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The need for more practical, robust, and affordable prosthetic hands for amputees has led to significant advancements in their functionality. However, the challenge of designing prostheses that balance dexterity, functionality, and affordability still persists. The lack of prosthetic designs that can efficiently address the needs of amputees for both heavy manual labor and social interactions, while also being lightweight and dexterous, is still evident. This paper presents a design for an anthropomorphic, adaptive, lightweight, body-powered prosthetic hand built for performing Activities of Daily Living using a selectively lockable differential mechanism. The proposed differential mechanism allows the users to flex and lock the prosthesis' fingers in a wide range of poses and grasps. The performance of the body-powered prosthesis is experimentally validated with three different types of experiments: i) object grasping, ii) total grasping strength, and iii) individual finger force exertion. The resultant prosthetic hand is lightweight and comfortable to wear and is adequately capable of grasping a wide range of objects, including items commonly used in cleaning and cooking applications where waterproofness is essential.

Electromyography (EMG) signals have been used in designing muscle-machine interfaces (MuMIs) for various applications, ranging from entertainment (EMG controlled games) to human assistance and human augmentation (EMG controlled prostheses and exoskeletons). For this, classical machine learning methods such as Random Forest (RF) models have been used to decode EMG signals. However, these methods depend on several stages of signal pre-processing and extraction of hand-crafted features so as to obtain the desired output. In this work, we propose EMG based frameworks for the decoding of object motions in the execution of dexterous, in-hand manipulation tasks using raw EMG signals input and two novel deep learning (DL) techniques called Temporal Multi-Channel Transformers and Vision Transformers. The results obtained are compared, in terms of accuracy and speed of decoding the motion, with RF-based models and Convolutional Neural Networks as a benchmark. The models are trained for 11 subjects in a motion-object specific and motion-object generic way, using the 10-fold cross-validation procedure. This study shows that the performance of MuMIs can be improved by employing DL-based models with raw myoelectric activations instead of developing DL or classic machine learning models with hand-crafted features.

Adaptive, tendon-driven and affordable prosthetic devices have received an increased interest over the last decades. Prosthetic devices range from body-powered solutions to fully actuated systems. Despite the significant progress in the field, most existing solutions are expensive, heavy, and bulky, or they cannot be used for partial hand amputations. In this paper, we focus on the development of adaptive, tendon-driven, glove-based, affordable prostheses for partial hand amputations and we propose two compact and lightweight devices (a body powered and a motor driven version). The efficiency of the devices is experimentally validated and their performance is evaluated using two different types of tests: i) grasping tests that involve different everyday objects and ii) tests that assess the force exertion capabilities of the proposed prostheses.

In this paper we present a series of design directions for the development of affordable, compliant, modular, underactuated robot fingers, that can be used as prostheses by amputees that suffer from various partial hand amputations (index to pinky fingers are considered). Our design is based on parametric models that have been derived from hand anthropometry studies. Various interfaces have been considered in order to control the prosthesis, depending on the type and level of amputation. More precisely: 1) An Electromyography (EMG) based interface is used to control the robot fingers employing the EMG signals of the human forearm muscles 2) A flex sensors based interface is used to record the motion of the intact finger/fingers and predict the motion of the prosthesis implementing a synergistic behavior in an efficient manner, 3) A body powered interface is used for those that want to achieve even lower cost, with robust intuitive operation. Following the proposed design directions, an amputee will be able to replicate our fingers and develop personalized, affordable, light-weight but yet efficient prostheses.