This research presents the design, multi-threaded software implementation, and empirical evaluation of an active, electromyography (EMG)-driven upper-limb orthosis utilizing a distributed master-slave embedded control architecture. Engineered specifically to address the clinical challenges of severe upper-limb paresis—such as those resulting from brachial plexus trauma—the system bypasses traditional localized muscle control by acquiring voluntary surface EMG signals from the sternocleidomastoid (SCM) neck muscle, utilizing it as a stable proxy for motion intent. To ensure hard real-time deterministic execution and high-fidelity signal processing, the system is physically and logically decoupled into three operational layers. A dedicated analog front-end (AD8232) and an STM32F407 microcontroller handle localized biosignal conditioning and digitization. A Jetson Nano single-board computer operates as the supervisory node, executing a strictly governed finite state machine (FSM) to perform digital filtering, feature extraction, and motion classification. Finally, an ESP32 microcontroller paired with a BTS7960 high-current H-bridge manages real-time brushed DC motor actuation. Communication across these distinct hardware layers is achieved wirelessly via a TCP/IP protocol over Wi-Fi, eliminating cumbersome physical tethers and establishing a foundation for advanced Internet of Things (IoT) integration. Software execution relies on a multi-threaded, mutex-protected environment capable of achieving an average sample-to-result latency of 6.02 milliseconds. Real-time joint angle feedback is continuously provided by an AS5048A absolute magnetic encoder via an SPI interface, ensuring kinematic safety and rigid adherence to predefined anatomical limits. Experimental results validate the system's capacity for safe, highly responsive operation, demonstrating that complex classification algorithms and wireless distributed topologies can successfully meet the rigorous latency requirements of wearable rehabilitation robotics, though future iterations may necessitate the integration of brushless actuators to mitigate static friction and low-speed operational limitations.
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Copyright (c) 2026 Антон Васильович Коцюбайло, д-р техн. наук, проф. Олександр Миколайович Лисенко (Автор)
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