ArticleHealthcare (Basel, Switzerland)2024
Deep Learning and IoT-Based Ankle-Foot Orthosis for Enhanced Gait Optimization.
Article in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning-Based Resource Management in Fog Computing: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Investigating the needs of older adults with type 2 diabetes and conceptualizing a healthy diet management application: a conceptual design.BMC geriatrics · 2025Article
- IoT-enabled medical advances shaping the future of orthopaedic surgery and rehabilitation.Journal of clinical orthopaedics and trauma · 2025Review
- An EMG-Based GRU Model for Estimating Foot Pressure to Support Active Ankle Orthosis Development.Sensors (Basel, Switzerland) · 2025Article
- Surface Electromyography Combined with Artificial Intelligence in Predicting Neuromuscular Falls in the Elderly: A Narrative Review of Present Applications and Future Perspectives.Healthcare (Basel, Switzerland) · 2025Review
- Clinical and instrument-based assessment of balance, gait, and motor functions in pediatric cerebral palsy: A systematic review.Wearable technologies · 2025Review
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND/
objectivesThis paper proposes a method for managing gait imbalances by integrating the Internet of Things (IoT) and machine learning technologies. Ankle-foot orthosis (AFO) devices are crucial medical braces that align the lower leg, ankle, and foot, offering essential support for individuals with gait imbalances by assisting weak or paralyzed muscles. This research aims to revolutionize medical orthotics through IoT and machine learning, providing a sophisticated solution for managing gait issues and enhancing patient care with personalized, data-driven insights.
methodsThe smart ankle-foot orthosis (AFO) is equipped with a surface electromyography (sEMG) sensor to measure muscle activity and an Inertial Measurement Unit (IMU) sensor to monitor gait movements. Data from these sensors are transmitted to the cloud via fog computing for analysis, aiming to identify distinct walking phases, whether normal or aberrant. This involves preprocessing the data and analyzing it using various machine learning methods, such as Random Forest, Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Transformer models.
resultsThe Transformer model demonstrates exceptional performance in classifying walking phases based on sensor data, achieving an accuracy of 98.97%. With this preprocessed data, the model can accurately predict and measure improvements in patients' walking patterns, highlighting its effectiveness in distinguishing between normal and aberrant phases during gait analysis.
conclusionsThese predictive capabilities enable tailored recommendations regarding the duration and intensity of ankle-foot orthosis (AFO) usage based on individual recovery needs. The analysis results are sent to the physician's device for validation and regular monitoring. Upon approval, the comprehensive report is made accessible to the patient, ensuring continuous progress tracking and timely adjustments to the treatment plan.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.