{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:20:26Z","timestamp":1760228426082,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Quantitative movement analysis is widely used in clinical practice and research to objectively and thoroughly investigate movement disorder [...]<\/jats:p>","DOI":"10.3390\/s22103720","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"3720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wearables for Movement Analysis in Healthcare"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3719-8789","authenticated-orcid":false,"given":"Paolo","family":"Capodaglio","sequence":"first","affiliation":[{"name":"Orthopaedic Rehabilitation Unit and Research Lab for Biomechanics, Rehabilitation and Ergonomics, Ospedale San Giuseppe, Istituto Auxologico Italiano, IRCCS, via Cadorna 90, 28824 Piancavallo di Oggebbio, Italy"},{"name":"Department Surgical Sciences, Physical and Rehabilitation Medicine, University of Torino, 10126 Torino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6299-7254","authenticated-orcid":false,"given":"Veronica","family":"Cimolin","sequence":"additional","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milan, Piazza Leonardo da Vinci 32, 20133 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zago, M., Tarabini, M., Delfino Spiga, M., Ferrario, C., Bertozzi, F., Sforza, C., and Galli, M. (2021). Machine-Learning Based Determination of Gait Events from Foot-Mounted Inertial Units. Sensors, 21.","DOI":"10.3390\/s21030839"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lueken, M., Mueller, L., Decker, M.G., Bollheimer, C., Leonhardt, S., and Ngo, C. (2020). Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life. Sensors, 20.","DOI":"10.3390\/s20247325"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Amitrano, F., Coccia, A., Ricciardi, C., Donisi, L., Cesarelli, G., Capodaglio, E.M., and D\u2019Addio, G. (2020). Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment. Sensors, 20.","DOI":"10.3390\/s20226691"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cimolin, V., Vismara, L., Ferraris, C., Amprimo, G., Pettiti, G., Lopez, R., Galli, M., Cremascoli, R., Sinagra, S., and Mauro, A. (2022). Computation of Gait Parameters in Post Stroke and Parkinson\u2019s Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems. Sensors, 22.","DOI":"10.3390\/s22030824"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Schifino, G., Cimolin, V., Pau, M., da Cunha, M.J., Leban, B., Porta, M., Galli, M., and Souza Pagnussat, A. (2021). Functional Electrical Stimulation for Foot Drop in Post-Stroke People: Quantitative Effects on Step-to-Step Symmetry of Gait Using a Wearable Inertial Sensor. Sensors, 21.","DOI":"10.3390\/s21030921"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Schwarz, A., Bhagubai, M.M.C., Wolterink, G., Held, J.P.O., Luft, A.R., and Veltink, P.H. (2020). Assessment of Upper Limb Movement Impairments after Stroke Using Wearable Inertial Sensing. Sensors, 20.","DOI":"10.3390\/s20174770"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pau, M., Capodaglio, P., Leban, B., Porta, M., Galli, M., and Cimolin, V. (2021). Kinematics Adaptation and Inter-Limb Symmetry during Gait in Obese Adults. Sensors, 21.","DOI":"10.3390\/s21175980"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cimolin, V., Gobbi, M., Buratto, C., Ferraro, S., Fumagalli, A., Galli, M., and Capodaglio, P. (2022). A Comparative Analysis of Shoes Designed for Subjects with Obesity Using a Single Inertial Sensor: Preliminary Results. Sensors, 22.","DOI":"10.3390\/s22030782"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pau, M., Mulas, I., Putzu, V., Asoni, G., Viale, D., Mameli, I., Leban, B., and Allali, G. (2020). Smoothness of Gait in Healthy and Cognitively Impaired Individuals: A Study on Italian Elderly Using Wearable Inertial Sensor. Sensors, 20.","DOI":"10.3390\/s20123577"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Valle, M.S., Casabona, A., Sapienza, I., Laudani, L., Vagnini, A., Lanza, S., and Cioni, M. (2021). Use of a Single Wearable Sensor to Evaluate the Effects of Gait and Pelvis Asymmetries on the Components of the Timed Up and Go Test, in Persons with Unilateral Lower Limb Amputation. Sensors, 22.","DOI":"10.3390\/s22010095"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Romano, P., Pournajaf, S., Ottaviani, M., Gison, A., Infarinato, F., Mantoni, C., De Pandis, M.F., Franceschini, M., and Goffredo, M. (2021). Sensor Network for Analyzing Upper Body Strategies in Parkinson\u2019s Disease versus Normative Kinematic Patterns. Sensors, 21.","DOI":"10.3390\/s21113823"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Habets, J.G.V., Herff, C., Kubben, P.L., Kuijf, M.L., Temel, Y., Evers, L.J.W., Bloem, B.R., Starr, P.A., Gilron, R., and Little, S. (2021). Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson\u2019s Disease Using a Wrist-Worn Accelerometer. Sensors, 21.","DOI":"10.1101\/2021.09.03.458142"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Prill, R., Walter, M., Kr\u00f3likowska, A., and Becker, R. (2021). A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation. Sensors, 21.","DOI":"10.3390\/s21248221"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, B.H., Hong, S.H., Oh, I.W., Lee, Y.W., Kee, I.H., and Lee, S.Y. (2021). Measurement of Ankle Joint Movements Using IMUs during Running. Sensors, 21.","DOI":"10.20944\/preprints202105.0771.v1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Di Paolo, S., Lopomo, N.F., Della Villa, F., Paolini, G., Figari, G., Bragonzoni, L., Grassi, A., and Zaffagnini, S. (2021). Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors. Sensors, 21.","DOI":"10.3390\/s21072331"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3720\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:19Z","timestamp":1760137819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,13]]},"references-count":15,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103720"],"URL":"https:\/\/doi.org\/10.3390\/s22103720","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,5,13]]}}}