Most prosthetic hands adopt an under-actuated mechanism to achieve dexterous motion performance with a lightweight and anthropomorphic design. Many have been verified in laboratories, and some have already been commercialized. However, a trade-off exists between the dexterity and the light weight of such prosthetic hands. In general, current commercially available prosthetic hands usually consider one aspect at the expense of the other, such as obtaining diversiform hand motions but an increased weight, or achieving lightweight design but with limited motion functions. This study attempts to attain a balance between the two factors, by realizing diversiform hand motions while reducing the weight as far as possible. An anthropomorphic prosthetic hand is proposed with only three servomotors embedded in a human-sized palm, with multiple functions, such as a stable/adaptive grasp and passive hyperextension. The proposed hand can achieve 13 grasp types with over 80% of the grasp motions under the Cutkosky taxonomy, while it weighs only 132.5 g, at less than 36% of the prosthesis weight limitation based on the study of Kay et al.

Although arch motions of the palm substantially contribute to frequent hand grasping, they are usually neglected in the development of prosthetic hands which focuses on digit movements. We designed the arch function for its implementation on an adaptive multi-finger prosthetic hand. The digits from the developed hand can perform adaptive grasping, and two carpometacarpal joints enable the palm of the prosthetic hand to form an arch with the thumb. Moreover, the arch posture can be passively released, mimicking the human hand switching between sphere and medium wrap grasps according to the situation. Other requirements such as weight, cost, and size limitations for hand prostheses were also considered. As a result, we only used three actuators fully embedded in the palm through a novel tendon-driven transmission. Although the prosthetic hand is almost the same size of an adult hand, it weighs only 146 g and can perform 70% of the 10 most frequent grasps.

This paper proposes a novel finger-individuating exoskeleton system with a non-contact leader–follower control strategy that effectively combines motion functionality and individual adaptability. Our solution comprises the following two interactive components: the leader side and the follower side. The leader side processes joint angle information from the healthy hand during motion via a Leap Motion Controller as the system input, providing more flexible and active operations owing to the non-contact manner. Then, as the follower side, the exoskeleton is driven to assist the user’s hand for rehabilitation training according to the input. The exoskeleton mechanism is designed as a universal module that can adapt to various digit sizes and weighs only 40 g. Additionally, the current motion of the exoskeleton is fed back to the system in real time, forming a closed loop to ensure control accuracy. Finally, four experiments validate the design effectiveness and motion performance of the proposed exoskeleton system. The experimental results indicate that our prototype can provide an average force of about 16.5 N for the whole hand during flexing, and the success rate reaches 82.03% in grasping tasks. Importantly, the proposed prototype holds promise for improving rehabilitation outcomes, offering diverse options for different stroke stages or application scenarios.

Stroke-induced hand motor dysfunction severely limits activities of daily living (ADL). While conventional systems face challenges in portability and sustained actuation accuracy, this work addresses these limitations through an integrated adaptive control framework and a lightweight 10-degrees-of-freedom (DoFs) tendon-driven exoskeleton. The system employs a rigid–flexible coupling design with a wearable mass under 300 g, ensuring compatibility across various finger lengths. The system is implemented via a motor imagery-based brain–computer interface (MI-BCI); by processing 64-channel electroencephalogram (EEG) signals, the system adaptively maps motor intent into three discrete grasp intensity levels (20%, 50%, and 80% maximum voluntary contraction). To reduce cognitive load and enhance system stability during rehabilitation, we propose a novel “Force–Topology Coupling” control paradigm. This paradigm functions as a synergistic filter, leveraging the correlation between intended effort level (IEL) and grasp taxonomy to map intensity levels to ADL-specific grasps (lateral, precision, and power). Validation with healthy subjects demonstrated 0° to 90° joint mobility and the successful execution of 9 ADL tasks. The results verify the efficacy of utilizing adaptive MI-BCI modulation to trigger biomechanically precise assistance, establishing a foundational computational paradigm with significant potential for clinical stroke rehabilitation.

To meet daily grasping needs under lightweight, low-complexity wearable constraints, this study proposes an underactuated multi-finger prosthetic hand with transmission–control co-design to achieve predictable multi-joint synergies and stable grasps under limited actuation. The prototype uses six miniature motors to drive 14 joint degrees of freedom (DOFs): four fingers have active metacarpophalangeal actuation with tendon-driven underactuated proximal and distal interphalangeal joints, while the thumb provides two independently controlled DOFs for opposition expansion and posture adjustment. It supports five-finger power grasps, tripod pinches, and lateral pinches. To mitigate tendon slack and stroke inconsistency, active/passive tendon-length constraints are defined, and an equal-stroke configuration is obtained via chord-to-arc mapping. A layered STM32F767-based controller combines a reference rotation range limit (free motion) with encoder speed-decay detection (contact/near-stall) to realize per-finger termination and overdrive protection without force/tactile sensors. Experiments report a total mass of 176.6 g and a peak single-finger driving force of approximately 2.8 N. Following the Feix GRASP taxonomy (33 types), the hand reproduces 24 types (72.7%), covering power, intermediate and precision grasps, both thumb abduction/adduction postures, and palm–pad–side opposition/contact, with stable grasp formation across objects of varying geometries.