Currently, few unpowered exoskeletons are used in the actual rehabilitation of patients. For individuals after anterior cruciate ligament (ACL) reconstruction, they usually exhibit abnormal gait characteristics, and this abnormal gait increases the likelihood of osteoarthritis (OA) in the long term. For these individuals, considering that the knee joint may be unstable due to insufficient muscle strength especially during the weight acceptance phase, this paper proposes a knee-extension-assisted unpowered exoskeleton for knee protection as well as gait rehabilitation training. This lightweight exoskeleton, weighing only 450g, holds promise for enhancing gait stability and lowering the risk of joint osteoarthritis. Custom-designed miniature gas springs contribute to energy storage and provide specified stiffness support to the human knee joint. Gait experiments conducted on six subjects (after ACL reconstruction nearly 1 month) indicated that walking with this exoskeleton significantly reduced peak rectus femoris (RF) muscle activity by 36.4% in weight acceptance phase. The results demonstrated the potential of this exoskeleton to relieve stress on the knee extensor muscles, safeguard patients during recovery, and assist in early gait training.

Pomegranate harvesting remains a challenging task due to the fruit's tough stem, dense canopy, and sensitivity to mechanical damage. Traditional harvesting robots rely on vision-based stem localization, which increases computational complexity and reduces robustness in unstructured orchard environments. This paper presents a dual-shear ring end-effector designed to eliminate the need for precise stem detection, utilizing a self-locking shear mechanism that allows the stem to naturally align between the cutting blades. The system integrates a vision-assisted robotic manipulator for fruit detection and a torque regulation mechanism for optimized cutting force application. Experimental validation demonstrates a success rate of over 90% for stems up to 8 mm in diameter and robust performance even under partial and full occlusion conditions. The results confirm that the proposed system achieves efficient, adaptable, and damage-free harvesting, providing a viable solution for autonomous pomegranate harvesting.

This work presents Dual-Stick, a novel controller with two sticks connected at the end that innovates a Dual-Ray interaction paradigm to enrich raycasting input in Virtual Reality (VR). Dual-Stick leverages the inherent human dexterity in using everyday tools such as clamps and tweezers to adjust the relative angle between two sticks. This design supports Dual-Ray interactions that provide with a heuristics-based enhanced mechanism. It also offers more flexible manipulation by taking advantages of additional degrees of freedom provided by clamping angle. We conducted two studies to evaluate the effectiveness of Dual-Ray in target selection and manipulation tasks. The results indicated that Dual-Ray significantly improved efficiency in target selection compared to single-ray input but did not outperform the enhanced single-ray technique. In terms of manipulation, Dual-Ray effectively reduced completion time and mode switching compared to single-ray input.

Typing in mid-air is often perceived as intuitive yet presents challenges due to finger coactivation, a neuromechanical phenomenon that involves involuntary finger movements stemming from the lack of physical constraints. Previous studies were used to examine and address the impacts of finger coactivation using algorithmic approaches. Alternatively, this paper explores the neuromechanical effects of finger coactivation on mid-air typing, aiming to deepen our understanding and provide valuable insights to improve these interactions. We utilized a wearable device that restrains finger coactivation as a prop to conduct two mid-air studies, including a rapid finger-tapping task and a ten-finger typing task. The results revealed that restraining coactivation not only reduced mispresses, which is a classic coactivated error always considered as harm caused by coactivation. Unexpectedly, the reduction of motor control errors and spelling errors, thinking as non-coactivated errors, also be observed. Additionally, the study evaluated the neural resources involved in motor execution using functional Near Infrared Spectroscopy (fNIRS), which tracked cortical arousal during mid-air typing. The findings demonstrated decreased activation in the primary motor cortex of the left hemisphere when coactivation was restrained, suggesting a diminished motor execution load. This reduction suggests that a portion of neural resources is conserved, which also potentially aligns with perceived lower mental workload and decreased frustration levels.

As touch interactions become ubiquitous in the field of human computer interactions, it is critical to enrich haptic feedback to improve efficiency, accuracy, and immersive experiences. This paper presents HapTag, a thin and flexible actuator to support the integration of push button tactile renderings to daily soft surfaces. Specifically, HapTag works under the principle of hydraulically amplified electroactive actuator (HASEL) while being optimized by embedding a pressure sensing layer, and being activated with a dedicated voltage appliance in response to users’ input actions, resulting in fast response time, controllable and expressive push-button tactile rendering capabilities. HapTag is in a compact formfactor and can be attached, integrated, or embedded on various soft surfaces like cloth, leather, and rubber. Three common push button tactile patterns were adopted and implemented with HapTag. We validated the feasibility and expressiveness of HapTag by demonstrating a series of innovative applications under different circumstances.