In an article recently published in the International Journal of Environmental Research and Public Health, researchers introduced a novel deep learning framework designed to identify obesity in adolescents through smartphone inertial measurements. This approach leverages gait pattern analysis using deep learning models to detect obesity indicators. The proposed method offers a scalable, accessible solution for continuous, non-intrusive obesity risk monitoring.
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Background
Obesity in adolescents is a growing concern due to its association with various health issues, including osteoarthritis, sleep apnea, cancer, and mental illnesses. Traditional methods, such as body mass index (BMI), offer a basic classification of overweight and obese individuals but fail to capture detailed biomechanical changes, such as those seen in gait patterns.
Gait analysis, the study of walking patterns, is widely used in clinical diagnoses, robotics, sports, and biomechanics. However, traditional methods like motion capture systems, which require markers, are costly and confined to lab-based settings, limiting their practicality for large-scale or real-time monitoring. Portable and unobtrusive alternatives, such as wearable sensors—including accelerometers, gyroscopes, and inertial measurement units—allow for gait analysis in real-world environments.
About the Research
In this paper, the authors propose a deep learning-based novel framework to identify obesity in adolescents using smartphone inertial sensors for gait data collection. Their main goal is to determine whether gait patterns captured by smartphone sensors can accurately differentiate between normal and obese adolescents.
The study used three deep learning models: convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and a combination of CNN and LSTM models. These models were selected for their ability to capture both spatial and temporal patterns in sequential data, making them suitable for analyzing complex gait patterns related to obesity.
Data was then collected from 244 first-year high school students using a mobile health app installed on smartphones. Each participant placed the smartphone in a pocket at the center of their body and walked a 78-foot course back and forth. Additionally, nine-channel rotation matrices from the smartphone’s rotation vector were used to analyze the gait data.
The researchers analyzed data from 173 participants, comprising 92 from the normal weight group and 46 from the overweight or obese group. To prepare the data for model training, they first reduced noise using a moving average filter, followed by time series segmentation through a sliding window method. For model evaluation, they allocated 80 % of the data for training and 20 % for testing.
The models were trained using the PyTorch framework with the Adam optimizer to minimize the cost function. Training was conducted over 50 epochs with a learning rate of 0.003. Model performance was assessed using several metrics, including accuracy, precision, recall, and the F1-score, which represents the harmonic mean of precision and recall.
Research Findings
The outcomes showed that the hybrid CNN-LSTM model outperformed both the CNN and LSTM models in classifying obesity based on gait patterns. It achieved an accuracy of 97 %, compared to 96.31 % for the LSTM model and 95.81 % for the CNN model. These results indicate that the hybrid model consistently performed better because it could handle sequential and spatial features.
While the CNN model performed well during training, it struggled to generalize to new data, likely due to its limitations in addressing the sequential nature of gait patterns. In contrast, the LSTM model effectively captured temporal dependencies but lacked the CNN’s ability to extract spatial features, resulting in slightly lower performance compared to the hybrid model. This study underscored the advantages of integrating deep learning models with smartphone technology for continuous, non-intrusive obesity risk monitoring in adolescents.
Additionally, the researchers noted significant differences in gait patterns between the obese and normal groups. The obese group exhibited shorter step lengths, slower walking speeds, and greater variability, consistent with previous research linking obesity to alterations in gait patterns.
Applications
This research has significant implications for developing new methods to detect and prevent obesity. Using smartphone inertial sensors and deep learning models, the study presents a noninvasive and cost-effective approach to monitoring gait patterns and identifying individuals at risk of obesity. This technology can be applied in clinical, community, and public health settings. It could offer personalized feedback on gait patterns and track changes over time, helping raise awareness of weight changes and supporting obesity prevention by providing quick alerts for better health monitoring.
Conclusion
In summary, using deep learning models combined with smartphone inertial sensors proved effective for identifying obesity through gait analysis. The hybrid CNN-LSTM model showed superior accuracy in classifying the obese based on gait patterns, highlighting the importance of integrating temporal and spatial feature extraction.
Future work should include more diverse samples to check the results and explore various smartphone models to ensure robustness. Overall, this research could significantly contribute to public health efforts to combat obesity.
Journal Reference
Degbey, G.-S.; Hwang, E.; Park, J.; Lee, S. Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements. Int. J. Environ. Res. Public Health 2024, 21, 1178. DOI: 10.3390/ijerph21091178, https://www.mdpi.com/1660-4601/21/9/1178
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