{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T01:21:24Z","timestamp":1789435284593,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T00:00:00Z","timestamp":1706227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Rochester Institute of Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy. Objective: Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols. Methods: 21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models\u2014Support Vector Machine, Logistic Regression, and Random Forest\u2014were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated. Results: The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk. Conclusion: Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.<\/jats:p>","DOI":"10.3390\/s24030812","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T12:06:58Z","timestamp":1706616418000},"page":"812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8941-0182","authenticated-orcid":false,"given":"Masoud","family":"Abdollahi","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2998-4993","authenticated-orcid":false,"given":"Ehsan","family":"Rashedi","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonia","family":"Jahangiri","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6572-0912","authenticated-orcid":false,"given":"Pranav Madhav","family":"Kuber","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasibeh","family":"Azadeh-Fard","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary","family":"Dombovy","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation and Neurology, Unity Hospital, Rochester, NY 14626, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e254","DOI":"10.1161\/CIR.0000000000000950","article-title":"Heart disease and stroke statistics\u20142021 update: A report from the American Heart Association","volume":"143","author":"Virani","year":"2021","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"933","DOI":"10.15585\/mmwr.mm6635e1","article-title":"Vital signs: Recent trends in stroke death rates\u2014United States, 2000\u20132015","volume":"66","author":"Yang","year":"2017","journal-title":"Morb. Mortal. Wkly. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1093\/gerona\/glu225","article-title":"Ambulatory fall-risk assessment: Amount and quality of daily-life gait predict falls in older adults","volume":"70","author":"Pijnappels","year":"2015","journal-title":"J. Gerontol. A Biol. Sci. Med. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1682\/JRRD.2007.09.0145","article-title":"Falls in individuals with stroke","volume":"45","author":"Weerdesteijn","year":"2008","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/j.1547-5069.2010.01369.x","article-title":"An integrative review of factors associated with falls during post-stroke rehabilitation","volume":"42","author":"Campbell","year":"2010","journal-title":"J. Nurs. Sch."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s12199-007-0023-8","article-title":"Development of an assessment sheet for fall prediction in stroke inpatients in convalescent rehabilitation wards in Japan","volume":"13","author":"Nakagawa","year":"2008","journal-title":"Environ. Health Prev. Med."},{"key":"ref_7","first-page":"216","article-title":"Falls in stroke patients: Risk factors and risk management","volume":"12","author":"Tsur","year":"2010","journal-title":"Isr. Med. Assoc. J."},{"key":"ref_8","first-page":"100519","article-title":"An inpatient fall risk assessment tool: Application of machine learning models on intrinsic and extrinsic risk factors","volume":"15","author":"Jahangiri","year":"2024","journal-title":"Mach. Learn. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tigrini, A., Verdini, F., Fioretti, S., Scattolini, M., Mobarak, R., Gambi, E., Burattini, L., and Mengarelli, A. (2023, January 22\u201324). Gait Event Timeseries Assessment through Spectral Biomarkers and Machine Learning. Proceedings of the 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L\u2019Aquila, Italy.","DOI":"10.1109\/CBMS58004.2023.00227"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5974","DOI":"10.1109\/JBHI.2022.3205058","article-title":"Identification of neurodegenerative diseases from gait rhythm through time domain and time-dependent spectral descriptors","volume":"26","author":"Mengarelli","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Abdollahi, M., Whitton, N., Zand, R., Dombovy, M., Parnianpour, M., Khalaf, K., and Rashedi, E. (2022). A systematic review of fall risk factors in stroke survivors: Towards improved assessment platforms and protocols. Front. Bioeng. Biotechnol., 10.","DOI":"10.3389\/fbioe.2022.910698"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1136\/jech-2015-206475","article-title":"Systematic review of risk prediction models for falls after stroke","volume":"70","author":"Walsh","year":"2016","journal-title":"J. Epidemiol. Community Health"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.physio.2015.01.009","article-title":"Do measures of reactive balance control predict falls in people with stroke returning to the community?","volume":"101","author":"Mansfield","year":"2015","journal-title":"Physiotherapy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.1016\/j.jstrokecerebrovasdis.2016.06.045","article-title":"Proposal for a New Predictive Scale for Recurrent Risk of Fall in a Cohort of Community-Dwelling Patients with Stroke","volume":"25","author":"Pinto","year":"2016","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wei, T.S., Liu, P.T., Chang, L.W., and Liu, S.Y. (2017). Gait asymmetry, ankle spasticity, and depression as independent predictors of falls in ambulatory stroke patients. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177136"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1016\/j.jstrokecerebrovasdis.2016.03.032","article-title":"One-Leg Standing Time of the Affected Side Moderately Predicts for Postdischarge Falls in Community Stroke Patients","volume":"25","author":"Yoshimoto","year":"2016","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.parkreldis.2016.03.007","article-title":"Motor dual-tasking deficits predict falls in Parkinson's disease: A prospective study","volume":"26","author":"Heinzel","year":"2016","journal-title":"Park. Relat. Disord."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.jns.2013.09.021","article-title":"Dual task cost of walking is related to fall risk in persons with multiple sclerosis","volume":"335","author":"Wajda","year":"2013","journal-title":"J. Neurol. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1080\/00140139.2020.1858185","article-title":"Smartphone-based human fatigue level detection using machine learning approaches","volume":"64","author":"Karvekar","year":"2021","journal-title":"Ergonomics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbiomech.2018.09.009","article-title":"Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities","volume":"81","author":"Halilaj","year":"2018","journal-title":"J. Biomech."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2433","DOI":"10.1016\/j.apmr.2017.05.018","article-title":"Factors Predicting Falls and Mobility Outcomes in Patients With Stroke Returning Home After Rehabilitation Who Are at Risk of Falling","volume":"98","author":"Ng","year":"2017","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Abdollahi, M., Kuber, P.M., Pierce, M., Cristales, K., Dombovy, M., LaLonde, J., and Rashedi, E. (2023, January 24\u201327). Motor-Cognitive Dual-Task Paradigm Affects Timed Up & Go (TUG) Test Outcomes in Stroke Survivors. Proceedings of the 2023 11th International IEEE\/EMBS Conference on Neural Engineering (NER), Baltimore, MD, USA.","DOI":"10.1109\/NER52421.2023.10123749"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Abdollahi, M., Kuber, P.M., Shiraishi, M., Soangra, R., and Rashedi, E. (2022). Kinematic Analysis of 360\u00b0 Turning in Stroke Survivors Using Wearable Motion Sensors. Sensors, 22.","DOI":"10.3390\/s22010385"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Silva, J., Madureira, J., Tonelo, C., Baltazar, D., Silva, C., Martins, A., Alcobia, C., and Sousa, I. (2017, January 21\u201323). Comparing Machine Learning Approaches for Fall Risk Assessment. Proceedings of the BIOSIGNALS, Porto, Portugal.","DOI":"10.5220\/0006227802230230"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"16349","DOI":"10.1038\/s41598-018-34671-6","article-title":"Application of wearable inertial sensors and a new test battery for distinguishing retrospective fallers from non-fallers among community-dwelling older people","volume":"8","author":"Qiu","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3813","DOI":"10.1007\/s12206-019-0724-0","article-title":"Fall prediction of the elderly with a logistic regression model based on instrumented timed up & go","volume":"33","author":"Seo","year":"2019","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"84795","DOI":"10.1109\/ACCESS.2023.3299489","article-title":"Machine Learning techniques applied to the development of a fall risk index for older adults","volume":"11","author":"Millet","year":"2023","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16154","DOI":"10.1038\/s41598-019-52697-2","article-title":"Fall risk prediction in multiple sclerosis using postural sway measures: A machine learning approach","volume":"9","author":"Sun","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"23119","DOI":"10.1109\/ACCESS.2023.3252886","article-title":"Fall risk prediction using wireless sensor insoles with machine learning","volume":"11","author":"Agrawal","year":"2023","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.arth.2020.08.034","article-title":"Machine learning predicts the fall risk of total hip arthroplasty patients based on wearable sensor instrumented performance tests","volume":"36","author":"Polus","year":"2021","journal-title":"J. Arthroplast."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Roshdibenam, V., Jogerst, G.J., Butler, N.R., and Baek, S. (2021). Machine learning prediction of fall risk in older adults using timed up and go test kinematics. Sensors, 21.","DOI":"10.3390\/s21103481"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e26275","DOI":"10.1097\/MD.0000000000026275","article-title":"The effect of the degree of dual-task interference on gait, dual-task cost, cognitive ability, balance, and fall efficacy in people with stroke: A cross-sectional study","volume":"100","author":"Baek","year":"2021","journal-title":"Medicine"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.physio.2015.04.011","article-title":"Dual-task testing to predict falls in community-dwelling older adults: A systematic review","volume":"102","author":"Wittwer","year":"2016","journal-title":"Physiotherapy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"95","DOI":"10.2522\/ptj.20130386","article-title":"Dual tasking with the timed \u201cup & go\u201d test improves detection of risk of falls in people with Parkinson disease","volume":"95","author":"Vance","year":"2015","journal-title":"Phys. Ther."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"921506","DOI":"10.3389\/fdgth.2022.921506","article-title":"Wearable sensor systems for fall risk assessment: A review","volume":"4","author":"Subramaniam","year":"2022","journal-title":"Front. Digit. Health"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Abdollahi, M., Ashouri, S., Abedi, M., Azadeh-Fard, N., Parnianpour, M., Khalaf, K., and Rashedi, E. (2020). Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach. Sensors, 20.","DOI":"10.3390\/s20123600"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s11517-016-1496-7","article-title":"A method to qualitatively assess arm use in stroke survivors in the home environment","volume":"55","author":"Leuenberger","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Van Meulen, F.B., Klaassen, B., Held, J., Reenalda, J., Buurke, J.H., Van Beijnum, B.-J.F., Luft, A., and Veltink, P.H. (2016). Objective evaluation of the quality of movement in daily life after stroke. Front. Bioeng. Biotechnol., 3.","DOI":"10.3389\/fbioe.2015.00210"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/812\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:49:46Z","timestamp":1760104186000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,26]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030812"],"URL":"https:\/\/doi.org\/10.3390\/s24030812","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,26]]}}}