Aleksandr Miklin

This article presents the results of a study exploring sensorimotor integration in upperlimb prostheses through the development of a prototype noninvasive adaptive control system for a bionic hand prosthesis. The study focuses on creating sensory feedback that replicates the properties of biofeedback with a focus on signals from the fingertips, unlike most studies that focus on recognizing patterns in electromyogramm (EMG) signals. The prototype integrates a twocomponent sensor system into a bionic hand prosthesis model with five independent servomotors. This system consists of a surface EMG sensor, which detects muscle activation intent, and thinfilm resistive pressure sensors embedded in the fingertips. The algorithm processes normalized EMG and pressure data in real time using a programmable microcontroller, implementing closedloop grip force adjustment. Key developments include dynamic calibration using the RMS signal envelope, multi-input PID controllers (tuned using the Ziegler – Nichols method) to minimize overshoot, and low-latency force adaptation for objects with variable compliance. The study also included numerical simulations using the Kelvin – Voigt contact model to simulate fingertip contact with soft and rigid materials. A series of experiments using the proposed prototype were conducted for comparison with the numerical simulations. The experimental results are consistent with the numerical simulations, with a smoother increase in force observed when interacting with the soft material. However, the experimental data differ from the model data for a given force setpoint and also have a dead zone associated with the characteristics of the force sensors used in the prototype. This research lays the foundation for accessible adaptive prosthetics and has direct applications in robotic systems.