Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries
Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries
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DOI：
10.1109/access.2023.3347263
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发表时间：
2024
期刊：
影响因子：
3.9
通讯作者：
Emily A. Kamienski;Ieee Paolo Bonato Graduate Student Member;I. H. H. A. Senior Member-I.-H.-H.-A.-Senior-Member-2278473099;Emily A. Kamienski
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文献类型：
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作者：
Emily A. Kamienski;Ieee Paolo Bonato Graduate Student Member;I. H. H. A. Senior Member-I.-H.-H.-A.-Senior-Member-2278473099;Emily A. Kamienski
跌倒可能会对老年人造成严重伤害，导致生活质量下降。目前，缺乏跌倒伤害预防设备，特别是对于依赖助行器的平衡障碍人群。在这里，助行器的功能得到了增强，因此它可以实时预测跌倒并通过快速可重构机制防止跌倒受伤。一个关键的挑战是实时跌倒预测，这是一个时间紧迫的决策过程。必须预先预测跌倒，以便系统有足够的时间部署伤害预防机制。数据是从使用助行器时经历各种失去平衡情况的人类受试者中收集的。基于多个长短期记忆 (LSTM) 网络的预测器是基于三种新技术构建的。首先，通过分别学习快速和慢速跌倒来识别不同的跌倒类型。其次，构建了一个“定时器 LSTM”，用于估计失衡无法恢复且必须激活跌倒预防机制之前剩余的时间。然后，如果时间允许，会收集额外的数据并进一步检查跌倒的可能性。这种方法降低了跌倒预测误报率。第三，使用数据缺陷指标（称为利普希茨商数）进一步分析混杂案例。寻找降低 Lipschitz 商从而提高数据可预测性的附加数据特征并将其合并到原始输入信号中。增强数据进一步提高了性能，最佳模型识别跌倒的成功率为 97%，误报率为 0.17%。该预测方法在新型助行器型跌倒预测和预防原型上实现。该助行器占地面积小，可提高机动性，并且在预计发生跌倒的情况下展开可扩展的腿时，不会翻倒。因此，拴在不翻倒助行器上的老年人可以避免跌倒。这项工作介绍了在可用数据有限时提高实时跌倒预测器性能的技术，确定如何从可用传感器信号中选择有效的特征，并将跌倒预测器合并到物理设备中以实现快速跌倒伤害预防响应。这为未来通过实时防坠落保护改善老年人健康的研究带来了巨大的好处。
Falls may cause serious injuries to older adults, leading to a deteriorated quality of life. Currently, there is a lack of fall injury prevention devices, especially for the balance impaired population who rely on mobility aids. Here, the functionality of a walker is augmented, so that it can predict a fall in real-time and prevent fall injuries via a rapidly reconfigurable mechanism. A key challenge is real-time fall prediction, which is a time-critical decision making process. A fall must be predicted preemptively so that the system has sufficient time to deploy the injury prevention mechanism. Data are collected from human subjects undergoing diverse loss-of-balance situations while using a walker. A predictor based on multiple Long-Short Term Memory (LSTM) networks is constructed based on three novel techniques. First, diverse fall types are identified by separately learning fast and slow falls. Second, a “Timer LSTM” is constructed that estimates the time remaining before an imbalance is unrecoverable and the fall prevention mechanism must be activated. Then if time allows, additional data are collected and the possibility of a fall is further examined. This approach lowered the fall prediction false positive rate. Third, confounding cases are further analyzed using a metric of data deficiency, called the Lipschitz quotient. Additional data features that lower the Lipschitz quotients and, thereby, increase data predictability, are sought and incorporated into the original input signals. Augmenting the data further improved performance, and the best model had a 97% success rate at identifying falls at a 0.17% false positive rate. The prediction method is implemented on a novel walker-type fall prediction and prevention prototype. The walker has a small footprint for improved maneuverability, and becomes untippable when its expandable legs are deployed in the event of a predicted fall. Thus, the older adult tethered to the untippable walker is protected from a fall. This work introduces techniques to improve the performance of real-time fall predictors when limited data is available, identifies how to select fruitful features from the available sensor signals, and incorporates a fall predictor into a physical device for a rapid fall injury prevention response. This promises immense benefits for future research on improving older adult wellbeing through real-time fall protection.