Anthropomorphic robotic hands are seeking to achieve key features such as multi-degree-of-freedom motion ability, bi-directional actuation, high adaptability, and sufficient stiffness. In this research, we propose a 10 active degrees-of-freedom anthropomorphic robotic hand with a soft-rigid hybrid structure and positive-negative pneumatic actuation. The fingers of the hand utilize pneumatic actuators composed of soft bellows to actuate a rigid skeleton for bending motion with unidirectional-stretchable nylon elastic fabric as a strain limiting layer. The positive-negative pneumatic actuation mimics the flexors and extensors of the human hand to implement bi-directional finger actuation. We present a prototype implementation of this robotic hand and provide a preliminary evaluation. The experimental results show that the combined positive-negative pneumatic actuation can extend the workspace of the finger and increase the finger stiffness compared to using only positive pneumatic actuation. The proposed robotic hand has good comprehensive performance (generated a blocking force of 7.5 N, scored seven in the Kapandji test, achieved 32 of the 33 grasp postures from the Feix taxonomy, and could implement five human grasping strategies) compared to other robotic hands in literatures.

The exoskeleton as functional wearable equipment has been increasingly used in working environments. However, the effects of wearing an exoskeleton on human thermal responses are still unknown. In this study, 10 male package handlers were exposed to 10 °C (COLD) and 25 °C (TEMP) ambient temperatures while performing a 10 kg lifting task (LIFTING) and sedentary (REST) both with (EXO) and without the exoskeleton (WEXO). Thermal responses, including the metabolic rate and mean skin temperature (MST), were continuously measured. Thermal comfort, thermal sensation and sweat feeling were also recorded. For LIFTING, metabolic heat production is significant decrease with the exoskeleton support. The MST and thermal sensation significantly increase when wearing the exoskeleton, but thermal discomfort and sweating are only aggravated in TEMP. For REST, MST and thermal sensation are also increased by the exoskeleton, and there is no significant difference in the metabolic rate between EXO and WEXO. The thermal comfort is significantly improved by wearing the exoskeleton only in COLD. The results suggest that the passive exoskeleton increases the local clothing insulation, and the way of wearing reduces the “pumping effect”, which makes a difference in the thermal response between COLD and TEMP. Designers need to develop appropriate usage strategies according to the operative temperature.

Based on the existing research on sound symbolism and crossmodal correspondence, this study proposed an extended research on cross-modal correspondence between various sound attributes and color properties in a group of non-synesthetes. In Experiment 1, we assessed the associations between each property of sounds and colors. Twenty sounds with five auditory properties (pitch, roughness, sharpness, tempo and discontinuity), each varied in four levels, were used as the sound stimuli. Forty-nine colors with different hues, saturation and brightness were used to match to those sounds. Result revealed that besides pitch and tempo, roughness and sharpness also played roles in sound-color correspondence. Reaction times of sound-hue were a little longer than the reaction times of sound-lightness. In Experiment 2, a speeded target discrimination task was used to assess whether the associations between sound attributes and color properties could invoke natural cross-modal correspondence and improve participants’ cognitive efficiency in cognitive tasks. Several typical sound-color pairings were selected according to the results of Experiment 1. Participants were divided into two groups (congruent and incongruent). In each trial participants had to judge whether the presented color could appropriately be associated with the sound stimuli. Result revealed that participants responded more quickly and accurately in the congruent group than in the incongruent group. It was also found that there was no significant difference in reaction times and error rates between sound-hue and sound-lightness. The results of Experiment 1 and 2 indicate the existence of a robust crossmodal correspondence between multiple attributes of sound and color, which also has strong influence on cognitive tasks. The inconsistency of the reaction times between sound-hue and sound-lightness in Experiment 1 and 2 is probably owing to the difference in experimental protocol, which indicates that the complexity of experiment design may be an important factor in crossmodal correspondence phenomena.

Teleoperation robot systems can help humans perform tasks in unstructured environments. However, non-intuitive control interfaces using only a keyboard or joystick and physiological tremor reduce the performance of teleoperation. This paper presents an intuitive control interface based on the wearable device gForcePro+ armband. Two gForcePro+ armbands are worn at the centroid of the upper arm and forearm, respectively. Firstly, the kinematics model of the human arm is established, and the inertial measurement units (IMUs) are used to capture the position and orientation information of the end of the arm. Then, a regression model of angular transformation is developed for the phenomenon that the rotation axis of the torsion joint is not perfectly aligned with the limb segment during motion, which can be applied to different individuals. Finally, to attenuate the physiological tremor, a variable gain extended Kalman filter (EKF) fusing sEMG signals is developed. The described control interface shows good attitude estimation accuracy compared to the VICON optical capture system, with an average angular RMSE of 4.837° ± 1.433°. The performance of the described filtering method is tested using the xMate3 Pro robot, and the results show it can improve the tracking performance of the robot and reduce the tremor.

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.

Individual physiological differences constrain the cross-user application of joint angle estimation models based on surface electromyography (sEMG) signals. Current cross-user methods for myoelectric joint angle estimation often involve the use of angle or optical sensors, which increases the training burden and costs for new users. To enable new users to perform joint angle estimation solely using sEMG sensors, this study proposes a hybrid unsupervised domain adaptation network that combines multi-order metric and adversarial mechanisms (MADAN). MADAN aims to minimize the distribution differences of sEMG signals between the source and target subjects by first aligning the domain distributions and then increasing domain confusion. The effectiveness of MADAN is validated through cross-subject wrist joint angle estimation involving 10 subjects, with an estimation frequency of 20Hz. The results demonstrate that MADAN achieves a significantly higher average coefficient of determination (0.8688 ± 0.0307) compared to other advanced UDA cross-subject estimation methods, such as TCDA (0.6534 ± 0.234) and ADANN (0.6655 ± 0.2255). Notably, MADAN requires only 20 seconds of unlabeled samples from the target subject for training. This work is expected to alleviate the cost and training burden for new users in performing myoelectric continuous motion estimation.