Abstract
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.
Highlights
Foot strike (FS) identification is necessary for human gait evaluation, providing insight into a person’s activity levels, mobility, and gait pattern
FS identification was completed by visual analysis with video-tracking systems (Vicon, etc.), ground reaction force analysis with force plates, or 3D accelerometer and gyroscope signal analysis from sensors placed at the foot/ankle or shank
The long short-term memory (LSTM) model was trained on all 12 signals collected during the 6-min walk test (6MWT)
Summary
Foot strike (FS) identification is necessary for human gait evaluation, providing insight into a person’s activity levels, mobility, and gait pattern. FS identification was completed by visual analysis with video-tracking systems (Vicon, etc.), ground reaction force analysis with force plates, or 3D accelerometer and gyroscope signal analysis from sensors placed at the foot/ankle or shank. While these methods have been successful for both able-bodied and disease populations [1,2,3], they can be expensive, difficult, and timely to set-up. 3D signals collected from a smartphone located at the pelvis have provided a more accessible analysis of the movement status [4,5,6] While these models can identify FS with a high accuracy, the FS identification models were typically based on able-bodied participant data
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