimmy Mbelwa, "A Video-based Detector for Suspicious Activity in Examination with OpenPose." arXiv preprint arXiv:2307.11413, (2023).
64- Dilliraj Ekambaram and Vijayakumar Ponnusamy, "Real-time AI-assisted visual exercise pose correctness during rehabilitation training for musculoskeletal disorder." Journal of Real-Time Image Processing, Vol. 21 (No. 1), p. 2, (2024).
65- Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh, "Realtime multi-person 2d pose estimation using part affinity fields." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2017), pp. 7291-99.
66- Tomas Simon, Hanbyul Joo, Iain Matthews, and Yaser Sheikh, "Hand keypoint detection in single images using multiview bootstrapping." in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, (2017), pp. 1145-53.
67- Andrea d'Avella, Philippe Saltiel, and Emilio Bizzi, "Combinations of muscle synergies in the construction of a natural motor behavior." Nature neuroscience, Vol. 6 (No. 3), pp. 300-08, (2003).
68- Anh Huy Phan and Andrzej Cichocki, "Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification." Neurocomputing, Vol. 74 (No. 11), pp. 1956-69, (2011).
69- Andrzej Cichocki, Rafal Zdunek, and Shun-ichi Amari, "Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization." in International conference on independent component analysis and signal separation, (2007): Springer, pp. 169-76.
70- Yeongdae Kim, Sorawit Stapornchaisit, Hiroyuki Kambara, Natsue Yoshimura, and Yasuharu Koike, "Muscle Synergy and Musculoskeletal Model‐Based Continuous Multi‐Dimensional Estimation of Wrist and Hand Motions." Journal of Healthcare Engineering, Vol. 2020 (No. 1), p. 5451219, (2020).
71- Eugen Diaconescu, "The use of NARX neural networks to predict chaotic time series." Wseas Transactions on computer research, Vol. 3 (No. 3), pp. 182-91, (2008).
72- Georg Dorffner, "Neural networks for time series processing." Neural network world, Vol. 6 (No. 4), pp. 447-68, (1996).
73- Tsungnan Lin, Bill G Horne, Peter Tino, and C Lee Giles, "Learning long-term dependencies in NARX recurrent neural networks." IEEE transactions on neural networks, Vol. 7 (No. 6), pp. 1329-38, (1996).
74- Zachary C Lipton, John Berkowitz, and Charles Elkan, "A critical review of recurrent neural networks for sequence learning." arXiv preprint arXiv:1506.00019, (2015).
75- José Maria P Menezes Jr and Guilherme A Barreto, "Long-term time series prediction with the NARX network: An empirical evaluation." Neurocomputing, Vol. 71 (No. 16-18), pp. 3335-43, (2008).
76- Mike Schuster and Kuldip K Paliwal, "Bidirectional recurrent neural networks." IEEE transactions on Signal Processing, Vol. 45 (No. 11), pp. 2673-81, (1997).
77- Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3555, (2014).
78- Daoming She and Minping Jia, "A BiGRU method for remaining useful life prediction of machinery." Measurement, Vol. 167p. 108277, (2021).
79- Andrea d'Avella, Alessandro Portone, Laure Fernandez, and Francesco Lacquaniti, "Control of fast-reaching movements by muscle synergy combinations." Journal of Neuroscience, Vol. 26 (No. 30), pp. 7791-810, (2006).
80- Petre Stoica and Randolph L Moses, Spectral analysis of signals. Pearson Prentice Hall Upper Saddle River, NJ, (2005).
81- Tomoko Nemoto and David Beglar, "Likert-scale questionnaires." in JALT 2013 conference proceedings, (2014), pp. 1-8.
82- Shigeki Kubota et al., "A newly developed upper limb single-joint HAL in a patient with elbow flexion reconstruction after traumatic brachial plexus injury: a case report." Interdisciplinary Neurosurgery, Vol. 10pp. 66-68, (2017).
83- Jiahao Chen and Hong Qiao, "Muscle-synergies-based neuromuscular control for motion learning and generalization of a musculoskeletal system." IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51 (No. 6), pp. 3993-4006, (2020).
84- Richard A Schmidt, Timothy D Lee, Carolee Winstein, Gabriele Wulf, and Howard N Zelaznik, Motor control and learning: A behavioral emphasis. Human kinetics, (2018).
85- Seyed Safavynia, Gelsy Torres-Oviedo, and Lena Ting, "Muscle synergies: implications for clinical evaluation and rehabilitation of movement." Topics in spinal cord injury rehabilitation, Vol. 17 (No. 1), pp. 16-24, (2011).
86- Yoshua Bengio, Patrice Simard, and Paolo Frasconi, "Learning long-term dependencies with gradient descent is difficult." IEEE transactions on neural networks, Vol. 5 (No. 2), pp. 157-66, (1994).
87- Xiaolei Liu, Zi Lin, and Ziming Feng, "Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM." Energy, Vol. 227p. 120492, (2021).
88- Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan, Geoffrey I Webb, Germain Forestier, and Mahsa Salehi, "Deep learning for time series classification and extrinsic regression: A current survey." ACM Computing Surveys, Vol. 56 (No. 9), pp. 1-45, (2024).
2- Hojjat Allah Haghgoo, Elmira Saed Pazuki, Ali S Hosseini, and Mehdi Rassafiani, "Depression, activities of daily living and quality of life in patients with stroke." Journal of the neurological sciences, Vol. 328 (No. 1-2), pp. 87-91, (2013).
3- Neilzo Nunes Oliveira, Érica Midori Ikegami, Nayara Gomes Nunes Oliveira, and Darlene Mara dos Santos Tavares, "Factors associated with functional disability in older adults with cataract: integrative review." Revista Brasileira de Geriatria e Gerontologia, Vol. 24p. e220076, (2022).
4- William D Spector and John A Fleishman, "Combining activities of daily living with instrumental activities of daily living to measure functional disability." The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, Vol. 53 (No. 1), pp. S46-S57, (1998).
5- John W Krakauer, "Motor learning: its relevance to stroke recovery and neurorehabilitation." Current opinion in neurology, Vol. 19 (No. 1), pp. 84-90, (2006).
6- Julie Moreland and Mary Ann Thomson, "Efficacy of electromyographic biofeedback compared with conventional physical therapy for upper-extremity function in patients following stroke: a research overview and meta-analysis." Physical therapy, Vol. 74 (No. 6), pp. 534-43, (1994).
7- Ana Cisnal, Paula Gordaliza, Javier Pérez Turiel, and Juan Carlos Fraile, "Interaction with a hand rehabilitation exoskeleton in EMG-driven bilateral therapy: Influence of visual biofeedback on the users’ performance." Sensors, Vol. 23 (No. 4), p. 2048, (2023).
8- Yukihiro Hara, "Neurorehabilitation with new functional electrical stimulation for hemiparetic upper extremity in stroke patients." Journal of Nippon Medical School, Vol. 75 (No. 1), pp. 4-14, (2008).
9- Lonnie A Nelson, "The role of biofeedback in stroke rehabilitation: past and future directions." Topics in stroke rehabilitation, Vol. 14 (No. 4), pp. 59-66, (2007).
10- Aaron R Seitz, "Cognitive neuroscience: targeting neuroplasticity with neural decoding and biofeedback." Current Biology, Vol. 23 (No. 5), pp. R210-R12, (2013).
11- Sisi Feng et al., "EMG biofeedback combined with rehabilitation training may be the best physical therapy for improving upper limb motor function and relieving pain in patients with the post-stroke shoulder-hand syndrome: a Bayesian network meta-analysis." Frontiers in Neurology, Vol. 13p. 1056156, (2023).
12- He Huang, Steven L Wolf, and Jiping He, "Recent developments in biofeedback for neuromotor rehabilitation." Journal of neuroengineering and rehabilitation, Vol. 3pp. 1-12, (2006).
13- Robert Teasell et al., "Developing a framework for utilizing adjunct rehabilitation therapies in motor recovery of upper extremity post stroke." Topics in Stroke Rehabilitation, Vol. 30 (No. 5), pp. 493-500, (2023).
14- Oonagh M Giggins, Ulrik McCarthy Persson, and Brian Caulfield, "Biofeedback in rehabilitation." Journal of neuroengineering and rehabilitation, Vol. 10pp. 1-11, (2013).
15- Sophie Hall, Fridolin Wild, and Tjeerd olde Scheper, "Real-time auditory biofeedback system for learning a novel arm trajectory: a usability study." Perspectives on Wearable Enhanced Learning (WELL) Current Trends, Research, and Practice, pp. 385-409, (2019).
16- EV Isakova and Yu V Egorova, "Visual and acoustic feedback on the support reaction for upper and lower extremities: a case study of a female patient after a stroke." Almanac of Clinical Medicine, Vol. 49 (No. 6), pp. 435-42, (2021).
17- Katherine Hankinson, Alex Shaykevich, Ann-Maree Vallence, Jennifer Rodger, Michael Rosenberg, and Christopher Etherton-Beer, "A tailored music-motor therapy and real-time biofeedback mobile phone app (‘GotRhythm’) to promote rehabilitation following stroke: a pilot study." Neuroscience Insights, Vol. 17p. 26331055221100587, (2022).
18- Tormene Giorgino, Paolo Tormene, G Maggioni, Davide Capozzi, Silvana Quaglini, and C Pistarini, "Assessment of sensorized garments as a flexible support to self-administered post-stroke physical rehabilitation." European Journal of Physical and Rehabilitation Medicine, Vol. 45 (No. 1), pp. 75-84, (2009).
19- Chang-Yong Kim et al., "Effect of spatial target reaching training based on visual biofeedback on the upper extremity function of hemiplegic stroke patients." Journal of physical therapy science, Vol. 27 (No. 4), pp. 1091-96, (2015).
20- Elizabeth B Brokaw, Emily Eckel, and Bambi R Brewer, "Usability evaluation of a kinematics focused Kinect therapy program for individuals with stroke." Technology and Health Care, Vol. 23 (No. 2), pp. 143-51, (2015).
21- Li-Fong Lin, Yi-Jia Lin, Zi-Hao Lin, Li-Yun Chuang, Wei-Chun Hsu, and Yuan-Hsiang Lin, "Feasibility and efficacy of wearable devices for upper limb rehabilitation in patients with chronic stroke: a randomized controlled pilot study." European journal of physical and rehabilitation medicine, Vol. 54 (No. 3), pp. 388-96, (2017).
22- Muhammed Nur Ögün, Ramazan Kurul, Mustafa Fatih Yaşar, Sule Aydin Turkoglu, Şebnem Avci, and Nebil Yildiz, "Effect of leap motion-based 3D immersive virtual reality usage on upper extremity function in ischemic stroke patients." Arquivos de neuro-psiquiatria, Vol. 77 (No. 10), pp. 681-88, (2019).
23- PK Paul, Ritam Chatterjee, Sucheta Chakraborty, KS Tiwary, PS Aithal, and S Sharma, "Advanced Healthcare Informatics Practice Vis-À-Vis Virtual Reality Implications—a Scientific Overview." Immersive Virtual and Augmented Reality in Healthcare, pp. 28-49, (2024).
24- Philémon Marcel-Millet, Philippe Gimenez, Alain Groslambert, Gilles Ravier, and Sidney Grospretre, "The type of visual biofeedback influences maximal handgrip strength and activation strategies." European Journal of Applied Physiology, Vol. 121pp. 1607-16, (2021).
25- Yusha Liu, Rafael ML Silva, Jeffrey B Friedrich, Dennis S Kao, Pierre D Mourad, and Aaron E Bunnell, "Surface electromyography-driven therapeutic gaming for rehabilitation of upper extremity weakness: a pilot study." Plastic and reconstructive surgery, Vol. 150 (No. 1), pp. 125-31, (2022).
26- Vivianne F Cardoso et al., "Towards an upper limb rehabilitation tool after stroke based on surface electromyography biofeedback and virtual reality." Research on Biomedical Engineering, Vol. 38 (No. 3), pp. 1017-25, (2022).
27- Onur Armagan, Funda Tascioglu, and Cengiz Oner, "Electromyographic biofeedback in the treatment of the hemiplegic hand: a placebo-controlled study." American Journal of Physical Medicine & Rehabilitation, Vol. 82 (No. 11), pp. 856-61, (2003).
28- Steven L Wolf, "Electromyographic biofeedback applications to stroke patients: a critical review." Physical therapy, Vol. 63 (No. 9), pp. 1448-59, (1983).
29- Fatemeh Davarinia and Ali Maleki, "SSVEP-gated EMG-based decoding of elbow angle during goal-directed reaching movement." Biomedical Signal Processing and Control, Vol. 71p. 103222, (2022).
30- Liwei Cheng, Duanling Li, Gongjing Yu, Zhonghai Zhang, and Shuyue Yu, "Robotic arm control system based on brain-muscle mixed signals." Biomedical Signal Processing and Control, Vol. 77p. 103754, (2022).
31- Stephen L Toepp, Martin V Mohrenschildt, and Aimee J Nelson, "An EMG-Based Biofeedback System for Tailored Interventions Involving Distributed Muscles." IEEE Sensors Journal, (2023).
32- Juri Taborri et al., "Feasibility of muscle synergy outcomes in clinics, robotics, and sports: a systematic review." Applied bionics and biomechanics, Vol. 2018 (No. 1), p. 3934698, (2018).
33- Vincent CK Cheung et al., "Muscle synergy patterns as physiological markers of motor cortical damage." Proceedings of the national academy of sciences, Vol. 109 (No. 36), pp. 14652-56, (2012).
34- Jenny C Castiblanco, Steffen Ortmann, Ivan F Mondragon, Catalina Alvarado-Rojas, Michael Jöbges, and Julian D Colorado, "Myoelectric pattern recognition of hand motions for stroke rehabilitation." Biomedical Signal Processing and Control, Vol. 57p. 101737, (2020).
35- Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, and Hamid Azadeh, "Handwritten signatures verification based on arm and hand muscles synergy." Biomedical Signal Processing and Control, Vol. 76p. 103697, (2022).
36- Filip Gasparic et al., "Nonlinear mapping from EMG to prosthesis closing velocity improves force control with EMG biofeedback." IEEE Transactions on Haptics, (2023).
37- Dario Farina, Roberto Merletti, and Roger M Enoka, "The extraction of neural strategies from the surface EMG." Journal of applied physiology, Vol. 96 (No. 4), pp. 1486-95, (2004).
38- Seyyed Ali Zendehbad, Hamid Reza Kobravi, Mohammad Mahdi Khalilzadeh, Athena Sharifi Razavi, and Payam Sasan Nezhad, "Presenting a New Muscle Synergy Analysis Based Mechanism to Design a Trackable Visual Biofeedback Signal: Applicable to Arm Movement Recovery After Ischemic Stroke." IEEE Access, Vol. 11pp. 70190-202, (2023).
39- Ali Zendehbad, Hamid Reza Kobravi, Mohammad Mahdi Khalilzadeh, Athena Sharifi Razavi, and Payam Sasannejad, "A New Visual Biofeedback Protocol Based on Analyzing the Muscle Synergy Patterns to Recover the Upper Limbs Movement in Ischemic Stroke Patients: A Pilot Study." The Neuroscience Journal of Shefaye Khatam, Vol. 11 (No. 3), pp. 11-24, (2023).
40- Seyedeh Somayeh Naghibi, Ali Fallah, Ali Maleki, and Farnaz Ghassemi, "Elbow angle generation during activities of daily living using a submovement prediction model." Biological Cybernetics, Vol. 114pp. 389-402, (2020).
41- Zonglei Chen, Minbo Ma, Tianrui Li, Hongjun Wang, and Chongshou Li, "Long sequence time-series forecasting with deep learning: A survey." Information Fusion, Vol. 97p. 101819, (2023).
42- Sabeen Ahmed, Ian E Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ravi P Ramachandran, and Ghulam Rasool, "Transformers in time-series analysis: A tutorial." Circuits, Systems, and Signal Processing, Vol. 42 (No. 12), pp. 7433-66, (2023).
43- Siyuan Huang, Yepeng Liu, Fan Zhang, Yue Li, Jinjiang Li, and Caiming Zhang, "CrossWaveNet: A dual-channel network with deep cross-decomposition for Long-term Time Series Forecasting." Expert Systems with Applications, Vol. 238p. 121642, (2024).
44- Weiping Jiang, Jian Wang, Zhao Li, Wudong Li, and Peng Yuan, "A new deep self-attention neural network for GNSS coordinate time series prediction." GPS Solutions, Vol. 28 (No. 1), p. 3, (2024).
45- Roberto Merletti and PJJEK Di Torino, "Standards for reporting EMG data." J Electromyogr Kinesiol, Vol. 9 (No. 1), pp. 3-4, (1999).
46- Roberto Merletti, Marco Knaflitz, and Carlo J DeLuca, "Electrically evoked myoelectric signals." Crit Rev Biomed Eng, Vol. 19 (No. 4), pp. 293-340, (1992).
47- Roberto Merletti and Loredana R Lo Conte, "Surface EMG signal processing during isometric contractions." Journal of Electromyography and Kinesiology, Vol. 7 (No. 4), pp. 241-50, (1997).
48- Edward A Clancy and Neville Hogan, "Multiple site electromyograph amplitude estimation." IEEE Transactions on Biomedical Engineering, Vol. 42 (No. 2), pp. 203-11, (1995).
49- Claudio Orizio, Renza Perini, and Arsenio Veicsteinas, "Muscular sound and force relationship during isometric contraction in man." European journal of applied physiology and occupational physiology, Vol. 58pp. 528-33, (1989).
50- Carlo J De Luca, Alexander Adam, Robert Wotiz, L Donald Gilmore, and S Hamid Nawab, "Decomposition of surface EMG signals." Journal of neurophysiology, Vol. 96 (No. 3), pp. 1646-57, (2006).
51- Gabriel Rilling, Patrick Flandrin, and Paulo Goncalves, "On empirical mode decomposition and its algorithms." in IEEE-EURASIP workshop on nonlinear signal and image processing, (2003), Vol. 3 (No. 3): Grado: IEEE, pp. 8-11.
52- Yun Yang, ChongJun Fan, and HongLin Xiong, "A novel general-purpose hybrid model for time series forecasting." Applied Intelligence, Vol. 52 (No. 2), pp. 2212-23, (2022).
53- Konstantin Dragomiretskiy and Dominique Zosso, "Variational mode decomposition." IEEE transactions on signal processing, Vol. 62 (No. 3), pp. 531-44, (2013).
54- Mojtaba Nazari and Sayed Mahmoud Sakhaei, "Variational mode extraction: A new efficient method to derive respiratory signals from ECG." IEEE journal of biomedical and health informatics, Vol. 22 (No. 4), pp. 1059-67, (2017).
55- Qing Zhu, Fan Zhang, Shan Liu, Yiqiong Wu, and Lin Wang, "A hybrid VMD–BiGRU model for rubber futures time series forecasting." Applied Soft Computing, Vol. 84p. 105739, (2019).
56- Mojtaba Nazari and Sayed Mahmoud Sakhaei, "Successive variational mode decomposition." Signal Processing, Vol. 174p. 107610, (2020).
57- http://www.seniam.org/
58- David C Preston and Barbara E Shapiro, Electromyography and neuromuscular disorders e-book: clinical-electrophysiologic correlations (Expert Consult-Online). Elsevier Health Sciences, (2012).
59- Ce Zheng et al., "Deep learning-based human pose estimation: A survey." ACM Computing Surveys, Vol. 56 (No. 1), pp. 1-37, (2023).
60- Tausif Diwan, G Anirudh, and Jitendra V Tembhurne, "Object detection using YOLO: Challenges, architectural successors, datasets and applications." multimedia Tools and Applications, Vol. 82 (No. 6), pp. 9243-75, (2023).
61- Muhammad Hussain, "YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection." Machines, Vol. 11 (No. 7), p. 677, (2023).
62- Nobuyasu Nakano et al., "Evaluation of 3D markerless motion capture accuracy using OpenPose with multiple video cameras." Frontiers in sports and active living, Vol. 2p. 50, (2020).
63- Reuben Moyo, Stanley Ndebvu, Michael Zimba, and Jimmy Mbelwa, "A Video-based Detector for Suspicious Activity in Examination with OpenPose." arXiv preprint arXiv:2307.11413, (2023).
64- Dilliraj Ekambaram and Vijayakumar Ponnusamy, "Real-time AI-assisted visual exercise pose correctness during rehabilitation training for musculoskeletal disorder." Journal of Real-Time Image Processing, Vol. 21 (No. 1), p. 2, (2024).
65- Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh, "Realtime multi-person 2d pose estimation using part affinity fields." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2017), pp. 7291-99.
66- Tomas Simon, Hanbyul Joo, Iain Matthews, and Yaser Sheikh, "Hand keypoint detection in single images using multiview bootstrapping." in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, (2017), pp. 1145-53.
67- Andrea d'Avella, Philippe Saltiel, and Emilio Bizzi, "Combinations of muscle synergies in the construction of a natural motor behavior." Nature neuroscience, Vol. 6 (No. 3), pp. 300-08, (2003).
68- Anh Huy Phan and Andrzej Cichocki, "Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification." Neurocomputing, Vol. 74 (No. 11), pp. 1956-69, (2011).
69- Andrzej Cichocki, Rafal Zdunek, and Shun-ichi Amari, "Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization." in International conference on independent component analysis and signal separation, (2007): Springer, pp. 169-76.
70- Yeongdae Kim, Sorawit Stapornchaisit, Hiroyuki Kambara, Natsue Yoshimura, and Yasuharu Koike, "Muscle Synergy and Musculoskeletal Model‐Based Continuous Multi‐Dimensional Estimation of Wrist and Hand Motions." Journal of Healthcare Engineering, Vol. 2020 (No. 1), p. 5451219, (2020).
71- Eugen Diaconescu, "The use of NARX neural networks to predict chaotic time series." Wseas Transactions on computer research, Vol. 3 (No. 3), pp. 182-91, (2008).
72- Georg Dorffner, "Neural networks for time series processing." Neural network world, Vol. 6 (No. 4), pp. 447-68, (1996).
73- Tsungnan Lin, Bill G Horne, Peter Tino, and C Lee Giles, "Learning long-term dependencies in NARX recurrent neural networks." IEEE transactions on neural networks, Vol. 7 (No. 6), pp. 1329-38, (1996).
74- Zachary C Lipton, John Berkowitz, and Charles Elkan, "A critical review of recurrent neural networks for sequence learning." arXiv preprint arXiv:1506.00019, (2015).
75- José Maria P Menezes Jr and Guilherme A Barreto, "Long-term time series prediction with the NARX network: An empirical evaluation." Neurocomputing, Vol. 71 (No. 16-18), pp. 3335-43, (2008).
76- Mike Schuster and Kuldip K Paliwal, "Bidirectional recurrent neural networks." IEEE transactions on Signal Processing, Vol. 45 (No. 11), pp. 2673-81, (1997).
77- Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3555, (2014).
78- Daoming She and Minping Jia, "A BiGRU method for remaining useful life prediction of machinery." Measurement, Vol. 167p. 108277, (2021).
79- Andrea d'Avella, Alessandro Portone, Laure Fernandez, and Francesco Lacquaniti, "Control of fast-reaching movements by muscle synergy combinations." Journal of Neuroscience, Vol. 26 (No. 30), pp. 7791-810, (2006).
80- Petre Stoica and Randolph L Moses, Spectral analysis of signals. Pearson Prentice Hall Upper Saddle River, NJ, (2005).
81- Tomoko Nemoto and David Beglar, "Likert-scale questionnaires." in JALT 2013 conference proceedings, (2014), pp. 1-8.
82- Shigeki Kubota et al., "A newly developed upper limb single-joint HAL in a patient with elbow flexion reconstruction after traumatic brachial plexus injury: a case report." Interdisciplinary Neurosurgery, Vol. 10pp. 66-68, (2017).
83- Jiahao Chen and Hong Qiao, "Muscle-synergies-based neuromuscular control for motion learning and generalization of a musculoskeletal system." IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51 (No. 6), pp. 3993-4006, (2020).
84- Richard A Schmidt, Timothy D Lee, Carolee Winstein, Gabriele Wulf, and Howard N Zelaznik, Motor control and learning: A behavioral emphasis. Human kinetics, (2018).
85- Seyed Safavynia, Gelsy Torres-Oviedo, and Lena Ting, "Muscle synergies: implications for clinical evaluation and rehabilitation of movement." Topics in spinal cord injury rehabilitation, Vol. 17 (No. 1), pp. 16-24, (2011).
86- Yoshua Bengio, Patrice Simard, and Paolo Frasconi, "Learning long-term dependencies with gradient descent is difficult." IEEE transactions on neural networks, Vol. 5 (No. 2), pp. 157-66, (1994).
87- Xiaolei Liu, Zi Lin, and Ziming Feng, "Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM." Energy, Vol. 227p. 120492, (2021).
88- Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan, Geoffrey I Webb, Germain Forestier, and Mahsa Salehi, "Deep learning for time series classification and extrinsic regression: A current survey." ACM Computing Surveys, Vol. 56 (No. 9), pp. 1-45, (2024).
| Files | ||
| Issue | Articles in Press | |
| Section | Original Article(s) | |
| Keywords | ||
| Electromyography Muscle Synergy Arm Joint Dynamics. | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Zendehbad SA, Kobravi HR, Khalilzadeh MM, Sharifi Razavi A, Sasan Nezhad P. Identifying The Arm Joint Dynamics Using Muscle Synergy Patterns and SVMD-BiGRU Hybrid Mechanism. Frontiers Biomed Technol. 2024;.
