{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T07:09:07Z","timestamp":1776236947723,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:00:00Z","timestamp":1643241600000},"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>Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution.<\/jats:p>","DOI":"10.3390\/s22030984","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T22:01:57Z","timestamp":1643320917000},"page":"984","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Fall Risk Assessment Using Wearable Sensors: A Narrative Review"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6489-4221","authenticated-orcid":false,"given":"Rafael N.","family":"Ferreira","sequence":"first","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimaraes, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-057 Braga, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4800-058 Guimaraes, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4177-2587","authenticated-orcid":false,"given":"Nuno Ferrete","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimaraes, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-057 Braga, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4800-058 Guimaraes, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0023-7203","authenticated-orcid":false,"given":"Cristina P.","family":"Santos","sequence":"additional","affiliation":[{"name":"Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058 Guimaraes, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4710-057 Braga, Portugal"},{"name":"LABBELS\u2014Associate Laboratory, 4800-058 Guimaraes, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,27]]},"reference":[{"key":"ref_1","unstructured":"WHO (2021, May 15). Falls. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/falls."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rajagopalan, R., Litvan, I., and Jung, T.P. (2017). Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions. Sensors, 17.","DOI":"10.3390\/s17112509"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9610567","DOI":"10.1155\/2019\/9610567","article-title":"A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results","volume":"2019","author":"Hemmatpour","year":"2019","journal-title":"Adv.-Hum.-Comput. Interact."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/TNSRE.2019.2911602","article-title":"A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System","volume":"27","author":"Saadeh","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Leone, A., Rescio, G., Giampetruzzi, L., and Siciliano, P. (2019, January 8\u201310). Smart EMG-based Socks for Leg Muscles Contraction Assessment. Proceedings of the 2019 IEEE International Symposium on Measurements Networking (M N), Catania, Italy.","DOI":"10.1109\/IWMN.2019.8804991"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rucco, R., Sorriso, A., Liparoti, M., Ferraioli, G., Sorrentino, P., Ambrosanio, M., and Baselice, F. (2018). Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors, 18.","DOI":"10.3390\/s18051613"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Rivolta, M.W., Aktaruzzaman, M., Rizzo, G., Lafortuna, C.L., Ferrarin, M., Bovi, G., Bonardi, D.R., and Sassi, R. (2015, January 25\u201329). Automatic vs. clinical assessment of fall risk in older individuals: A proof of concept. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319987"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tang, W., Fulk, G., Zeigler, S., Zhang, T., and Sazonov, E. (2019, January 19\u201322). Estimating Berg Balance Scale and Mini Balance Evaluation System Test Scores by Using Wearable Shoe Sensors. Proceedings of the 2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI), Chicago, IL, USA.","DOI":"10.1109\/BHI.2019.8834631"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1038\/s41746-019-0204-z","article-title":"Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors","volume":"2","author":"Greene","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Reginatto, B., Taylor, K., Patterson, M.R., Power, D., Komaba, Y., Maeda, K., Inomata, A., and Caulfield, B. (2015, January 25\u201329). Context aware falls risk assessment: A case study comparison. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319631"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Parvaneh, S., Najafi, B., Toosizadeh, N., Riaz, I.B., and Mohler, J. (2016, January 11\u201314). Is there any association between ventricular ectopy and falls in community-dwelling older adults?. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.125-394"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Annese, V.F., and De Venuto, D. (2015, January 18\u201319). FPGA based architecture for fall-risk assessment during gait monitoring by synchronous EEG\/EMG. Proceedings of the 6th International Workshop on Advances in Sensors and Interfaces (IWASI), Gallipoli, Italy.","DOI":"10.1109\/IWASI.2015.7184953"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.gaitpost.2016.06.016","article-title":"Sleep quality is associated with walking under dual-task, but not single-task performance","volume":"49","author":"Agmon","year":"2016","journal-title":"Gait Posture"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nait Aicha, A., Englebienne, G., van Schooten, K.S., Pijnappels, M., and Kr\u00f6se, B. (2018). Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry. Sensors, 18.","DOI":"10.3390\/s18051654"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.bspc.2018.03.005","article-title":"Prospective elderly fall prediction by older-adult fall-risk modeling with feature selection","volume":"43","author":"Howcroft","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/1743-0003-10-91","article-title":"Review of Fall Risk Assessment in Geriatric Populations Using Inertial Sensors","volume":"10","author":"Howcroft","year":"2013","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1049\/htl.2015.0019","article-title":"Review: Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults","volume":"2","author":"Shany","year":"2015","journal-title":"Healthc. Technol. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1111\/j.1532-5415.2005.00509.x","article-title":"Monitoring Falls in Cohort Studies of Community-Dwelling Older People: Effect of the Recall Interval","volume":"53","author":"Ganz","year":"2006","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1093\/ageing\/afh106","article-title":"Fear-related avoidance of activities, falls and physical frailty. A prospective community-based cohort study","volume":"33","author":"Delbaere","year":"2004","journal-title":"Age Ageing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1111\/j.1532-5415.1997.tb00946.x","article-title":"Gait changes in older adults: Predictors of falls or indicators of fear","volume":"45","author":"Maki","year":"1997","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Annese, V.F., and De Venuto, D. (2015, January 21\u201323). Gait analysis for fall prediction using EMG triggered movement related potentials. Proceedings of the 10th International Conference on Design Technology of Integrated Systems in Nanoscale Era (DTIS), Napoli, Italy.","DOI":"10.1109\/DTIS.2015.7127386"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1093\/ageing\/26.3.189","article-title":"Fear of falling and restriction of mobility in elderly fallers","volume":"26","author":"Vellas","year":"1997","journal-title":"Age Ageing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1109\/TNSRE.2017.2771383","article-title":"Wearable inertial sensors for fall risk assessment and prediction in older adults: A systematic review and meta-analysis","volume":"26","author":"Montesinos","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.artmed.2018.08.005","article-title":"Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis","volume":"95","author":"Rivolta","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6743","DOI":"10.1109\/JSEN.2017.2749446","article-title":"Quantitative Assessment of Balance Impairment for Fall-Risk Estimation Using Wearable Triaxial Accelerometer","volume":"17","author":"Shahzad","year":"2017","journal-title":"IEEE Sensor J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"035004","DOI":"10.1088\/1361-6579\/ab0d3e","article-title":"Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery","volume":"40","author":"Saporito","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rescio, G., Leone, A., Caroppo, A., and Siciliano, P. (2015, January 19\u201320). A preliminary study on fall risk evaluation through electromiography systems. Proceedings of the 2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL), Thessaloniki, Greece.","DOI":"10.1109\/IMCTL.2015.7359590"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Leone, A., Rescio, G., and Siciliano, P. (2017, January 27\u201329). Fall risk evaluation by surface electromyography technology. Proceedings of the 2017 International Conference on Engineering, Technology and Innovation (ICE\/ITMC), Madeira, Portugal.","DOI":"10.1109\/ICE.2017.8280003"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Buisseret, F., Catinus, L., Grenard, R., Jojczyk, L., Fievez, D., Barvaux, V., and Dierick, F. (2020). Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People. Sensors, 20.","DOI":"10.3390\/s20113207"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1109\/JIOT.2018.2844837","article-title":"A Smart Environment-Adapting Timed-Up-and-Go System Powered by Sensor-Embedded Insoles","volume":"6","author":"Yang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Selvaraj, M., Baltzopoulos, V., Shaw, A., Maganaris, C.N., Cullen, J., O\u2019Brien, T., and Kot, P. (2018, January 2\u20135). Stair Fall Risk Detection Using Wearable Sensors. Proceedings of the 11th International Conference on Developments in eSystems Engineering (DeSE), Cambridge, UK.","DOI":"10.1109\/DeSE.2018.00023"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vieira, B., Pereira, L., Freitas, R., Terroso, M., and Simoes, R. (2015, January 17\u201320). A gamified application for assessment of balance and fall prevention. Proceedings of the 10th Iberian Conference on Information Systems and Technologies (CISTI), Aveiro, Portugal.","DOI":"10.1109\/CISTI.2015.7170473"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dzhagaryan, A., Milenkovic, A., Jovanov, E., and Milosevic, M. (2015, January 14\u201317). Smart Button: A wearable system for assessing mobility in elderly. Proceedings of the 17th International Conference on E-health Networking, Application Services (HealthCom), Boston, MA, USA.","DOI":"10.1109\/HealthCom.2015.7454536"},{"key":"ref_35","first-page":"44","article-title":"The MobiFall Dataset:: Fall Detection and Classification with a Smartphone","volume":"2","author":"Vavoulas","year":"2016","journal-title":"Int. J. Monit. Surveill. Technol. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yao, M., Zhang, Q., Li, M., Li, H., Ning, Y., Xie, G., Zhao, G., Ma, Y., Gao, X., and Jin, Z. (2015, January 9\u201312). A wearable pre-impact fall early warning and protection system based on MEMS inertial sensor and GPRS communication. Proceedings of the IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA.","DOI":"10.1109\/BSN.2015.7299397"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.gaitpost.2006.03.008","article-title":"Minimum foot clearance during walking: Strategies for the minimisation of trip-related falls","volume":"25","author":"Begg","year":"2007","journal-title":"Gait Posture"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Abbate, S., Avvenuti, M., Cola, G., Corsini, P., Light, J., and Vecchio, A. (2011, January 9\u201312). Recognition of false alarms in fall detection systems. Proceedings of the 2011 IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC.2011.5766464"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.inffus.2020.10.018","article-title":"40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: Methods, lessons learned, and future challenges","volume":"68","author":"Nazarahari","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.gaitpost.2012.07.012","article-title":"Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors","volume":"37","author":"Mariani","year":"2013","journal-title":"Gait Posture"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/25\/2\/R01","article-title":"Accelerometry: Providing an integrated, practical method for long-term, ambulatory monitoring of human movement","volume":"25","author":"Mathie","year":"2004","journal-title":"Physiol. Meas."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TITB.2012.2226905","article-title":"A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals","volume":"17","author":"Cheng","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1589\/jpts.28.1478","article-title":"A comparative study of the electromyographic activities of lower extremity muscles during level walking and Pedalo riding","volume":"28","author":"Lee","year":"2016","journal-title":"J. Phys. Ther. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/0021-9290(85)90043-0","article-title":"Antonsson, R.W.M. The frequency content of gait","volume":"18","author":"Erik","year":"1985","journal-title":"J. Biomech."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/1475-925X-13-34","article-title":"A portable system with sample rate of 250 Hz for characterization of knee and hip angles in the sagittal plane during gait","volume":"13","author":"Martinez","year":"2014","journal-title":"Biomed. Eng. Online"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Winter, D.A. (2009). Biomechanics and Motor Control of Human Movement, John Wiley & Sons.","DOI":"10.1002\/9780470549148"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1111\/j.1532-5415.1991.tb01616.x","article-title":"The timed \u201cUp & Go\u201d: A test of basic functional mobility for frail elderly persons","volume":"39","author":"Podsiadlo","year":"1991","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_48","first-page":"S7","article-title":"Measuring balance in the elderly: Validation of an instrument","volume":"83","author":"Berg","year":"1992","journal-title":"Can. J. Public Health Rev. Can. Sante Publique"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/0002-9343(86)90717-5","article-title":"Fall risk index for elderly patients based on number of chronic disabilities","volume":"80","author":"Tinetti","year":"1986","journal-title":"Am. J. Med."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"323","DOI":"10.2340\/16501977-0537","article-title":"Using psychometric techniques to improve the Balance Evaluation Systems Test: The mini-BESTest","volume":"42","author":"Franchignoni","year":"2010","journal-title":"J. Rehabil. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1164\/ajrccm.166.1.at1102","article-title":"ATS Statement: Guidelines for the Six-Minute Walk Test","volume":"166","author":"Crapo","year":"2002","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/02701367.1999.10608028","article-title":"A 30-s chair-stand test as a measure of lower body strength in community-residing older adults","volume":"70","author":"Jones","year":"1999","journal-title":"Res. Q. Exerc. Sport"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11517-016-1504-y","article-title":"A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials","volume":"55","author":"Aziz","year":"2016","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/MSP.2010.939038","article-title":"Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP]","volume":"28","author":"Yu","year":"2011","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/JBHI.2020.3025049","article-title":"Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis","volume":"25","author":"Meyer","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.gaitpost.2019.09.007","article-title":"Time series classification using a modified LSTM approach from accelerometer-based data: A comparative study for gait cycle detection","volume":"74","author":"Tan","year":"2019","journal-title":"Gait Posture"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Casilari, E., Santoyo-Ram\u00f3n, J.A., and Cano-Garc\u00eda, J.M. (2017). Analysis of Public Datasets for Wearable Fall Detection Systems. Sensors, 17.","DOI":"10.3390\/s17071513"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s11556-016-0168-9","article-title":"The FARSEEING real-world fall repository: A large-scale collaborative database to collect and share sensor signals from real-world falls","volume":"13","author":"Klenk","year":"2016","journal-title":"Eur. Rev. Aging Phys. Act. Off. J. Eur. Group Res. Elder. Phys. Act."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s11556-017-0173-7","article-title":"A systematic review of gait perturbation paradigms for improving reactive stepping responses and falls risk among healthy older adults","volume":"14","author":"McCrum","year":"2017","journal-title":"Eur. Rev. Aging Phys. Act."},{"key":"ref_60","first-page":"40","article-title":"A Survey of Cross Validation Procedures for Model Selection","volume":"4","author":"Arlot","year":"2009","journal-title":"Stat. Surv."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"46721","DOI":"10.1038\/srep46721","article-title":"An ecologically-controlled exoskeleton can improve balance recovery after slippage","volume":"7","author":"Monaco","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"9802","DOI":"10.1016\/j.ifacol.2017.08.887","article-title":"Design of a Robotic Knee Assistive Device (ROKAD) for Slip-Induced Fall Prevention during Walking","volume":"50","author":"Trkov","year":"2017","journal-title":"IFAC-PapersOnLine"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e013661","DOI":"10.1136\/bmjopen-2016-013661","article-title":"Systematic review and meta-analysis: Tai Chi for preventing falls in older adults","volume":"7","author":"Huang","year":"2017","journal-title":"BMJ Open"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2294","DOI":"10.1111\/ggi.13082","article-title":"Perturbation-based balance training for falls reduction among older adults: Current evidence and implications for clinical practice","volume":"17","author":"Gerards","year":"2017","journal-title":"Geriatr. Gerontol. Int."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"377","DOI":"10.2165\/11539920-000000000-00000","article-title":"Comparison of traditional and recent approaches in the promotion of balance and strength in older adults","volume":"41","author":"Granacher","year":"2011","journal-title":"Sport. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/984\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:08:53Z","timestamp":1760134133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/984"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,27]]},"references-count":65,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030984"],"URL":"https:\/\/doi.org\/10.3390\/s22030984","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,27]]}}}