Cloud AI Coaching for Myoelectric Prosthesis Calibration

The patent transitions from local calibration (single user's limited data) to cloud-based aggregated calibration (multiple users' combined data). By moving data collection and processing to the cloud dimension, the system accesss larger diverse data sets without requiring each user to collect extensive data locally, thus improving calibration accuracy while maintaining user convenience.

The cloud server acts as an intermediary between the user's device and the calibration process. It receives data from multiple users, aggregates and processes it to create improved calibration models, then returns calibrated parameters to individual users. This intermediary enables accurate calibration using aggregated data without requiring users to directly handle complex data collection and processing.

The system enables users to perform calibration independently through a user-friendly interface that guides them through the process. The cloud-based AI model automatically processes their data and provides calibrated parameters without requiring specialist intervention. Users receive real-time feedback and coaching messages to complete calibration accurately, eliminating the need for in-person specialist visits while maintaining calibration quality.

The patent implements real-time feedback mechanisms where users receive coaching messages, performance metrics, and guidance during the calibration process. The system monitors calibration progress and provides iterative feedback to help users achieve accurate calibration independently, reducing the need for repeated specialist visits and saving time.

The patent implements dynamic calibration that adapts to changing physiological conditions. The cloud-based AI model continuously learns from new data and updates calibration parameters in response to varying muscle signals, humidity levels, and socket positions. This dynamic approach maintains control reliability despite degradation factors, unlike static conventional systems.

The system performs preliminary calibration and continuous monitoring before control degradation occurs. By establishing an initial accurate calibration using aggregated data and providing ongoing feedback during use, the system prepares and maintains optimal control parameters in advance, preventing reliability issues before they arise rather than reacting to degradation after it occurs.