E114232 - MAS - Muscle Analyzer System

Important results from the project

The MAS project developed a portable device to accurately assess muscle quality, specifically tailored for sarcopenia. By integrating microwave sensors, Raspberry Pi, NanoVNA, cloud services, and a user interface for real-time data, the project´s goal was realized. Phantom experiments validated the device´s accuracy, and a three-step AI algorithm estimated the thickness of skin, fat, and muscle layers. Secure communication via BACE+ and Hytton Cloud ensures safe data handling for clinical applications.

Expected long term effects

The MAS project developed a reliable, portable device for assessing muscle quality, validated through phantom experiments and clinical trials. Integrating a Raspberry Pi, NanoVNA, and a three-stage machine learning algorithm, it predicts tissue thickness and detects muscle quality variations. Dual communication via BACE+ and Hytton Cloud ensures secure, real-time data handling. This cost-effective tool enhances the detection and monitoring of sarcopenia and other muscle conditions for clinicians.

Approach and implementation

The MAS project has developed a portable and precise muscle quality assessment system. This device combines a Raspberry Pi, NanoVNA, and a touchscreen, featuring dual communication through BACE+ and Hytton Cloud for secure data transfer. The system´s accuracy was confirmed using Artificial Tissue-Emulating (ATE) phantoms. It employs a three-stage machine learning algorithm to predict tissue thicknesses, which has been effective in both phantom studies and clinical trials. The system is designed for scalability, reliability, and user-friendliness in clinical environments.