{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:35:23Z","timestamp":1771698923213,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH\/NICHD","award":["P2C HD101913"],"award-info":[{"award-number":["P2C HD101913"]}]},{"name":"NIH\/NICHD","award":["P20 GM109040"],"award-info":[{"award-number":["P20 GM109040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Upper extremity hemiplegia is a serious problem affecting the lives of many people post-stroke. Motor recovery requires high repetitions and quality of task-specific practice. Sufficient practice cannot be completed during therapy sessions, requiring patients to perform additional task practices at home on their own. Adherence to and quality of these home task practices are often limited, which is likely a factor reducing rehabilitation effectiveness post-stroke. However, home adherence is typically measured by self-reports that are known to be inconsistent with objective measurement. The objective of this study was to develop algorithms to enable the objective identification of task type and quality. Twenty neurotypical participants wore an IMU sensor on the wrist and performed four representative tasks in prescribed fashions that mimicked correct, compensatory, and incomplete movement qualities typically seen in stroke survivors. LSTM classifiers were trained to identify the task being performed and its movement quality. Our models achieved an accuracy of 90.8% for task identification and 84.9%, 81.1%, 58.4%, and 73.2% for movement quality classification for the four tasks for unseen participants. The results warrant further investigation to determine the classification performance for stroke survivors and if quantity and quality feedback from objective monitoring facilitates effective task practice at home, thereby improving motor recovery.<\/jats:p>","DOI":"10.3390\/s23136110","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T01:42:47Z","timestamp":1688434967000},"page":"6110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice"],"prefix":"10.3390","volume":"23","author":[{"given":"Mingqi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Computing, Clemson University, Clemson, SC 29634, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3861-1371","authenticated-orcid":false,"given":"Gabrielle","family":"Scronce","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Ralph H. Johnson VA Health Care System, Charleston, SC 29401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Finetto","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristen","family":"Coupland","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Ralph H. Johnson VA Health Care System, Charleston, SC 29401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing, Clemson University, Clemson, SC 29634, USA"},{"name":"Summer Intern, Research Experience for Undergraduates, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melanie E.","family":"Lambert","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing, Clemson University, Clemson, SC 29634, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7483-0360","authenticated-orcid":false,"given":"Adam","family":"Baker","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Ralph H. Johnson VA Health Care System, Charleston, SC 29401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing, Clemson University, Clemson, SC 29634, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6446-5905","authenticated-orcid":false,"given":"Na Jin","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Ralph H. Johnson VA Health Care System, Charleston, SC 29401, USA"},{"name":"Department of Rehabilitation Sciences, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E153","DOI":"10.1161\/CIR.0000000000001052","article-title":"Heart Disease and Stroke Statistics-2022 Update: A Report from the American Heart Association","volume":"145","author":"Tsao","year":"2022","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1053\/apmr.2002.35473","article-title":"Motor Recovery after Stroke: A Systematic Review of the Literature","volume":"83","author":"Hendricks","year":"2002","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1161\/01.STR.32.6.1279","article-title":"Estimates of the Prevalence of Acute Stroke Impairments and Disability in a Multiethnic Population","volume":"32","author":"Lawrence","year":"2001","journal-title":"Stroke"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21S","DOI":"10.1177\/1545968311410941","article-title":"Neurological Principles and Rehabilitation of Action Disorders","volume":"25","author":"Sathian","year":"2011","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1056\/NEJMcp043511","article-title":"Rehabilitation after Stroke","volume":"352","author":"Dobkin","year":"2005","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1177\/1545968313481279","article-title":"Transfer of Training Between Distinct Motor Tasks After Stroke","volume":"27","author":"Schaefer","year":"2013","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1161\/STROKEAHA.114.004695","article-title":"Is More Better? Using Metadata to Explore Dose\u2013Response Relationships in Stroke Rehabilitation","volume":"45","author":"Lohse","year":"2014","journal-title":"Stroke"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1161\/STROKEAHA.118.023603","article-title":"Dosage Matters","volume":"50","author":"Winstein","year":"2019","journal-title":"Stroke"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1016\/j.apmr.2009.04.005","article-title":"Observation of Amounts of Movement Practice Provided during Stroke Rehabilitation","volume":"90","author":"Lang","year":"2009","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1002\/ana.24734","article-title":"Dose Response of Task-specific Upper Limb Training in People at Least 6 Months Poststroke: A Phase II, Single-blind, Randomized, Controlled Trial","volume":"80","author":"Lang","year":"2016","journal-title":"Ann. Neurol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.jphys.2016.08.006","article-title":"Increasing the Amount of Usual Rehabilitation Improves Activity after Stroke: A Systematic Review","volume":"62","author":"Schneider","year":"2016","journal-title":"J. Physiother."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1080\/10749357.2016.1200292","article-title":"Exercise after Stroke: Patient Adherence and Beliefs after Discharge from Rehabilitation","volume":"24","author":"Miller","year":"2017","journal-title":"Top. Stroke Rehabil."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1177\/0269215518811903","article-title":"A Systematic Review of Measures of Adherence to Physical Exercise Recommendations in People with Stroke","volume":"33","author":"Levy","year":"2019","journal-title":"Clin. Rehabil."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/10749357.2019.1707950","article-title":"Home Program Practices for Supporting and Measuring Adherence in Post-Stroke Rehabilitation: A Scoping Review","volume":"27","author":"Nolfi","year":"2020","journal-title":"Top. Stroke Rehabil."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1016\/j.apmr.2017.12.025","article-title":"Comparison of Self-Report versus Sensor-Based Methods for Measuring the Amount of Upper Limb Activity Outside the Clinic","volume":"99","author":"Waddell","year":"2018","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1177\/1545968306298414","article-title":"Improvement of Arm Movement Patterns and Endpoint Control Depends on Type of Feedback during Practice in Stroke Survivors","volume":"21","author":"Cirstea","year":"2007","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/nrn.2017.26","article-title":"Motor Compensation and Its Effects on Neural Reorganization after Stroke","volume":"18","author":"Jones","year":"2017","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e1827","DOI":"10.1002\/pri.1827","article-title":"Measurement of Adherence to Home-Based Exercises among Community-Dwelling Stroke Survivors in India","volume":"25","author":"Mahmood","year":"2020","journal-title":"Physiother. Res. Int."},{"key":"ref_19","first-page":"3682898","article-title":"Effect of Self-Directed Home Therapy Adherence Combined with TheraBracelet on Post-Stroke Hand Recovery: A Pilot Study","volume":"2023","author":"Scronce","year":"2023","journal-title":"Stroke Res. Treat."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Martino Cinnera, A., Picerno, P., Bisirri, A., Koch, G., Morone, G., and Vannozzi, G. Upper Limb Assessment with Inertial Measurement Units According to the International Classification of Functioning in Stroke: A Systematic Review and Correlation Meta-Analysis. Top. Stroke Rehabil., 2023.","DOI":"10.1080\/10749357.2023.2197278"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2100411","DOI":"10.1109\/JTEHM.2018.2829208","article-title":"Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training","volume":"6","author":"Lee","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s42234-020-00053-5","article-title":"Determining Grasp Selection from Arm Trajectories via Deep Learning to Enable Functional Hand Movement in Tetraplegia","volume":"6","author":"Bhagat","year":"2020","journal-title":"Bioelectron. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1109\/TNSRE.2022.3197993","article-title":"Monitoring Arm Movements Post-Stroke for Applications in Rehabilitation and Home Settings","volume":"30","author":"Eng","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"van den Tillaar, R., Bhandurge, S., and Stewart, T. (2021). Can Machine Learning with Imus Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball?. Sensors, 21.","DOI":"10.3390\/s21072288"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5860","DOI":"10.1038\/s41598-020-61789-3","article-title":"High-Resolution Motor State Detection in Parkinson\u2019s Disease Using Convolutional Neural Networks","volume":"10","author":"Pfister","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103556","DOI":"10.1016\/j.apergo.2021.103556","article-title":"Prediction of Slaughterhouse Workers\u2019 RULA Scores and Knife Edge Using Low-Cost Inertial Measurement Sensor Units and Machine Learning Algorithms","volume":"98","author":"Villalobos","year":"2022","journal-title":"Appl. Ergon."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2880","DOI":"10.1016\/j.jstrokecerebrovasdis.2017.07.004","article-title":"Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning","volume":"26","author":"Bochniewicz","year":"2017","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3200\/JMBR.36.2.212-224","article-title":"Challenge Point: A Framework for Conceptualizing the Effects of Various Practice Conditions in Motor Learning","volume":"36","author":"Guadagnoli","year":"2004","journal-title":"J. Mot. Behav."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1161\/01.STR.0000196940.20446.c9","article-title":"Task-Specific Training with Trunk Restraint on Arm Recovery in Stroke: Randomized Control Trial","volume":"37","author":"Michaelsen","year":"2006","journal-title":"Stroke"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1985). Learning Internal Representations by Error Propagation, MIT Press.","DOI":"10.21236\/ADA164453"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_32","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_33","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_34","first-page":"2079","article-title":"On Over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1080\/09638280500534937","article-title":"Extrinsic Feedback for Motor Learning after Stroke: What Is the Evidence?","volume":"28","author":"Wulf","year":"2006","journal-title":"Disabil. Rehabil."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1177\/00315125211036413","article-title":"Optimizing Feedback Frequency in Motor Learning: Self-Controlled and Moderate Frequency KR Enhance Skill Acquisition","volume":"128","author":"Hebert","year":"2021","journal-title":"Percept. Mot. Skills"},{"key":"ref_38","unstructured":"Prettenhofer, P., and Stein, B. (2010). Cross-Language Text Classification Using Structural Correspondence Learning, Association for Computational Linguistics."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kulis, B., Saenko, K., and Darrell, T. (2011, January 20\u201325). What You Saw Is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995702"},{"key":"ref_40","first-page":"2213","article-title":"Hybrid Heterogeneous Transfer Learning through Deep Learning","volume":"28","author":"Zhou","year":"2014","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nam, J., and Kim, S. (September, January 30). Heterogeneous Defect Prediction. Proceedings of the 2015 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, Bergamo, Italy.","DOI":"10.1145\/2786805.2786814"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Al Ghamdi, M., Li, M., Abdel-Mottaleb, M., and Shousha, M.A. (2019, January 12\u201317). Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682915"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1017\/BrImp.2015.21","article-title":"Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use after Stroke","volume":"17","author":"Hayward","year":"2016","journal-title":"Brain Impair."},{"key":"ref_44","unstructured":"Lang, C.E., and Birkenmeier, R.L. (2013). Upper-Extremity Task-Specific Training after Stroke or Disability, AOTA Press."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"444","DOI":"10.5014\/ajot.2014.011619","article-title":"Feasibility of High-Repetition, Task-Specific Training for Individuals with Upper-Extremity Paresis","volume":"68","author":"Waddell","year":"2014","journal-title":"Am. J. Occup. Ther."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1177\/1545968310361957","article-title":"Translating Animal Doses of Task-Specific Training to People with Chronic Stroke in 1-Hour Therapy Sessions: A Proof-of-Concept Study","volume":"24","author":"Birkenmeier","year":"2010","journal-title":"Neurorehabil. Neural Repair"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:05:00Z","timestamp":1760126700000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,3]]},"references-count":46,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136110"],"URL":"https:\/\/doi.org\/10.3390\/s23136110","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,3]]}}}