Generative AI and Deep Learning for Sensor-Based Clinical Biomechanics
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".
Deadline for manuscript submissions: 20 February 2026 | Viewed by 71
Special Issue Editor
Interests: neurology; clinical biomechanics; machine learning; movement disorders
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Generative AI and Deep Learning are advancing sensor-based clinical biomechanics, including movement analysis, digital health, and personalized rehabilitation. AI-powered analytics are opening up new possibilities for early diagnostics, real-time patient monitoring, and precision medicine by combining data from wearable sensors, inertial measurement units (IMUs), optoelectronic motion capture systems, and force platforms.
This Special Issue focuses on the use of Generative AI, including generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, as well as advanced Deep Learning architectures like LSTMs, Transformers, and Graph Neural Networks (GNNs), to improve the understanding of biomechanical patterns and support neurodegenerative disease management.
We welcome contributions related to the following topics:
- Synthetic data generation for biomechanical modeling and sensor fusion;
- Deep Learning frameworks for real-time movement disorder detection and rehabilitation monitoring;
- Overcoming data scarcity with AI-driven augmentation techniques;
- Multimodal sensor integration for complete digital health applications;
- Explainable AI (XAI) enables transparent and interpretable clinical decision-making.
By bridging the gap between sensor technologies and AI-driven clinical solutions, this Special Issue hopes to attract interdisciplinary research from AI experts, clinicians, and biomechanical scientists, fostering innovation in healthcare, neuroscience, and movement science.
Dr. Mariano Serrao
Guest Editor
Dante Trabassi
Guest Editor Assistant
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Keywords
- generative AI
- deep learning
- sensor-based biomechanics
- movement analysis
- synthetic data generation
- wearable sensors
- explainable AI (XAI)
- neurodegenerative disorders
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