Recently, a shared-autonomous scheme has been introduced into prosthetic hand control field, where the user provides high-level intent by moving the hand towards the target, and the artificial intelligence system autonomously executes low-level control (e.g., grasp and release the object). This system reduces user workload but risks unintended grasp or release actions without explicit user control. In particular, release actions remain challenging, as vision-based autonomous systems typically assume that proximity to a supporting surface signals the user's intent to let go, making mid-air release tasks difficult and error-prone. This study presents an inertial measurement unit (IMU)-based gesture-triggered interface enabling voluntary initiation or override of grasp and release actions to the autonomous system. A real-time motion detection algorithm recognizes three deliberate upper-limb gestures: shoulder shrug, elbow flap, and wrist shake, across three control paradigms: autonomous, hybrid, and manual. In a controlled study with 14 able-bodied participants and one individual with an upper-limb difference, the elbow flap emerged as the most preferred gesture (66% preference) and achieved 95% mean successful rate. Manual mode produced the highest accuracy (95%), while autonomous mode and hybrid mode were most preferred for daily use (38%). Results suggest that IMU-based voluntary triggers enhance alignment between user intent and prosthetic action, improving reliability and perceived control. This approach offers a practical pathway toward safer, more adaptable prosthetic systems and can be extended to real-world applications requiring rapid, intentional overrides of autonomous behavior.

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