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 of muscle 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.