An On-Node Processing Approach for Anomaly Detection in Gait
An On-Node Processing Approach for Anomaly Detection in Gait
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DOI：
10.1109/jsen.2015.2464774
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发表时间：
2015-11-01
影响因子：
4.3
通讯作者：
Lo, Benny
中科院分区：
文献类型：
--
作者：
Cola, Guglielmo;Avvenuti, Marco;Lo, Benny
提出了一种利用可穿戴式加速度计捕获步态偏差的新方法。以前的研究已经概述了步态分析对评估老年患者的虚弱和跌倒风险的重要性。已经提出了几种基于可穿戴传感器的解决方案，以帮助老年医生进行行动能力评估测试，例如定时起跳测试。然而，这些方法只能应用于有监督的场景，不允许对步态进行连续和不显眼的监控。我们提出的方法是为了在完全无人监督的方式下实现步态的连续监测，需要使用一个安装在腰部的加速度计。用户的步态模式使用特定的基于加速的功能自动学习，而异常检测用于捕获用户行走方式的细微变化。所有需要的处理都可以在可穿戴设备上实时执行。该方法在30名志愿者中进行了评估，他们模拟了膝关节屈曲损伤。平均而言，我们的方法对持续时间类似于5 S的步态异常节段的识别准确率接近84%，及时发现步态异常可以早期干预和预防跌倒。
A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can only be applied to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we propose is designed to achieve continuous monitoring of gait in a completely unsupervised fashion, requiring the use of a single waist-mounted accelerometer. The user's gait patterns are automatically learned using specific acceleration-based features, while anomaly detection is used to capture subtle changes in the way the user walks. All the required processing can be executed in real time on the wearable device. The method was evaluated with 30 volunteers, who simulated a knee flexion impairment. On average, our method obtained similar to 84% accuracy in the recognition of abnormal gait segments lasting similar to 5 s. Prompt detection of gait anomalies could enable early intervention and prevent falls.