{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T22:58:21Z","timestamp":1790290701354,"version":"4.1.0"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and ICT, Korea","award":["NRF-2020R1A2B5B02002478"],"award-info":[{"award-number":["NRF-2020R1A2B5B02002478"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild to severe. Autistic children use fewer communicative gestures compared with typically developing children (TD). With time, the parents may learn their gestures and understand what is occurring in their child\u2019s mind. However, it is difficult for other people to understand their gestures. In this paper, we propose a wearable-sensors-based platform to recognize autistic gestures using various classification techniques. The proposed system defines, monitors, and classifies the gestures of the individuals. We propose using wearable sensors that transmit their data using a Bluetooth interface to a data acquisition and classification server. A dataset of 24 gestures is created by 10 autistic children performing each gesture about 10 times. Time- and frequency-domain features are extracted from the sensors\u2019 data, which are classified using k-nearest neighbor (KNN), decision tree, neural network, and random forest models. The main objective of this work is to develop a wearable-sensor-based IoT platform for gesture recognition in children with autism spectrum disorder (ASD). We achieve an accuracy of about 91% with most of the classifiers using dataset cross-validation and leave-one-person-out cross-validation.<\/jats:p>","DOI":"10.3390\/s21103319","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T11:30:16Z","timestamp":1620732616000},"page":"3319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms"],"prefix":"10.3390","volume":"21","author":[{"given":"Uzma Abid","family":"Siddiqui","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-8353","authenticated-orcid":false,"given":"Farman","family":"Ullah","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asif","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6441-4139","authenticated-orcid":false,"given":"Ajmal","family":"Khan","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4889-7115","authenticated-orcid":false,"given":"Rehmat","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheroz","family":"Paracha","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Shahzad","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9559-4352","authenticated-orcid":false,"given":"Kyung-Sup","family":"Kwak","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"ref_1","unstructured":"(2020, December 10). National Institute of Mental Health. Autism Spectrum Disorder, Available online: https:\/\/www.nimh.nih.gov\/health\/topics\/autism-spectrum-disorders-asd\/index.shtml."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Amiri, A., Peltier, N., Goldberg, C., Sun, Y., Nathan, A., Hiremath, S., and Mankodiya, K. (2017). WearSense: Detecting Autism stereotypic behaviors through smartwatches. Healthcare, 5.","DOI":"10.3390\/healthcare5010011"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alwakeel, S.S., Alhalabi, B., Aggoune, H., and Alwakeel, M. (2015, January 9\u201311). A machine learning based WSN system for autism activity recognition. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning ICMLA 2015, Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.46"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.jaac.2017.03.013","article-title":"What is the male-to-female ratio in Autism Spectrum Disorder? A systematic review and meta-analysis","volume":"56","author":"Loomes","year":"2017","journal-title":"J. Am. Acad. Child Adolesc. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1017\/S0305000900008989","article-title":"Gestures and words during the transition to two-word speech","volume":"23","author":"Capirci","year":"1996","journal-title":"J. Child Lang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1146\/annurev-psych-113011-143802","article-title":"Gesture\u2019s role in speaking, learning, and creating language","volume":"64","author":"Alibali","year":"2013","journal-title":"Annu. Rev. Psychol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neurenf.2018.03.002","article-title":"Comprehension of conventional gestures in typical children, children with autism spectrum disorders and children with language disorders","volume":"67","author":"Perrault","year":"2019","journal-title":"Neuropsychiatr. Enfance. Adolesc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/MPUL.2016.2592260","article-title":"Wearables and the internet of things for health: Wearable, interconnected devices promise more efficient and comprehensive health care","volume":"7","author":"Metcalf","year":"2016","journal-title":"IEEE Pulse"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/JBHI.2013.2293059","article-title":"Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors","volume":"18","author":"Clifton","year":"2014","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., Patel, V., and Haines, A. (2013). The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: A systematic review. PLoS Med., 10.","DOI":"10.1371\/journal.pmed.1001362"},{"key":"ref_11","unstructured":"Shoaib, M. (2021, April 10). Sitting Is the New Smoking: Online Complex Human Activity Recognition with Smartphones and Wearables. Available online: https:\/\/research.utwente.nl\/en\/publications\/sitting-is-the-new-smoking-online-complex-human-activity-recognit."},{"key":"ref_12","unstructured":"Lowry, B.L. (2021, May 10). \u201cTuning In\u201d to Others: How Young Children Develop Theory of Mind. Available online: http:\/\/www.hanen.org\/Helpful-Info\/Articles\/Tuning-In-to-Others-How-Young-Children-Develop.aspx."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1016\/S0140-6736(09)61376-3","article-title":"Autism","volume":"374","author":"Levy","year":"2009","journal-title":"Lancet"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/S0003-3472(05)80640-2","article-title":"Stereotypies: A critical review","volume":"41","author":"Behaviour","year":"1991","journal-title":"Anim. Behav."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Camada, M.Y.O., St, D., and Conceic, G.S. (2016, January 29\u201331). Recognition of affective state for austist from stereotyped gestures. Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics, Lisbon, Portugal.","DOI":"10.5220\/0005983201970204"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1002\/dac.3010","article-title":"Smartphone-based activity recognition independent of device orientation and placement","volume":"29","author":"Guo","year":"2015","journal-title":"Int. J. Commun. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.sigpro.2017.10.011","article-title":"Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders","volume":"144","author":"Kia","year":"2018","journal-title":"Signal Processing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1093\/deafed\/eni037","article-title":"Perception of sign language and its application to visual communications for deaf people","volume":"10","author":"Muir","year":"2005","journal-title":"J. Deaf Stud. Deaf Educ."},{"key":"ref_19","unstructured":"Piana, S., Staglian\u00f2, A., Camurri, A., and Odone, F. (2021, April 15). A Set of Full-Body Movement Features for Emotion Recognition to Help Children Affected by Autism Spectrum Condition. Available online: http:\/\/www.fdg2013.org\/program\/workshops\/papers\/IDGEI2013\/idgei2013_4.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rad, N.M., Bizzego, A., Kia, S.M., Jurman, G., Venuti, P., and Furlanello, C. (2015). Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism. arXiv, 15\u201319.","DOI":"10.1109\/ICDMW.2016.0076"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1007\/s10803-011-1243-8","article-title":"The effects of face expertise training on the behavioral performance and brain activity of adults with high functioning autism spectrum disorders","volume":"42","author":"Faja","year":"2012","journal-title":"J. Autism Dev. Disord."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, N., Costa, S., Rodrigues, J., and Soares, F. (2014, January 14\u201315). Detection of stereotyped hand flapping movements in Autistic children using the Kinect sensor: A case study. Proceedings of the 2014 IEEE International Conference on Autonomous Robot Systems and Competitions ICARSC 2014, Espinho, Portugal.","DOI":"10.1109\/ICARSC.2014.6849788"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Incertis, I.G., Garc\u00eda-Bermejo, J.G., and Casanova, E.Z. (2006, January 20\u201324). Hand gesture recognition for deaf people interfacing. Proceedings of the International Conference on Pattern Recognition, Hong Kong, China.","DOI":"10.1109\/ICPR.2006.619"},{"key":"ref_24","unstructured":"\u00c1ngel-L\u00f3pez, J.P., and de la Pe\u00f1a, N.A. (2017). Proceedings of the VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, 26\u201328 October 2016, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sombandith, V., Walairacht, A., and Walairacht, S. (2017, January 27\u201330). Hand gesture recognition for Lao alphabet sign language using HOG and correlation. Proceedings of the ECTI-CON 2017\u20142017 14th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Phuket, Thailand.","DOI":"10.1109\/ECTICon.2017.8096321"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/7333.928571","article-title":"A reliable gait phase detection system","volume":"9","author":"Pappas","year":"2001","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, F.J., and Solmaz, G. (2016, January 12\u201314). We hear your activities through Wi-Fi signals. Proceedings of the 2016 IEEE 3rd World Forum Internet Things, WF-IoT 2016, Reston, VA, USA.","DOI":"10.1109\/WF-IoT.2016.7845478"},{"key":"ref_28","first-page":"157","article-title":"Hand gesture recognition for sign language: A new higher order fuzzy HMM approach","volume":"8","author":"Darwish","year":"2016","journal-title":"Int. J. Eng. Technol."},{"key":"ref_29","first-page":"97","article-title":"Classification of sign language gestures using machine learning","volume":"8","author":"Bhattacharya","year":"2020","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1016\/j.procs.2018.04.095","article-title":"Recognition of basketball referee signals from videos using histogram of oriented gradients (HOG) and support vector machine (SVM)","volume":"130","author":"Raudonis","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1109\/TBME.2011.2178070","article-title":"Multisensor data fusion for physical activity assessment","volume":"59","author":"Liu","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2656","DOI":"10.1109\/TBME.2011.2160723","article-title":"Identifying types of physical activity with a single accelerometer: Evaluating laboratory-trained algorithms in daily life","volume":"58","author":"Gyllensten","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.jpdc.2018.08.010","article-title":"A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system","volume":"123","author":"Comput","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhou, S., Liu, Y., and Li, K. (2018, January 18\u201321). Recognition of Multi-scale Multi-angle Gestures Based on HOG-LBP Feature. Proceedings of the 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018, Singapore.","DOI":"10.1109\/ICARCV.2018.8581098"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yao, R., Lin, G., Shi, Q., and Ranasinghe, D.C. (2017). Efficient dense labelling of human activity sequences from wearables using fully convolutional networks rui. Pattern Recognit.","DOI":"10.1016\/j.patcog.2017.12.024"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vaitkevi\u010dius, A., Taroza, M., Bla\u017eauskas, T., Dama\u0161evi\u010dius, R., Maskeliunas, R., and Wo\u017aniak, M. (2019). Recognition of American sign language gestures in a virtual reality using leap motion. Appl. Sci., 9.","DOI":"10.3390\/app9030445"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mukherjee, A., Misra, S., Mangrulkar, P., Rajarajan, M., and Rahulamathavan, Y. (2017, January 17\u201320). SmartARM: A smartphone-based group activity recognition and monitoring scheme for military applications. Proceedings of the 11th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2017, Bhubaneswar, India.","DOI":"10.1109\/ANTS.2017.8384149"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Farooq, M., and Sazonov, E. (2017, January 4\u20136). Real time monitoring and recognition of eating and physical activity with a wearable device connected to the eyeglass. Proceedings of the Eleventh International Conference on Sensing Technology, ICST, Sydney, Australia.","DOI":"10.1109\/ICSensT.2017.8304420"},{"key":"ref_40","first-page":"200","article-title":"Alphabet hand gesture recognition using histogram of oriented gradients, support vector machine and K-nearest neighbor algorithm","volume":"6","author":"Ramos","year":"2019","journal-title":"Int. Res. J. Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.procs.2015.12.276","article-title":"A new data glove approach for Malaysian sign language detection","volume":"76","author":"Shukor","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nasri, N., Orts-Escolano, S., and Cazorla, M. (2020). An semg-controlled 3d game for rehabilitation therapies: Real-time time hand gesture recognition using deep learning techniques. Sensors, 20.","DOI":"10.3390\/s20226451"},{"key":"ref_43","first-page":"419","article-title":"Sign language gesture recognition using HMM","volume":"10255 LNCS","author":"Parcheta","year":"2017","journal-title":"Lect. Notes Comput. Sci. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mannini, A., Rosenberger, M., Haskell, W.L., Sabatini, A.M., and Intille, S.S. (2017). Activity recognition in youth using single accelerometer placed at wrist or ankle. Med. Sci. Sports Exerc.","DOI":"10.1249\/MSS.0000000000001144"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Krishnan, K.S., Saha, A., Ramachandran, S., and Kumar, S. (2017, January 5\u20137). Recognition of human arm gestures using Myo armband for the game of hand cricket. Proceedings of the 2017 IEEE 5th International Symposium on Robotics and Intelligent Sensors, IRIS 2017, Ottawa, ON, Canada.","DOI":"10.1109\/IRIS.2017.8250154"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Thabtah, F., Kamalov, F., and Rajab, K. (2018). A new computational intelligence approach to detect autistic features for autism screening. Int. J. Med. Inform.","DOI":"10.1016\/j.ijmedinf.2018.06.009"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.procs.2020.03.399","article-title":"Analysis and detection of autism spectrum disorder using machine learning techniques","volume":"167","author":"Raj","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Iqbal, A., Ullah, F., Anwar, H., Ur Rehman, A., Shah, K., Baig, A., Ali, S., Yoo, S., and Kwak, K.S. (2020). Wearable internet-of-things platform for human activity recognition and health care. Int. J. Distrib. Sens. Netw., 16.","DOI":"10.1177\/1550147720911561"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., and Greer, K. (2003). KNN model-based approach in classification. On the Move to Meaningful Internet Systems, Springer.","DOI":"10.1007\/978-3-540-39964-3_62"},{"key":"ref_50","unstructured":"Yoav, F., and Mason, L. (1999, January 27\u201330). The alternating decision tree learning algorithm. Proceedings of the ICML, Bled, Slovenia."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"Volume 45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"ref_52","unstructured":"Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. Neural Networks for Perception, Academic Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:59:10Z","timestamp":1760162350000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,11]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21103319"],"URL":"https:\/\/doi.org\/10.3390\/s21103319","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,11]]}}}