{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T00:05:25Z","timestamp":1773446725101,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Government - Research Training Program","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Clinicians lack objective means for monitoring if their knee osteoarthritis patients are improving outside of the clinic (e.g., at home). Previous human activity recognition (HAR) models using wearable sensor data have only used data from healthy people and such models are typically imprecise for people who have medical conditions affecting movement. HAR models designed for people with knee osteoarthritis have classified rehabilitation exercises but not the clinically relevant activities of transitioning from a chair, negotiating stairs and walking, which are commonly monitored for improvement during therapy for this condition. Therefore, it is unknown if a HAR model trained on data from people who have knee osteoarthritis can be accurate in classifying these three clinically relevant activities. Therefore, we collected inertial measurement unit (IMU) data from 18 participants with knee osteoarthritis and trained convolutional neural network models to identify chair, stairs and walking activities, and phases. The model accuracy was 85% at the first level of classification (activity), 89\u201397% at the second (direction of movement) and 60\u201367% at the third level (phase). This study is the first proof-of-concept that an accurate HAR system can be developed using IMU data from people with knee osteoarthritis to classify activities and phases of activities.<\/jats:p>","DOI":"10.3390\/s21103381","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T22:46:14Z","timestamp":1620859574000},"page":"3381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Human Activity Recognition for People with Knee Osteoarthritis\u2014A Proof-of-Concept"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9728-3128","authenticated-orcid":false,"given":"Jay-Shian","family":"Tan","sequence":"first","affiliation":[{"name":"School of Allied Health, Faculty of Health Sciences, Curtin University, Perth 6845, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1826-6184","authenticated-orcid":false,"given":"Behrouz Khabbaz","family":"Beheshti","sequence":"additional","affiliation":[{"name":"Curtin Institute for Computation, Curtin University, Perth 6845, Australia"}]},{"given":"Tara","family":"Binnie","sequence":"additional","affiliation":[{"name":"School of Allied Health, Faculty of Health Sciences, Curtin University, Perth 6845, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2119-8066","authenticated-orcid":false,"given":"Paul","family":"Davey","sequence":"additional","affiliation":[{"name":"School of Allied Health, Faculty of Health Sciences, Curtin University, Perth 6845, Australia"}]},{"given":"J. P.","family":"Caneiro","sequence":"additional","affiliation":[{"name":"School of Allied Health, Faculty of Health Sciences, Curtin University, Perth 6845, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2429-9233","authenticated-orcid":false,"given":"Peter","family":"Kent","sequence":"additional","affiliation":[{"name":"School of Allied Health, Faculty of Health Sciences, Curtin University, Perth 6845, Australia"}]},{"given":"Anne","family":"Smith","sequence":"additional","affiliation":[{"name":"School of Allied Health, Faculty of Health Sciences, Curtin University, Perth 6845, Australia"}]},{"given":"Peter","family":"O\u2019Sullivan","sequence":"additional","affiliation":[{"name":"School of Allied Health, Faculty of Health Sciences, Curtin University, Perth 6845, Australia"}]},{"given":"Amity","family":"Campbell","sequence":"additional","affiliation":[{"name":"School of Allied Health, Faculty of Health Sciences, Curtin University, Perth 6845, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ackerman, I.N., Bohensky, M.A., Zomer, E., Tacey, M., Gorelik, A., Brand, C.A., and de Steiger, R. (2019). The projected burden of primary total knee and hip replacement for osteoarthritis in Australia to the year 2030. BMC Musculoskelet. Disord., 20.","DOI":"10.1186\/s12891-019-2411-9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/S0140-6736(15)60692-4","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990\u20132013: A systematic analysis for the Global Burden of Disease Study 2013","volume":"386","author":"Vos","year":"2015","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1002\/art.23259","article-title":"Participation restrictions among older adults with osteoarthritis: A mediated model of physical symptoms, activity limitations, and depression","volume":"59","author":"Machado","year":"2008","journal-title":"Arthritis Care Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1002\/art.23083","article-title":"Factors associated with restricted mobility outside the home in community-dwelling adults ages fifty years and older with knee pain: An. example of use of the International Classification of Functioning to investigate participation restriction","volume":"57","author":"Wilkie","year":"2007","journal-title":"Arthritis Care Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100069","DOI":"10.1016\/j.ocarto.2020.100069","article-title":"Machine learning in knee osteoarthritis: A review","volume":"2","author":"Kokkotis","year":"2020","journal-title":"Osteoarthr. Cartil. Open"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1016\/j.joca.2013.05.002","article-title":"OARSI recommended performance-based tests to assess physical function in people diagnosed with hip or knee osteoarthritis","volume":"21","author":"Dobson","year":"2013","journal-title":"Osteoarthr. Cartil."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Weygers, I., Kok, M., Konings, M., Hallez, H., De Vroey, H., and Claeys, K. (2020). Inertial sensor-based lower limb joint kinematics: A methodological systematic review. Sensors, 20.","DOI":"10.3390\/s20030673"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mundt, M., Koeppe, A., David, S., Witter, T., Bamer, F., Potthast, W., and Markert, B. (2020). Estimation of gait mechanics based on simulated and measured IMU data using an artificial neural network. Front. Bioengi. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00041"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"24","DOI":"10.7575\/aiac.ijkss.v.8n.3p.24","article-title":"A comparison of inertial motion capture systems: DorsaVi and Xsens","volume":"8","author":"Drapeaux","year":"2020","journal-title":"Int. J. Kinesiol. Sports Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.gaitpost.2017.10.005","article-title":"Mobile assessment of the lower limb kinematics in healthy persons and in persons with degenerative knee disorders: A systematic review","volume":"59","author":"Jonkers","year":"2018","journal-title":"Gait Posture"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Brock, H., Ohgi, Y., and Lee, J. Learning to Judge Like a Human: Convolutional Networks for Classification of Ski Jumping Errors. Proceedings of the 2017 ACM International Symposium on Wearable Computers.","DOI":"10.1145\/3123021.3123038"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jiang, W., and Yin, Z. (2015). Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks. Proceedings of the 23rd ACM International Conference on Multimedia, Association for Computing Machinery.","DOI":"10.1145\/2733373.2806333"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xue, Y. (2015, January 9\u201312). A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China.","DOI":"10.1109\/SMC.2015.263"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fridriksdottir, E., and Bonomi, A.G. (2020). Accelerometer-based human activity recognition for patient monitoring using a deep neural network. Sensors, 20.","DOI":"10.3390\/s20226424"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1080\/02640414.2018.1521769","article-title":"Machine and deep learning for sport-specific movement recognition: A systematic review of model development and performance","volume":"37","author":"Cust","year":"2019","journal-title":"J. Sports Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1007\/s40279-018-0878-4","article-title":"Wearable inertial sensor systems for lower limb exercise detection and evaluation: A systematic review","volume":"48","author":"Caulfield","year":"2018","journal-title":"Sports Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/s12984-020-00779-y","article-title":"Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments","volume":"17","author":"Rast","year":"2020","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.jsams.2016.07.007","article-title":"A simple method for quantifying jump loads in volleyball athletes","volume":"20","author":"Charlton","year":"2017","journal-title":"J. Sci. Med. Sport"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4193","DOI":"10.3390\/s150204193","article-title":"Wearable sensor-based rehabilitation exercise assessment for knee osteoarthritis","volume":"15","author":"Chen","year":"2015","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40798-020-0237-5","article-title":"Development of a human activity recognition system for ballet tasks","volume":"6","author":"Hendry","year":"2020","journal-title":"Sports Med. Open"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, P., Liu, K., Hsieh, C., and Chan, C. (2017, January 13\u201317). Human Motion Identification for Rehabilitation Exercise Assessment of Knee Osteoarthritis. Proceedings of the 2017 International Conference on Applied System Innovation (ICASI), Sapporo, Japan.","DOI":"10.1109\/ICASI.2017.7988396"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neunet.2018.02.017","article-title":"Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors","volume":"102","year":"2018","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.patrec.2018.03.020","article-title":"Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor","volume":"118","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1123\/ijspp.2016-0683","article-title":"Monitoring hitting load in tennis using inertial sensors and machine learning","volume":"12","author":"Whiteside","year":"2017","journal-title":"Int. J. Sports Physiol. Perform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Arif, M., and Kattan, A. (2015). Physical activities monitoring using wearable acceleration sensors attached to the body. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130851"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"169183","DOI":"10.1109\/ACCESS.2020.3024003","article-title":"Design of a wearable wireless multi-sensor monitoring system and application for activity recognition using deep learning","volume":"8","author":"Ascioglu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Emmerzaal, J., De Brabandere, A., Vanrompay, Y., Vranken, J., Storms, V., De Baets, L., Corten, K., Davis, J., Jonkers, I., and Vanwanseele, B. (2020). Towards the monitoring of functional status in a free-living environment for people with hip or knee osteoarthritis: Design and evaluation of the JOLO blended care app. Sensors, 20.","DOI":"10.3390\/s20236967"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ramanujam, E., Perumal, T., and Padmavathi, S. (2021). Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review. IEEE Sens. J., 1.","DOI":"10.1109\/JSEN.2021.3069927"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1016\/j.jbiomech.2007.10.016","article-title":"Gait and neuromuscular pattern changes are associated with differences in knee osteoarthritis severity levels","volume":"41","author":"Astephen","year":"2008","journal-title":"J. Biomech."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.gaitpost.2018.03.002","article-title":"Biomechanical characteristics of stair ambulation in patients with knee OA: A systematic review with meta-analysis toward a better definition of clinical hallmarks","volume":"62","author":"Iijima","year":"2018","journal-title":"Gait Posture"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.gaitpost.2012.01.005","article-title":"Sit-to-stand alterations in advanced knee osteoarthritis","volume":"36","author":"Turcot","year":"2012","journal-title":"Gait Posture"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1053\/joca.2002.0797","article-title":"Increased knee joint loads during walking are present in subjects with knee osteoarthritis","volume":"10","author":"Baliunas","year":"2002","journal-title":"Osteoarthr. Cartil."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.clinbiomech.2015.03.007","article-title":"Knee motion variability in patients with knee osteoarthritis: The effect of self-reported instability","volume":"30","author":"Gustafson","year":"2015","journal-title":"Clin. Biomech."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.jelekin.2011.07.011","article-title":"Effect of severity of knee osteoarthritis on the variability of gait parameters","volume":"21","author":"Kiss","year":"2011","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Albert, M., Toledo, S., Shapiro, M., and Koerding, K. (2012). Using mobile phones for activity recognition in Parkinson\u2019s patients. Front. Neurol., 3.","DOI":"10.3389\/fneur.2012.00158"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lonini, L., Gupta, A., Kording, K., and Jayaraman, A. (2016, January 16\u201320). Activity Recognition in Patients with Lower Limb Impairments: Do we need training data from each patient?. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orladno, FL, USA.","DOI":"10.1109\/EMBC.2016.7591425"},{"key":"ref_37","unstructured":"National Clinical Guideline (2014). National Clinical Guideline. National Institute for Health and Clinical Excellence: Guidance. Osteoarthritis: Care and Management in Adults, National Institute for Health and Care Excellence."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/1477-7525-1-64","article-title":"The knee injury and osteoarthritis outcome score (KOOS): From joint injury to osteoarthritis","volume":"1","author":"Roos","year":"2003","journal-title":"Health Qual. Life Outcomes"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/S0021-9290(01)00222-6","article-title":"ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion\u2014Part I: Ankle, hip, and spine","volume":"35","author":"Wu","year":"2002","journal-title":"J. Biomech."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hou, C. (2020, January 15\u201318). A Study on IMU-Based Human Activity Recognition Using Deep Learning and Traditional Machine Learning. Proceedings of the 2020 5th International Conference on Computer and Communication Systems (ICCCS), Shanghai, China.","DOI":"10.1109\/ICCCS49078.2020.9118506"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sani, S., Massie, S., Wiratunga, N., and Cooper, K. (2017). Learning Deep and Shallow Features for Human Activity Recognition, Springer International Publishing.","DOI":"10.1007\/978-3-319-63558-3_40"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: A survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1007\/s10618-017-0495-0","article-title":"Activity recognition in beach volleyball using a Deep Convolutional Neural Network","volume":"31","author":"Kautz","year":"2017","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_45","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimisation. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"133982","DOI":"10.1109\/ACCESS.2020.3010715","article-title":"Deep neural networks for human activity recognition with wearable sensors: Leave-one-subject-out cross-validation for model. selection","volume":"8","author":"Gholamiangonabadi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Deep, S., and Zheng, X. (2019, January 27\u201329). Leveraging CNN and Transfer Learning for Vision-based Human Activity Recognition. Proceedings of the 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), Auckland, New Zealand.","DOI":"10.1109\/ITNAC46935.2019.9078016"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/TNSRE.2017.2745418","article-title":"Using inertial sensors to automatically detect. and segment activities of daily living in people with Parkinson\u2019s disease","volume":"26","author":"Nguyen","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Janidarmian, M., Roshan Fekr, A., Radecka, K., and Zilic, Z. (2017). A comprehensive analysis on wearable acceleration sensors in human activity recognition. Sensors, 17.","DOI":"10.3390\/s17030529"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/1479-5868-9-148","article-title":"Direct and indirect measurement of physical activity in older adults: A systematic review of the literature","volume":"9","author":"Kowalski","year":"2012","journal-title":"Int. J. Behav. Nutr. Phys. Act."},{"key":"ref_51","first-page":"1","article-title":"Validity of tools to measure physical activity in older adults following total knee arthroplasty","volume":"1","author":"Jasper","year":"2021","journal-title":"J. Aging Phys. Act."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lee, J., Joo, H., Lee, J., and Chee, Y. (2020). Automatic classification of squat posture using inertial sensors: Deep learning approach. Sensors, 20.","DOI":"10.3390\/s20020361"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3381\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:59:58Z","timestamp":1760162398000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3381"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,12]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21103381"],"URL":"https:\/\/doi.org\/10.3390\/s21103381","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,12]]}}}