Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations
| Autor: | Natalie Baddour, Helena Burger, Andrej Bavec, Pascale Juneau, Edward D. Lemaire |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
030506 rehabilitation
Computer science Decision tree STRIDE TP1-1185 Accelerometer smartphone Biochemistry Article Analytical Chemistry law.invention 03 medical and health sciences 0302 clinical medicine Gait (human) Amputees law Artificial Intelligence 6MWT decision tree Humans Computer vision Electrical and Electronic Engineering Instrumentation stride parameters foot strike detection Aged Retrospective Studies business.industry Chemical technology Deep learning Decision Trees deep learning Gyroscope amputee Atomic and Molecular Physics and Optics machine learning Memory Short-Term Lower Extremity Artificial intelligence Applications of artificial intelligence 0305 other medical science business LSTM 030217 neurology & neurosurgery Decision tree model |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 6974, p 6974 (2021) Sensors Volume 21 Issue 21 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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