An inertial sensing mechanism for measuring gait parameters and gait energy expenditure

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

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.

A hand motion capture method based on infrared thermography for measuring fine motor skills in biomedicine

Many biomedical applications require fine motor skill assessments; however, real-time and contactless fine motor skill assessments are not typically implemented. In this study, we followed the 2D-to-3D pipeline principle and proposed a transformer-based spatial-temporal network to accurately regress 3D hand joint locations by inputting infrared thermal video for eliminating need of multiple cameras or RGB-D devices. We also developed a dataset composed of infrared thermal videos and ground truth annotations for training. The label represents a set of 3D joint locations from infrared optical trackers, which is considered the gold standard for clinical applications. To demonstrate their potential, the proposed method was used to measure the finger motion angle, and we investigated its accuracy by comparing the proposal with the Azure Kinect system and Leap Motion system. On the proposed dataset, the proposed method achieved a 3D hand pose mean error of less than 14 mm and outperforms the other deep learning methods. When the error thresholds were larger than approximately 35 mm, our method first to achieved excellent performance (>80%) in terms of the fraction of good frames. For the finger motion angle calculation task, the proposed and commercial systems had comparable inter-system reliability (ICC_2,1 ranging from 0.81 to 0.83) and excellent validity (Pearson's r-values ranging from 0.82 to 0.86). We believe that the proposed approaches can capture hand motion and measure finger motion angles and can be used in different biomedicine scenarios as an effective evaluation tool for fine motor skills.