Optical-Tattoo Sensing for Non-contact Control of Bionic Limbs: A Conceptual Framework

Conventional pattern recognition (PR) methods in bionic hand systems are reliant on contact-based sensors and remain vulnerable to the inherent instability of biological signals. This study presents an alternative method that uses a novel non-contact PR approach to classify the motion of individual fingers and hand grasping gestures. This method does not depend on biosignals; instead, it utilises optical sensing. To enhance optical differentiation during muscle contraction, interference-pigment tattoos were applied to the targeted areas of the skin of the muscular areas. In this approach, muscle activity in the forearm’s flexor medialis region is captured through red-green-blue (RGB) colour information and reflected light intensity (LI), which is then processed by a low execution time (ET) and an accurate recognition system. Two integrated sensors were used to precisely detect light reflections associated with a group ofmuscle activities. To minimise feeding data to the system, the RGB signals were clustered by light intensity using the k-nearest neighbours (KNN) algorithm, and then both time-domain and wavelet features were extracted and classified using four classifiers, including linear discriminant analysis (LDA), support vector machine (SVM),multilayer perceptron (MLP), and convolutional neural network (CNN). In the golden-integrated tattoo design, the system demonstrated a mean accuracy of 97.74%±0.8% across twelve intact participants and a mean accuracy of 95.23%±1.2% across six wrist disarticulation amputees, both classified as athletes and non-athletes. This underscores its strong potential as an alternative method to enhance the accuracy, intuitiveness, and reliability of prosthetic hand control systems.