Ottawa Hospital Research Institute, Ottawa, ON K1Y 4E9, Canada.
Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Sensors (Basel). 2021 Oct 21;21(21):6974. doi: 10.3390/s21216974.
Foot strike detection is important when evaluating a person's gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.
当评估一个人的步态特征时，脚部触地检测很重要。智能手机的加速度计和陀螺仪信号已被用于训练人工智能 (AI) 模型，以实现健全人和老年人的自动脚部触地检测。然而，对于下肢截肢者的脚部触地检测研究有限，因为他们的步态更具变异性和不对称性。使用智能手机放置在后骨盆处采集的原始加速度计和陀螺仪信号，开发了一种新的下肢截肢者自动脚部触地检测方法。原始信号用于训练决策树模型和长短期记忆 (LSTM) 模型，以实现自动脚部触地检测。这些模型是使用在使用 TOHRC Walk Test 应用程序进行的 6 分钟步行测试 (6MWT) 期间收集的回顾性数据（n = 72）开发的。在 6MWT 期间，为每位参与者在后腰带上放置了一部 Android 智能手机，以在 50 Hz 下采集加速度计和陀螺仪信号。用于脚部触地识别的最佳模型是 LSTM，其在 LSTM 层中具有 100 个隐藏节点，在密集层中具有 50 个隐藏节点，批量大小为 64（准确率为 99.0%，灵敏度为 86.4%，特异性为 99.4%，精度为 83.7%）。这项研究为下肢截肢者人群创建了一种新的自动脚部触地识别方法，与手动标记等效，可用于临床。自动脚部触地检测是进行步长分析和启用其他 AI 应用（如跌倒检测）所必需的。
