Abstract
Background: Advancements in low-cost additivemanufacturing and artificial intelligence have
enabled new avenues for developing accessiblemyoelectric prostheses. However, achieving
reliable real-time control and ensuring mechanical durability remain significant challenges,
particularly for affordable systems designed for resource-constrained settings. Objective: This
study aimed to design and validate a low-cost, 3D-printed prosthetic arm that integrates
single-channel electromyography (EMG) sensing with machine learning for real-time gesture
classification. The device incorporates an anatomically inspired structure with 14 passive mechanical
degrees of freedom (DOF) and 5 actively actuated tendon-driven DOF. The objective
was to evaluate the system’s ability to recognize open, close, and power-grip gestures and to
assess its functional grasping performance. Method: A Fast Fourier Transform (FFT)-based
feature extraction pipeline was implemented on single-channel EMG data collected from ablebodied
participants. A Support Vector Machine (SVM) classifier was trained on 5000 EMG
samples to distinguish three gesture classes and benchmarked against alternative models. Mechanical
performance was assessed through power-grip evaluation, while material feasibility
was examined using PLA-based 3D-printed components. No amputee trials or long-term durability
tests were conducted in this phase. Results: The SVMclassifier achieved 92.7% accuracy,
outperforming K-Nearest Neighbors and Artificial Neural Networks. The prosthetic hand
demonstrated a 96.4%power-grip success rate, confirming stable grasping performance despite
its simplified tendon-driven actuation. Limitations include the reliance on single-channel EMG,
testing restricted to able-bodied subjects, and the absence of dynamic loading or long-term
mechanical reliability assessments, which collectively limit clinical generalizability. Overall,
the findings confirm the technical feasibility of integrating low-cost EMG sensing, machine
learning, and 3D printing for real-time prosthetic control while emphasizing the need for
expanded biomechanical testing and amputee-specific validation prior to clinical application.