Interpretable and explainable artificial intelligence for wearable sensor-based fall risk assessment in older adults:...

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-based fall risk assessment has not been systematically examined.

A PRISMA 2020-compliant 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 post-hoc XAI. Data were extracted on population characteristics, sensor modalities, outcome definitions, ML algorithms, explainability strategies, validation methods, and predictive performance.

Eleven studies (2019-2025, total n = 5,484) 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 0.65-0.92 and AUC of 0.70-0.92, 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&O users or incorporated device-specific predictors.

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.