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The ankle is formed by the talocrural joint (crural = leg). It consists of the articulations between the talus bone of the foot and the distal ends of the tibia and fibula of the leg. The superior aspect of the talus bone is square-shaped and has three areas of articulation. The top of the talus articulates with the inferior tibia. This is the portion of the ankle joint that carries the body weight between the leg and foot. The sides of the talus are firmly held in position by the articulations...

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Deep Learning and IoT-Based Ankle-Foot Orthosis for Enhanced Gait Optimization.

Ferdous Rahman Shefa¹, Fahim Hossain Sifat¹, Jia Uddin²

¹Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka 1207, Bangladesh.

Healthcare (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

This study introduces a smart ankle-foot orthosis (AFO) using IoT and machine learning for gait imbalance management. The Transformer model achieved 98.97% accuracy in classifying walking phases, enabling personalized treatment recommendations.

Keywords:

Internet of Things ankle–foot orthosis good health and well-being healthcare machine learning wearable device

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Area of Science:

Biomedical Engineering
Rehabilitation Technology
Artificial Intelligence in Healthcare

Background:

Gait imbalances affect mobility and quality of life.
Ankle-foot orthosis (AFO) devices provide crucial support for individuals with muscle weakness or paralysis.
Current AFO management lacks personalized, data-driven insights.

Purpose of the Study:

To develop an integrated Internet of Things (IoT) and machine learning system for managing gait imbalances.
To revolutionize medical orthotics with a sophisticated, data-driven approach.
To enhance patient care through personalized gait management solutions.

Main Methods:

A smart ankle-foot orthosis (AFO) integrated with surface electromyography (sEMG) and Inertial Measurement Unit (IMU) sensors.
Cloud-based data transmission via fog computing for real-time analysis.
Machine learning models including Transformer, LSTM, ANN, SVM, Random Forest, and Decision Tree were employed for gait phase classification.

Main Results:

The Transformer model achieved 98.97% accuracy in classifying normal versus aberrant walking phases.
Accurate prediction and measurement of patient walking pattern improvements were demonstrated.
The system effectively distinguished between normal and aberrant gait phases.

Conclusions:

Predictive analytics enable tailored recommendations for AFO usage duration and intensity.
Physician validation and patient access to progress reports ensure continuous monitoring and treatment adjustments.
This approach facilitates personalized rehabilitation and improved patient outcomes.