Falls among older adults are a major cause of injury and loss of independence, yet most existing systems detect falls only after onset or provide very limited warning time. This study presents a synthetic benchmarking framework for early fall-risk prediction using multimodal wearable-inspired time-series data and compares classical and temporal machine learning architectures under a realistic evaluation protocol. A synthetic dataset of 1000 sequences was generated to emulate normal activity, slip events, and pre-fall instability using biomechanical, physiological, and contextual variables. Eight baseline models and two augmented temporal variants were trained and evaluated using subject-wise splits to reduce leakage. Performance differed substantially by model family and evaluation protocol. Classical baselines achieved the strongest overall macro-F1 scores, whereas temporal models showed more modest discrimination. Under a fixed alerting rule, operational early-warning behavior varied considerably: some models failed to trigger alerts, while others achieved higher pre-fall trigger rates at the cost of increased false alarms. These findings show that apparent performance depends strongly on partitioning strategy, calibration, and alert design. The proposed framework provides a reproducible basis for benchmarking early-warning fall-risk models and supports future validation using real-world cohorts and deployment-oriented calibration strategies.
Sykes et al. (2026) studied this question.