{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T20:56:43Z","timestamp":1792184203448,"version":"4.4.0"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Falls among older adults are a leading cause of morbidity and loss of independence. Wearable sensors combined with machine learning (ML) offer opportunities for objective fall risk evaluation, but low model transparency limits clinical adoption. Interpretable and explainable artificial intelligence (XAI) methods can address this constraint, yet their application in wearable sensor\u2013based fall risk assessment has not been systematically examined.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A PRISMA 2020\u2013compliant systematic review was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore. Studies were eligible if they included older adults, employed wearable sensors, hybrid sensor systems, or structured clinical assessment instruments, applied AI\/ML for fall risk assessment (not detection), and incorporated intrinsic interpretability or\n                      <jats:italic>post-hoc<\/jats:italic>\n                      XAI. Data were extracted on population characteristics, sensor modalities, outcome definitions, ML algorithms, explainability strategies, validation methods, and predictive performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Eleven studies (2019\u20132025, total\n                      <jats:italic>n<\/jats:italic>\n                      =\u202f5,484) met inclusion criteria. Inertial Measurement Units predominated. Fall risk definitions were heterogeneous, spanning retrospective fall history, clinical balance scales, and prospective diaries. Explainability was implemented almost exclusively at the global level, with only two studies providing both global and local explanations. On comparable tasks, interpretable models achieved accuracy of 0.65\u20130.92 and AUC of 0.70\u20130.92, suggesting interpretability carried no consistent performance penalty over more complex designs. All studies relied on internal validation only; none performed external validation or real-time deployment. None recruited P&amp;amp;O users or incorporated device-specific predictors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Current models favour interpretable architectures and achieve moderate-to-high performance, but are constrained by heterogeneous outcome definitions, absence of external validation, and global-only explainability that limits individual-level clinical utility. The evidence base does not yet support clinical deployment. Extending these findings to prosthetics and orthotics users, a clinically important downstream application, will require device-specific datasets, asymmetry-adjusted thresholds, and instance-level explanations. These represent the priority directions for the next stage of this research agenda.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fncom.2026.1860978","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T05:37:45Z","timestamp":1784007465000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Interpretable and explainable artificial intelligence for wearable sensor-based fall risk assessment in older adults: a systematic review with considerations for prosthetics and orthotics"],"prefix":"10.3389","volume":"20","author":[{"given":"Adeel Hussain","family":"Samdani","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Sir Syed University of Engineering and Technology","place":["Karachi, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saad Jawaid","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Prosthetics and Orthotics, College of Medical Rehabilitation Sciences, Taibah University","place":["Al Madinah Al Munawwarah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhannad","family":"Farhan","sequence":"additional","affiliation":[{"name":"Department of Prosthetics and Orthotics, College of Medical Rehabilitation Sciences, Taibah University","place":["Al Madinah Al Munawwarah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kehinde Quasim","family":"Yusuf","sequence":"additional","affiliation":[{"name":"Department of Prosthetics and Orthotics, College of Medical Rehabilitation Sciences, Taibah University","place":["Al Madinah Al Munawwarah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abu Zeeshan","family":"Bari","sequence":"additional","affiliation":[{"name":"Department of Engineering Technology, Sam Houston State University","place":["Huntsville, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Faisal","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer Science and Information Technology, King Faisal University","place":["Al Ahsa, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usaimah","family":"Nasir","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Electrical Engineering, University of Oulu","place":["Oulu, Finland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveed","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Allied Health Professions and Pharmacy, Keele University","place":["Keele, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saleh Ahmed","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Department of Prosthetics and Orthotics, College of Medical Rehabilitation Sciences, Taibah University","place":["Al Madinah Al Munawwarah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yousef Mohammed Saad","family":"Alshahrani","sequence":"additional","affiliation":[{"name":"Department of Prosthetics and Orthotics, College of Medical Rehabilitation Sciences, Taibah University","place":["Al Madinah Al Munawwarah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1109\/JBHI.2023.3267039","article-title":"Ambient monitoring of gait and machine learning models for dynamic and short-term falls risk assessment in people with dementia","volume":"27","author":"Adeli","year":"2023","journal-title":"IEEE J. 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