{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:50:28Z","timestamp":1726851028168},"reference-count":64,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:00:00Z","timestamp":1634688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019737","name":"Information Technology Industry Development Agency","doi-asserted-by":"publisher","award":["CFP149"],"id":[{"id":"10.13039\/501100019737","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (\u00b15.1%) with careful feature and classifier selection.<\/jats:p>","DOI":"10.3390\/s21216948","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T01:31:26Z","timestamp":1634779886000},"page":"6948","source":"Crossref","is-referenced-by-count":11,"title":["Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3874-3462","authenticated-orcid":false,"given":"Hussein","family":"Sarwat","sequence":"first","affiliation":[{"name":"Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt"}]},{"given":"Hassan","family":"Sarwat","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Ain Shams University, Cairo 11566, Egypt"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8641-9985","authenticated-orcid":false,"given":"Shady A.","family":"Maged","sequence":"additional","affiliation":[{"name":"Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt"}]},{"given":"Tamer H.","family":"Emara","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Ain Shams University, Cairo 11591, Egypt"}]},{"given":"Ahmed M.","family":"Elbokl","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Ain Shams University, Cairo 11591, Egypt"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0367-0187","authenticated-orcid":false,"given":"Mohammed Ibrahim","family":"Awad","sequence":"additional","affiliation":[{"name":"Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Westendorp, W.F., Nederkoorn, P.J., Vermeij, J.D., Dijkgraaf, M.G., and van de Beek, D. (2011). Post-stroke infection: A systematic review and meta-analysis. BMC Neurol., 11.","DOI":"10.1186\/1471-2377-11-110"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"634","DOI":"10.2471\/BLT.16.181636","article-title":"Stroke: A global response is needed","volume":"94","author":"Johnson","year":"2016","journal-title":"Bull. World Health Organ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1080\/096382899297684","article-title":"Disablement following stroke","volume":"21","author":"Mayo","year":"1999","journal-title":"Disabil. Rehabil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1586\/14737175.8.1.75","article-title":"Post-stroke depression","volume":"8","author":"Gaete","year":"2008","journal-title":"Expert Rev. Neurother."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1016\/S0140-6736(11)60325-5","article-title":"Stroke rehabilitation","volume":"377","author":"Langhorne","year":"2011","journal-title":"Lancet"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13","DOI":"10.2340\/1650197771331","article-title":"The poststroke hemiplegic patient. 1. a method for evaluation of physical performance","volume":"7","author":"Leyman","year":"1975","journal-title":"Scand. J. Rehabil. Med."},{"key":"ref_7","unstructured":"Goodglass, H., and Kaplan, E. (1972). The Assessment of Aphasia and Related Disorders, Lea & Febiger."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105182","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105182","article-title":"Care for patients with stroke during the COVID-19 pandemic: Physical therapy and rehabilitation suggestions for preventing secondary stroke","volume":"29","author":"Wang","year":"2020","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"iv3","DOI":"10.1136\/jnnp.74.suppl_4.iv3","article-title":"Principles of neurological rehabilitation","volume":"74","author":"Barnes","year":"2003","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/09638280600756257","article-title":"Comparing contents of functional outcome measures in stroke rehabilitation using the International Classification of Functioning, Disability and Health","volume":"29","author":"Schepers","year":"2007","journal-title":"Disabil. Rehabil."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-13-94","article-title":"Machine learning, medical diagnosis, and biomedical engineering research-commentary","volume":"13","author":"Foster","year":"2014","journal-title":"Biomed. Eng. Online"},{"key":"ref_12","first-page":"408","article-title":"Application of machine learning to medical diagnosis","volume":"389","author":"Kononenko","year":"1997","journal-title":"Mach. Learn. Data Min. Methods Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1055\/s-0038-1634196","article-title":"Acceptance of rules generated by machine learning among medical experts","volume":"40","author":"Pazzani","year":"2001","journal-title":"Methods Inf. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"520","DOI":"10.11124\/JBISRIR-2017-003896","article-title":"The economic cost of robotic rehabilitation for adult stroke patients: A systematic review","volume":"17","author":"Lo","year":"2019","journal-title":"JBI Database Syst. Rev. Implement. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1109\/JIOT.2018.2803201","article-title":"Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes","volume":"5","author":"Verma","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Qin, Y., Kim, I., and Wang, Y. (2017, January 11\u201315). Towards an IoT-based upper limb rehabilitation assessment system. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8037343"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schapire, R.E. (2013). Explaining adaboost. Empirical Inference, Springer.","DOI":"10.1007\/978-3-642-41136-6_5"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107896","DOI":"10.1016\/j.measurement.2020.107896","article-title":"An automated system for motor function assessment in stroke patients using motion sensing technology: A pilot study","volume":"161","author":"Sheng","year":"2020","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1555008","DOI":"10.1142\/S0218001415550083","article-title":"A survey of applications and human motion recognition with microsoft kinect","volume":"29","author":"Lun","year":"2015","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1002\/lio2.354","article-title":"Automated assessment of psychiatric disorders using speech: A systematic review","volume":"5","author":"Low","year":"2020","journal-title":"Laryngoscope Investig. Otolaryngol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1109\/TNSRE.2019.2939587","article-title":"Cellphone-Based Automated Fugl-Meyer Assessment to Evaluate Upper Extremity Motor Function After Stroke","volume":"27","author":"Song","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"20097","DOI":"10.3390\/s150820097","article-title":"A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study","volume":"15","author":"Otten","year":"2015","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"De-la Torre, R., O\u00f1a, E.D., Balaguer, C., and Jard\u00f3n, A. (2020). Robot-aided systems for improving the assessment of upper limb spasticity: A systematic review. Sensors, 20.","DOI":"10.3390\/s20185251"},{"key":"ref_24","first-page":"9758939","article-title":"A review of robotics in neurorehabilitation: Towards an automated process for upper limb","volume":"2018","author":"Balaguer","year":"2018","journal-title":"J. Healthc. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"32352","DOI":"10.1109\/ACCESS.2019.2901814","article-title":"Review of automated systems for upper limbs functional assessment in neurorehabilitation","volume":"7","author":"Baeza","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sarwat, H., Sarwat, H., Awad, M.I., and Maged, S.A. (2020, January 15\u201316). Assessment of Post-Stroke Patients Using Smartphones and Gradient Boosting. Proceedings of the 2020 15th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt.","DOI":"10.1109\/ICCES51560.2020.9334654"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"El-Agroudy, M.N., Gaber, M., Joseph, D., Ibrahim, M., Amin, M., Helmy, D., Hanafy, M., Hisham, S., Awad, M.I., and Youssef, A.R. (2020, January 8\u20139). Assistive Exoskeleton Hand Glove. Proceedings of the 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), Aswan, Egypt.","DOI":"10.1109\/ITCE48509.2020.9047803"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/TNSRE.2017.2720727","article-title":"Data glove system embedded with inertial measurement units for hand function evaluation in stroke patients","volume":"25","author":"Lin","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Luzhnica, G., Simon, J., Lex, E., and Pammer, V. (2016, January 19\u201320). A sliding window approach to natural hand gesture recognition using a custom data glove. Proceedings of the 2016 IEEE Symposium on 3D User Interfaces (3DUI), Greenville, SC, USA.","DOI":"10.1109\/3DUI.2016.7460035"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1109\/TIM.2003.809484","article-title":"Data glove with a force sensor","volume":"52","author":"Tarchanidis","year":"2003","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"012032","DOI":"10.1088\/1757-899X\/403\/1\/012032","article-title":"Flex sensors and MPU6050 sensors responses on smart glove for sign language translation","volume":"Volume 403","author":"Yudhana","year":"2018","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"ref_32","unstructured":"SpectraSymbol (2021, October 11). Flex Sensor Special Edition. Rev. A., Available online: https:\/\/www.sparkfun.com\/datasheets\/Sensors\/Flex\/flex22.pdf."},{"key":"ref_33","unstructured":"Interlink Electronics, Inc. (2021, October 11). FSR 400 Data Sheet. Rev. A., Available online: https:\/\/cdn.shopify.com\/s\/files\/1\/0672\/9409\/files\/force-sensitive-resistor-DataSheet-FSR400.pdf?v=1616803927."},{"key":"ref_34","unstructured":"InvenSense (2021, October 11). MPU-6000 and MPU-6050 Product Specification. Rev. D., Available online: http:\/\/dlnmh9ip6v2uc.cloudfront.net\/datasheets\/Components\/General%20IC\/PS-MPU-6000A.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/BF01189220","article-title":"An overview of median and stack filtering","volume":"11","author":"Gabbouj","year":"1992","journal-title":"Circuits, Syst. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hauser, N., and Wade, E. (2018, January 18\u201321). Detecting reach to grasp activities using motion and muscle activation data. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512937"},{"key":"ref_37","unstructured":"Chung, W.Y., Purwar, A., and Sharma, A. (2008, January 20\u201325). Frequency domain approach for activity classification using accelerometer. Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada."},{"key":"ref_38","unstructured":"Pokress, S.C., and Veiga, J.J.D. (2013). MIT App Inventor: Enabling personal mobile computing. arXiv."},{"key":"ref_39","unstructured":"Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python, O\u2019Reilly Media, Inc."},{"key":"ref_40","unstructured":"Kemp, C., and Gyger, B. (2013). Professional Heroku Programming, John Wiley & Sons."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Moroney, L. (2017). The firebase realtime database. The Definitive Guide to Firebase, Springer.","DOI":"10.1007\/978-1-4842-2943-9"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1109\/TPAMI.2013.159","article-title":"Learning nonlinear functions using regularized greedy forest","volume":"36","author":"Johnson","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/BF01584082","article-title":"Matroids and the greedy algorithm","volume":"1","author":"Edmonds","year":"1971","journal-title":"Math. Program."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley & Sons.","DOI":"10.1002\/9781118548387"},{"key":"ref_46","unstructured":"Weisstein, E.W. (2021, September 15). Bernoulli Distribution. Available online: https:\/\/mathworld.wolfram.com\/BernoulliDistribution.html."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Frykberg, G.E., Grip, H., and Alt-Murphy, M. (2021). How Many Trials Are Needed in Kinematic Analysis of a Reach-to-Grasp Task?-a Study in Persons with Stroke and Non-Disabled Controls. J. Neuroeng. Rehabil.","DOI":"10.21203\/rs.3.rs-278551\/v1"},{"key":"ref_48","unstructured":"Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., and Ridella, S. (2012). The\u2019K\u2019in K-Fold Cross Validation, ESANN."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/S0140-6736(17)31447-2","article-title":"Family-led rehabilitation after stroke in India (ATTEND): A randomised controlled trial","volume":"390","author":"Lindley","year":"2017","journal-title":"Lancet"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.jstrokecerebrovasdis.2012.12.003","article-title":"Family conferences in stroke rehabilitation: A literature review","volume":"22","author":"Loupis","year":"2013","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1177\/104438947205300904","article-title":"Role of the family in rehabilitation","volume":"53","author":"Shellhase","year":"1972","journal-title":"Soc. Casework"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1177\/1545968314553030","article-title":"Interrater reliability of the Wolf Motor Function Test\u2013Functional Ability Scale: Why it matters","volume":"29","author":"Duff","year":"2015","journal-title":"Neurorehabilit. Neural Repair"},{"key":"ref_53","first-page":"789","article-title":"Principles and techniques of the Brunnstrom approach to the treatment of hemiplegia","volume":"46","author":"Perry","year":"1967","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1155\/2015\/818243","article-title":"On training efficiency and computational costs of a feed forward neural network: A review","volume":"2015","author":"Laudani","year":"2015","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Obdr\u017e\u00e1lek, \u0160., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., and Pavel, M. (September, January 28). Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346149"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_57","unstructured":"MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/TNSRE.2017.2755667","article-title":"Automated evaluation of upper-limb motor function impairment using Fugl-Meyer assessment","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Olesh, E.V., Yakovenko, S., and Gritsenko, V. (2014). Automated assessment of upper extremity movement impairment due to stroke. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0104487"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kim, W.S., Cho, S., Baek, D., Bang, H., and Paik, N.J. (2016). Upper extremity functional evaluation by Fugl-Meyer assessment scoring using depth-sensing camera in hemiplegic stroke patients. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0158640"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Julianjatsono, R., Ferdiana, R., and Hartanto, R. (2017, January 11\u201312). High-resolution automated Fugl-Meyer Assessment using sensor data and regression model. Proceedings of the 2017 3rd International Conference on Science and Technology-Computer (ICST), Yogyakarta, Indonesia.","DOI":"10.1109\/ICSTC.2017.8011847"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MWC.001.1900125","article-title":"When eHealth meets IoT: A smart wireless system for poststroke home rehabilitation","volume":"26","author":"Bisio","year":"2019","journal-title":"IEEE Wirel. Commun."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/JIOT.2016.2628938","article-title":"Enabling IoT for in-home rehabilitation: Accelerometer signals classification methods for activity and movement recognition","volume":"4","author":"Bisio","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"E946","DOI":"10.1503\/cmaj.201197","article-title":"For-profit long-term care homes and the risk of COVID-19 outbreaks and resident deaths","volume":"192","author":"Stall","year":"2020","journal-title":"Cmaj"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6948\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T12:50:10Z","timestamp":1721479810000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6948"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,20]]},"references-count":64,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21216948"],"URL":"https:\/\/doi.org\/10.3390\/s21216948","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,20]]}}}