Falls are the leading cause of fatal and non-fatal injuries among seniors with serious and costly consequences. Laboratory evidence supports the view that impaired ability to execute compensatory balance reactions (CBRs) or near-falls is linked to an increased risk of falling. Therefore, as an alternative to the commonly used fall risk assessment methods examining spatial-temporal parameters of gait, this study focuses on the development of machine learning-based models to detect multidirectional CBRs using wearable inertial measurement units (IMUs). Random forest models were developed based upon the data captured by five wearable IMUs to 1) detect CBRs during normal gait, and 2) identify the type of CBR (eight different classes). A perturbation treadmill (PT) was employed to systematically elicit CBRs (i.e. PT-CBRs) during walking in different directions (e.g slip-like, trip-like, and medio-lateral) and amplitudes (e.g., low-, high-amplitude). We hypothesized that these PT-CBRs could simulate naturally-occurring CBRs (N-CBRs). Proof-of-concept testing in 9 young, healthy adults demonstrated accuracies of 96.60% and 80.64% for the PT-CBR detection and type identification models, respectively. Performance of the detection model was tested against a published dataset (IMUFD) simulating N-CBRs, including the most common types observed in older adults in long-term care facilities, which achieved sensitivity of 100%, but poor specificity. Adding normal gait data from IMUFD for training improved specificity, indicating treadmill walking alone is insufficient exemplar data. Perturbation treadmill combined with overground walking data is a suitable paradigm to collect training datasets of involuntary CBR events. These findings suggest that accurate detection of naturally-occurring CBRs is feasible, and supports further investigation of implementing a wearable sensor system to track naturally-occurring CBRs as a novel means of fall risk assessment.

中文翻译：

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自动检测多方向补偿性平衡反应：迈向自然发生在跌倒附近的步骤

跌落是造成老年人致命和非致命伤害的主要原因，其后果是严重而代价高昂的。实验室证据支持这样的观点，即执行代偿平衡反应（CBR）或接近跌倒的能力受损与跌倒的风险增加有关。因此，作为替代常用的检查步态时空参数的跌落风险评估方法的方法，本研究着重于开发基于机器学习的模型，以使用可穿戴惯性测量单元（IMU）检测多向CBR。基于五个可穿戴IMU捕获的数据开发了随机森林模型，以：1）在正常步态期间检测CBR，以及2）识别CBR的类型（八种不同的类别）。使用摄动跑步机（PT）来系统诱发CBR（即 PT-CBR）在不同方向（例如，滑样，绊倒和中外侧）和振幅（例如，低，高振幅）上行走。我们假设这些PT-CBR可以模拟自然发生的CBR（N-CBR）。在9位健康的年轻人中进行的概念验证测试显示，PT-CBR检测和类型识别模型的准确度分别为96.60％和80.64％。针对模拟N-CBR的已发布数据集（IMUFD）对检测模型的性能进行了测试，包括在长期护理机构中老年人中观察到的最常见类型，其敏感性达到100％，但特异性较差。从IMUFD添加正常步态数据以训练特异性得到改善，表明仅跑步机行走不足以作为示例数据。摄动跑步机结合地面步行数据是收集非自愿性CBR事件训练数据集的合适范例。这些发现表明，准确检测自然发生的CBR是可行的，并支持进一步研究实施可穿戴传感器系统以追踪自然发生的CBR，作为跌倒风险评估的新方法。