This review article aims to provide an updated and comprehensive overview of the latest trends in adult upper limb prostheses, specifically targeting various amputation levels such as transradial, transmetacarpal, transcarpal, and transhumeral. A systematic search was conducted across multiple databases, including IEEE Xplore, MDPI, Scopus, Frontiers, and Espacenet, covering from 2018 to 2023. After applying exclusion criteria, 49 scientific articles (33 patents and 16 commercial prostheses) were meticulously selected for review. The article offers an in-depth analysis of several critical aspects of upper limb prostheses. It discusses the evolution and current state of input control mechanisms, the number of degrees of freedom, and the variety of grips available in prostheses, all tailored according to the level of amputation. Additionally, the review delves into the selection of materials used in developing these prostheses and examines the progression of technology readiness levels. A significant focus is also placed on the evolution of prosthesis weight over the years for different amputation levels. Moreover, the review identifies and explores critical technological challenges and prospects in upper limb prostheses. Finally, the article culminates with a conclusion that encapsulates the key findings and insights on the advancements and ongoing developments in this field.

Globally, 2.5% of upper limb amputations are transhumeral, and both mechanical and electronic prosthetics are being developed for individuals with this condition. Mechanics often require compensatory movements that can lead to awkward gestures. Electronic types are mainly controlled by superficial electromyography (sEMG). However, in proximal amputations, the residual limb is utilized less frequently in daily activities. Muscle shortening increases with time and results in weakened sEMG readings. Therefore, sEMG-controlled models exhibit a low success rate in executing gestures. The LIBRA NeuroLimb prosthesis is introduced to address this problem. It features three active and four passive degrees of freedom (DOF), offers up to 8 h of operation, and employs a hybrid control system that combines sEMG and electroencephalography (EEG) signal classification. The sEMG and EEG classification models achieve up to 99% and 76% accuracy, respectively, enabling precise real-time control. The prosthesis can perform a grip within as little as 0.3 s, exerting up to 21.26 N of pinch force. Training and validation sessions were conducted with two volunteers. Assessed with the “AM-ULA” test, scores of 222 and 144 demonstrated the prosthesis’s potential to improve the user’s ability to perform daily activities. Future work will prioritize enhancing the mechanical strength, increasing active DOF, and refining real-world usability.

This paper presents the design of a new haptic feedback device for transradial myoelectric upper limb prosthesis that allows the amputee person to perceive the sensation of force-gripping and object-sliding. The system designed has three mechanical-actuator units to convey the sensation of force, and one vibrotactile unit to transmit the sensation of object sliding. The device designed will be placed on the user's amputee forearm. In order to validate the design of the structure, a stress analysis through Finite Element Method (FEM) is conducted.

Upper-limb amputations significantly affect independence and quality of life, particularly in low-income regions where advanced prosthetic technology is costly and lacks adequate personalization. Conventional myoelectric prostheses, while offering functional restoration, have limited adaptability and high cost. This study presents a personalized transradial myoelectric prosthesis that combines additive manufacturing and Artificial Intelligence (AI) control, offering an accessible and high-performance solution. The prosthesis design utilizes additive manufacturing (3D printing) for anatomical personalization via 3D scanning and parametric modeling. An AI-driven control system utilizes machine learning to classify electromyography (EMG) signals in real-time, specifically detecting the user’s intention to perform flexion or extension movements, and tailoring responses to individual users. Evaluation employed the "Brief Activity Measure for Upper Limb Amputees (BAM-ULA)" protocol with nine participants with transradial amputations. Trials with the nine participants yielded an average BAM-ULA score of 7.4 out of 10 (Standard Deviation (SD) 0.7). This demonstrated robust functional performance, comparable to high-end commercial devices in initial tests. Gross motor tasks saw 100% success rates; fine motor tasks, 22.2%. Integrating AI and additive manufacturing resulted in an affordable, high-performance, personalized prosthesis. This work highlights how localized digital manufacturing enables accessible customization for users in low-resource settings. The main novelty is this validated integration of personalized additive manufacturing and adaptive AI control in an affordable transradial prosthesis addressing the needs of developing countries.

This paper presents the implementation of a Finger force detection platform capable to measure the exerted forces by the index, middle, ring and little finger in single-axis direction (flexion); and in dual-axis direction (flexion/adduction) by the thumb. It can be adapted to different sizes in both right or left hand. A test protocol is established. Also, a Graphical User Interface (GUI) is developed in order to assist the subject during the test. The results show the maximum forces developed by each finger among Peruvian participants.