{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T10:07:40Z","timestamp":1790158060458,"version":"4.1.0"},"reference-count":62,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Naples Federico II","id":[{"id":"https:\/\/ror.org\/05290cv24","id-type":"ROR","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/05290cv24","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Objective: Falls among the elderly constitute a major global public health issue. Research has therefore focused on two complementary areas: fall risk prevention and fall detection. Existing solutions mainly rely on body-attached sensors confined to laboratory settings, whereas recent research has shifted toward wearable e-textiles with non-invasive, long-term-wear sensors. In this context, the study presents a wearable sensor platform to both predict fall risk and detect falls, based on sensorized clothing integrating inertial measurement units and surface electromyography sensors. Methods: Fall risk was estimated from gait parameters extracted during a 10 m walking test as the probability of belonging to a faller (vs. non-faller) group, using a logistic regression model trained on the G-STRIDE dataset, complemented by neuromuscular parameters extracted from sEMG and associated with fall risk. Fall detection, focused on improving pre-impact identification, was framed as a binary classification between activities of daily living and falls, using a reduced Spatio-Temporal Attention Network trained on the FallTL dataset and refined with our own platform\u2019s data. Results: The obtained results were: fall-risk assessment, Area Under the Curve = 77.8%, Accuracy = 68.7%; fall detection, Accuracy = 96.7%, F1 Score = 77.3%, lead time = 390 ms. Conclusions: As a single-subject proof of concept, these results support the feasibility of a unified platform integrating objective fall-risk screening and fall event identification, with a predicted time before impact suitable for protective systems intervention; validation on a larger, representative cohort is required before any clinical claim can be made.<\/jats:p>","DOI":"10.3390\/s26185867","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T16:07:29Z","timestamp":1789574849000},"page":"5867","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a Wearable Sensor Platform for Fall Risk Assessment and Fall Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8004-3734","authenticated-orcid":false,"given":"Antonella","family":"Imperato","sequence":"first","affiliation":[{"name":"ETA Bioengineering S.R.L., Corso Nicolangelo Protopisani 70, 80146 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Michele","family":"Caporaso","sequence":"additional","affiliation":[{"name":"ETA Bioengineering S.R.L., Corso Nicolangelo Protopisani 70, 80146 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Valentina De","family":"Pascalis","sequence":"additional","affiliation":[{"name":"ETA Bioengineering S.R.L., Corso Nicolangelo Protopisani 70, 80146 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1287-3223","authenticated-orcid":false,"given":"Giuseppe","family":"Di Gironimo","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering\u2014Fraunhofer JL IDEAS, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8485-7006","authenticated-orcid":false,"given":"Antonio","family":"Lanzotti","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering\u2014Fraunhofer JL IDEAS, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4731-2370","authenticated-orcid":false,"given":"Stanislao","family":"Grazioso","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering\u2014Fraunhofer JL IDEAS, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3736-0491","authenticated-orcid":false,"given":"Angela","family":"Palomba","sequence":"additional","affiliation":[{"name":"Department of Public Health, University of Naples Federico II, Via Sergio Pansini 5, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0416-1410","authenticated-orcid":false,"given":"Teodorico","family":"Caporaso","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering\u2014Fraunhofer JL IDEAS, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"ref_1","unstructured":"WHO (2021). Step Safely: Strategies for Preventing and Managing Falls Across the Life-Course, WHO."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"875","DOI":"10.15585\/mmwr.mm6927a5","article-title":"Trends in nonfatal falls and fall-related injuries among adults aged \u226565 years\u2014United States, 2012\u20132018","volume":"69","author":"Moreland","year":"2020","journal-title":"MMWR Morb. Mortal. Wkly. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1136\/ip-2023-045023","article-title":"Healthcare spending for non-fatal falls among older adults, USA","volume":"30","author":"Haddad","year":"2024","journal-title":"Inj. Prev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1177\/00333549231155869","article-title":"Cause-specific mortality among adults aged \u226565 years in the United States, 1999 through 2020","volume":"139","author":"Kakara","year":"2024","journal-title":"Public Health Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"379","DOI":"10.5770\/cgj.24.521","article-title":"Fear of falling in older adults: A scoping review of recent literature","volume":"24","author":"MacKay","year":"2021","journal-title":"Can. Geriatr. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chan, J.S., and Yan, J.H. (2014). Neuropsychological mechanisms of falls in older adults. Front. Aging Neurosci., 6.","DOI":"10.3389\/fnagi.2014.00064"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2340\/1650197787192530","article-title":"Walking after stroke. Measurement and recovery over the first 3 months","volume":"19","author":"Wade","year":"1987","journal-title":"J. Rehabil. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/s12603-011-0062-0","article-title":"Timed Up and Go test and risk of falls in older adults: A systematic review","volume":"15","author":"Beauchet","year":"2011","journal-title":"J. Nutr. Health Aging"},{"key":"ref_9","first-page":"2","article-title":"Berg balance scale","volume":"73","author":"Berg","year":"2009","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pavasini, R., Guralnik, J., Brown, J.C., Di Bari, M., Cesari, M., Landi, F., Vaes, B., Legrand, D., Verghese, J., and Wang, C. (2016). Short physical performance battery and all-cause mortality: Systematic review and meta-analysis. BMC Med., 14.","DOI":"10.1186\/s12916-016-0763-7"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"ii37","DOI":"10.1093\/ageing\/afl084","article-title":"Falls in older people: Epidemiology, risk factors and strategies for prevention","volume":"35","author":"Rubenstein","year":"2006","journal-title":"Age Ageing"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Subramaniam, S., Faisal, A.I., and Deen, M.J. (2022). Wearable sensor systems for fall risk assessment: A review. Front. Digit. Health, 4.","DOI":"10.3389\/fdgth.2022.921506"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Eichler, N., Raz, S., Toledano-Shubi, A., Livne, D., Shimshoni, I., and Hel-Or, H. (2022). Automatic and efficient fall risk assessment based on machine learning. Sensors, 22.","DOI":"10.3390\/s22041557"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lim, Z.K., Connie, T., Goh, M.K.O., and Saedon, N.I.B. (2024). Fall risk prediction using temporal gait features and machine learning approaches. Front. Artif. Intell., 7.","DOI":"10.3389\/frai.2024.1425713"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1080\/17434440.2016.1198694","article-title":"Wearable inertial sensors for human movement analysis","volume":"13","author":"Iosa","year":"2016","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.cmpb.2012.02.003","article-title":"Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: Validation on normal subjects by standard gait analysis","volume":"108","author":"Benedetti","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nkizi, Y., and Thamsuwan, O. (2024). Fall risk assessment in active elderly through the use of inertial measurement units: Determining the right postural balance variables and sensor locations. Appl. Sci., 14.","DOI":"10.3390\/app142311312"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Caporaso, T., Grazioso, S., and Di Gironimo, G. (2022). Development of an integrated virtual reality system with wearable sensors for ergonomic evaluation of human\u2013robot cooperative workplaces. Sensors, 22.","DOI":"10.3390\/s22062413"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100580","DOI":"10.1016\/j.jnha.2025.100580","article-title":"Gait parameters and daily physical activity for distinguishing pre-frail, frail, and non-frail older adults: A scoping review","volume":"29","author":"Zhang","year":"2025","journal-title":"J. Nutr. Health Aging"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ruiz-Ruiz, L., Jimenez, A.R., Garcia-Villamil, G., and Seco, F. (2021). Detecting fall risk and frailty in elders with inertial motion sensors: A survey of significant gait parameters. Sensors, 21.","DOI":"10.3390\/s21206918"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1016\/j.jbiomech.2016.02.055","article-title":"The complexity of daily life walking in older adult community-dwelling fallers and non-fallers","volume":"49","author":"Ihlen","year":"2016","journal-title":"J. Biomech."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"16915","DOI":"10.1038\/s41598-025-02128-2","article-title":"Predicting fall risk through step width variability at increased gait speed in community dwelling older adults","volume":"15","author":"Kim","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.jamda.2015.12.013","article-title":"Walking-induced fatigue leads to increased falls risk in older adults","volume":"17","author":"Morrison","year":"2016","journal-title":"J. Am. Med. Dir. Assoc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3991\/ijoe.v20i13.50101","article-title":"Biofeedback-Based Method for Real-Time Fatigue Monitoring of Knee","volume":"20","author":"Franco","year":"2024","journal-title":"Int. J. Online Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Annese, V.F., and De Venuto, D. (2015). Fall-risk assessment by combined movement related potentials and co-contraction index monitoring. Proceedings of the 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE.","DOI":"10.1109\/BioCAS.2015.7348366"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sinha, A., Jha, D., Swain, M.K., Rath, S.S., and Ghosh, N. (2024). Elderly Fall Detection Using Machine Learning. Prospects of Science, Technology and Applications, CRC Press.","DOI":"10.1201\/9781003489443-18"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ishaq, M., Guastella, D.C., Sutera, G., and Muscato, G. (2026). A systematic review of fall detection and prediction technologies for older adults: An analysis of sensor modalities and computational models. Appl. Sci., 16.","DOI":"10.3390\/app16041929"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e70143","DOI":"10.1111\/wvn.70143","article-title":"Wearable Technologies for Detecting Near-Falls: A Systematic Review With Implications for Geriatric Nursing Practice","volume":"23","author":"Labrague","year":"2026","journal-title":"Worldviews Evid.-Based Nurs."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, S., Miranda, F., Wang, Y., Rasheed, R., and Bhatt, T. (2022). Near-fall detection in unexpected slips during over-ground locomotion with body-worn sensors among older adults. Sensors, 22.","DOI":"10.3390\/s22093334"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hrub\u1ef3, D., Hrub\u00e1, E., and \u010cern\u1ef3, M. (2026). Research of Fall Detection and Fall Prevention Technologies: A Review. Sensors, 26.","DOI":"10.3390\/s26041192"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hughes-Riley, T., Dias, T., and Cork, C. (2018). A historical review of the development of electronic textiles. Fibers, 6.","DOI":"10.3390\/fib6020034"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Caporaso, T., Megna, A., Pesce, C., Grazioso, S., and Lanzotti, A. (2026). Wearable sensor-based methodology to assess physical fitness and motor coordination in playground park. Int. J. Interact. Des. Manuf. (IJIDeM), 1\u201310.","DOI":"10.1007\/s12008-026-02500-0"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Amitrano, F., Coccia, A., Colelli Riano, F., Pagano, G., Biancardi, A., Losavio, E., and D\u2019Addio, G. (2026). Automatic Identification of Lower-Limb Neuromuscular Activation Patterns During Gait Using a Textile Wearable Multisensor System. Sensors, 26.","DOI":"10.3390\/s26030997"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Leone, A., Rescio, G., Giampetruzzi, L., and Siciliano, P. (2019). Smart EMG-based socks for leg muscles contraction assessment. Proceedings of the 2019 IEEE International Symposium on Measurements & Networking (M&N), IEEE.","DOI":"10.1109\/IWMN.2019.8804991"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rescio, G., Leone, A., Giampetruzzi, L., and Siciliano, P. (2020). Fall Risk assessment using new sEMG-based smart socks. Advances in Data Science: Methodologies and Applications, Springer.","DOI":"10.1007\/978-3-030-51870-7_8"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Moore, A., Li, J., Contag, C.H., Currano, L.J., Pyles, C.O., Hinkle, D.A., and Patil, V.S. (2024). Wearable surface electromyography system to predict freeze of gait in Parkinson\u2019s disease patients. Sensors, 24.","DOI":"10.3390\/s24237853"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rahemtulla, Z., Turner, A., Oliveira, C., Kaner, J., Dias, T., and Hughes-Riley, T. (2023). The design and engineering of a fall and near-fall detection electronic textile. Materials, 16.","DOI":"10.3390\/ma16051920"},{"key":"ref_38","first-page":"167","article-title":"Surface electromyography: The SENIAM project","volume":"36","author":"Merletti","year":"2000","journal-title":"Eur. J. Phys. Rehabil. Med."},{"key":"ref_39","unstructured":"Caporaso, T., Grazioso, S., Palomba, A., Panariello, D., Grazioso, A., Caporaso, M., Di Gironimo, G., and Lanzotti, A. (2024). Process for Manufacturing a Garment for the Acquisition of Electromyographic Signals. (US Patent App. 18\/692,021)."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4683","DOI":"10.1038\/s41467-020-18503-8","article-title":"Fully organic compliant dry electrodes self-adhesive to skin for long-term motion-robust epidermal biopotential monitoring","volume":"11","author":"Zhang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.1109\/TIM.2018.2806950","article-title":"Effect of pressure on skin-electrode impedance in wearable biomedical measurement devices","volume":"67","author":"Taji","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.gaitpost.2019.10.039","article-title":"Wearable inertial sensors to measure gait and posture characteristic differences in older adult fallers and non-fallers: A scoping review","volume":"76","author":"Patel","year":"2020","journal-title":"Gait Posture"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"\u00c1lvarez, M.N., Rodr\u00edguez-S\u00e1nchez, C., Huertas-Hoyas, E., Garc\u00eda-Villamil-Neira, G., Espinoza-Cerda, M.T., P\u00e9rez-Delgado, L., Reina-Robles, E., Martin, I.B., Del-Ama, A.J., and Ruiz-Ruiz, L. (2023). Predictors of fall risk in older adults using the G-STRIDE inertial sensor: An observational multicenter case\u2013control study. BMC Geriatr., 23.","DOI":"10.1186\/s12877-023-04379-y"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1109\/TNSRE.2025.3645365","article-title":"Fall Monitoring With Single IMU: A Large-Scale Dataset and a Novel Dual-Branch Network","volume":"34","author":"Cai","year":"2025","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_45","unstructured":"Garc\u00eda-Villamil Neira, G., Neira \u00c1lvarez, M., Huertas Hoyas, E., Ruiz Ruiz, L., Garc\u00eda-de Villa, S., del Ama, A.J., Rodr\u00edguez S\u00e1nchez, M.C., and Jim\u00e9nez Ruiz, A. (2022). GSTRIDE: A Database of Frailty and Functional Assessments with Inertial Gait Data from Elderly Fallers and Non-Fallers Populations, Zenodo."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kim, S., Lee, S., and Jeong, W. (2020). EMG measurement with textile-based electrodes in different electrode sizes and clothing pressures for smart clothing design optimization. Polymers, 12.","DOI":"10.3390\/polym12102406"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1177\/0040517516646041","article-title":"A critical review on compression textiles for compression therapy: Textile-based compression interventions for chronic venous insufficiency","volume":"87","author":"Liu","year":"2017","journal-title":"Text. Res. J."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Byun, S., Lee, H.J., Han, J.W., Kim, J.S., Choi, E., and Kim, K.W. (2019). Walking-speed estimation using a single inertial measurement unit for the older adults. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0227075"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S1050-6411(97)84626-3","article-title":"Cocontraction in three age groups of children during treadmill locomotion","volume":"7","author":"Frost","year":"1997","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Qassim, H.M., Hasan, W.Z.W., Ramli, H.R., Harith, H.H., Mat, L.N.I., and Ismail, L.I. (2022). Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier. Sensors, 22.","DOI":"10.3390\/s22051900"},{"key":"ref_51","unstructured":"Cai, Y. (2025). FallTL: A Large-Scale Motion Dataset for Pre-Impact Fall Detection Based on Wearable Inertial Sensors, Zenodo."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"100448","DOI":"10.1016\/j.medntd.2026.100448","article-title":"Changes in Foot Gait Patterns After Prolonged Walking in Older Adults: An IMU and Plantar Force Analysis","volume":"31","author":"Zhang","year":"2026","journal-title":"Med. Nov. Technol. Devices"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s12938-016-0194-x","article-title":"Pre-impact fall detection","volume":"15","author":"Hu","year":"2016","journal-title":"Biomed. Eng. Online"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1109\/JSEN.2008.2012212","article-title":"Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier","volume":"9","author":"Shi","year":"2009","journal-title":"IEEE Sens. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105325","DOI":"10.1016\/j.bspc.2023.105325","article-title":"A practical wearable fall detection system based on tiny convolutional neural networks","volume":"86","author":"Yu","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yu, X., Jang, J., and Xiong, S. (2021). A large-scale open motion dataset (KFall) and benchmark algorithms for detecting pre-impact fall of the elderly using wearable inertial sensors. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.692865"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J.D., and Vargas-Bonilla, J.F. (2017). SisFall: A fall and movement dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Koo, B., Yu, X., Lee, S., Yang, S., Kim, D., Xiong, S., and Kim, Y. (2023). Tinyfallnet: A lightweight pre-impact fall detection model. Sensors, 23.","DOI":"10.3390\/s23208459"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"111785","DOI":"10.1016\/j.measurement.2022.111785","article-title":"A comprehensive comparison of accuracy and practicality of different types of algorithms for pre-impact fall detection using both young and old adults","volume":"201","author":"Yu","year":"2022","journal-title":"Measurement"},{"key":"ref_60","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."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"De Venuto, D., and Mezzina, G. (2020). High-specificity digital architecture for real-time recognition of loss of balance inducing fall. Sensors, 20.","DOI":"10.3390\/s20030769"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Maray, N., Ngu, A.H., Ni, J., Debnath, M., and Wang, L. (2023). Transfer learning on small datasets for improved fall detection. Sensors, 23.","DOI":"10.3390\/s23031105"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5867\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T09:30:01Z","timestamp":1790155801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5867"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":62,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185867"],"URL":"https:\/\/doi.org\/10.3390\/s26185867","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]}}}