Inertial sensor-based heel strike and energy expenditure prediction using a hybrid machine learning approach.

Abstract

Objective: Gait analysis plays a critical role in healthcare, biomechanics, and sports science, particularly for estimating energy expenditure (EE). This study introduces a hybrid machine learning approach integrating convolutional neural networks (CNNs), long-short-term memory (LSTM) networks, and transfer learning (TL) to estimate volume of oxygen (VO₂) and detect heel strikes (HS) using data from a single 9-axis inertial measurement unit (IMU).

Methods: A clinical-grade VO₂ machine provided reference data for model training. The hybrid model was designed to combine spatial and temporal feature extraction capabilities from CNNs and LSTM networks while leveraging pre-trained weights through TL. The study compared the performance of the hybrid model with an LSTM-only approach to quantify improvements in VO₂ prediction.

Results: The hybrid model significantly reduced the VO₂ prediction error from 20% to 3% compared to using LSTM-only approach. Additionally, the model demonstrated high accuracy for HS detection, achieving 93.53% accuracy as indicated by training and validation results. The lightweight IMU-based system proved effective for VO₂ estimation, offering a practical alternative to traditional VO₂ measurement systems, which are often complex, bulky, and uncomfortable for subjects.

Conclusions: This study highlights the potential of a hybrid machine learning approach using IMU-based systems for accurate VO₂ estimation and HS detection. While the results are promising, the model's performance is constrained by 10 healthy subject datasets. Future work will require validation with more diverse datasets to enhance generalizability and robustness.