学术动态：Benchmarking Time-Series Artificial Intelligence Architectures for Wearable Sensor-Based Fall Prediction: A Synthe.
MDPI Sensors 论文摘要：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 synthet
论文标题：Benchmarking Time-Series Artificial Intelligence Architectures for Wearable Sensor-Based Fall Prediction: A Synthetic Data Simulation Framework
发布日期：2026-05-24
作者：Edward R. Sykes, Mohammad Maghsoudimehrabani, Abdulrahman Al-Shanoon
DOI：10.3390/s26113326
论文摘要：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.
元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113326. Vol. 26, Issue 11. Authors: Edward R. Sykes, Mohammad Maghsoudimehrabani, Abdulrahman Al-Shanoon.
开放许可： https://creativecommons.org/licenses/by/4.0/
原文链接： https://doi.org/10.3390/s26113326
PDF 链接： https://www.mdpi.com/1424-8220/26/11/3326/pdf
来源： MDPI Sensors via Crossref
