Mechanical Engineering, The Pennsylvania State University, Berks College, Reading, PA, USA; Kinesiology, The Pennsylvania State University, Berks College, Reading, PA, USA; Mechanical Engineering, Alvernia University, Reading, PA, USA.
Mechanical Engineering, The Pennsylvania State University, Berks College, Reading, PA, USA.
J Biomech. 2024 Sep;174:112255. doi: 10.1016/j.jbiomech.2024.112255. Epub 2024 Aug 2.
Recent reports have suggested that there may be a relationship between footstrike pattern and overuse injury incidence and type. With the recent increase in wearable sensors, it is important to identify paradigms where the footstrike pattern can be detected in real-time from minimal data. Machine learning was used to classify tibial acceleration data into three distinct footstrike patterns: rearfoot, midfoot, or forefoot. Tibial accelerometry data were collected during treadmill running from 58 participants who each ran with rearfoot, midfoot, and forefoot strike patterns. These data were used as inputs into an artificial neural network classifier. Models were created by using three distinct acceleration data sets, using the first 100%, 75%, and 40% of stance phase. All models were able to predict the footstrike pattern with up to 89.9% average accuracy. The highest error was associated with the identification of the midfoot versus forefoot strike pattern. This technique required no pre-selection of features or filtering of the data and may be easily incorporated into a wearable device to aid with real-time footstrike pattern detection.
最近的报告表明，足部着地方式与过度使用损伤的发生和类型之间可能存在关联。随着可穿戴传感器的日益普及，重要的是要确定可以从最少的数据中实时检测足部着地方式的范例。机器学习被用于将胫骨加速度数据分类为三种不同的足部着地方式：后足、中足或前足。从 58 名参与者在跑步机上跑步时收集胫骨加速度计数据，这些参与者分别采用后足、中足和前足着地方式。这些数据被用作人工神经网络分类器的输入。通过使用三个不同的加速度数据集来创建模型，使用支撑相的前 100%、75%和 40%。所有模型都能够以高达 89.9%的平均准确率预测足部着地方式。最大的错误与识别中足与前足着地方式有关。该技术不需要对特征进行预选或对数据进行过滤，并且可以很容易地集成到可穿戴设备中，以帮助实时检测足部着地方式。
