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Dense & Attention Convolutional Neural Networks for Toe Walking Recognition
Insights
A new AI model, the Dense & Attention convolutional network (DANet), effectively detects idiopathic toe walking (ITW) in children. This advancement aids in early diagnosis and intervention for the gait disorder.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
- Pediatric Gait Analysis
Background:
- Idiopathic toe walking (ITW) is a common pediatric gait disorder characterized by limited heel strike during walking.
- ITW can lead to secondary complications including balance issues, pain, and impaired physical development.
- Early detection of ITW is crucial for timely and effective intervention strategies.
Purpose of the Study:
- To develop and evaluate a novel deep learning architecture for accurate detection of idiopathic toe walking.
- To improve upon existing methods for ITW identification using advanced convolutional neural network features.
- To provide a scalable and generalizable AI solution for diagnosing ITW.
Main Methods:
- A one-dimensional Dense & Attention convolutional network (DANet) architecture was proposed.
- Dense blocks were integrated for enhanced feature transfer, and attention modules were used to prioritize relevant features.
- The Focal Loss function was modified to address data imbalance issues in the dataset.
Main Results:
- The DANet achieved a test recall of 88.91% for ITW detection on a local dataset.
- Validation on public datasets yielded an average precision of 89.34%, recall of 91.50%, and F1-Score of 92.04%.
- The proposed DANet demonstrated superior performance compared to other existing methods.
Conclusions:
- The DANet model presents a valid and feasible approach for the automated detection of idiopathic toe walking.
- The AI-driven method shows significant potential for improving early diagnosis and management of ITW in children.
- The study highlights the efficacy of integrating dense blocks and attention mechanisms in deep learning for gait analysis.
Abstract:
Idiopathic toe walking (ITW) is a gait disorder where children's initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new one-dimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are incorporated into the network to highlight useful features while suppressing unwanted noises. Also, the Focal Loss function is enhanced to alleviate the imbalance sample issue. The proposed approach outperforms other methods and obtains a superior performance. It achieves a test recall of 88.91% for recognizing idiopathic toe walking on the local dataset collected from real-world experimental scenarios. To ensure the scalability and generalizability of the proposed approach, the algorithm is further validated through the publicly available datasets, and the proposed approach achieves an average precision, recall, and F1-Score of 89.34%, 91.50%, and 92.04%, respectively. Experimental results present a competitive performance and demonstrate the validity and feasibility of the proposed approach.
