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With the advances in microfluidics, electrowetting-on-dielectric (EWOD) chips have widely been applied to various biological and chemical laboratory protocols. Glass-based EWOD chips with nonregular electrodes are proposed, which allow more reliable droplet operations and facilitate the integration of optical sensors for many biochemical applications. Furthermore, nonregular electrode designs are utilized in EWOD chips, e.g., interdigitated electrodes for more reliable droplet manipulation, custom shaped electrodes for specific applications like concentric heating, etc. However, due to the technical challenges of fabricating multilayer interconnection on the glass substrate, e.g., unreliable process and high cost, both control electrodes and wires are fabricated with a single-layer configuration, which poses significant challenges to pin selection for nonregular electrodes. In this article, we propose a minimum-cost flow-based routing algorithm called NR-Router+ that features efficient and robust routing for single-layer EWOD chips with nonregular electrodes. To the best of our knowledge, this is the first work that overcomes the aforementioned challenges. We construct a minimum-cost flow algorithm to generate optimal routing paths followed by a light-weight model to handle flow capacity. A grid reduction strategy is proposed to reduce the computational overhead. Additionally, a flow collocation algorithm based on integer linear programming is presented to efficiently prevent wire overlapping. Experimental results show that NR-Router+ achieves 100% routability while minimizing wirelength with shorter run time. Moreover, NR-Router+ can generate mask files feasible for manufacturing via adjustments of design parameters, thus demonstrating its robustness and efficiency.

Photon‐to‐Photon latency indicates the time it takes for the device to transmit events captured from the real world to display of headset in real‐time, and this lag is 0 for AR, but is noticeable for MR that uses Video See‐Through. The industry‐leading product assert to deliver a PTP performance within a span of 12 milliseconds. Therefore, high‐precision equipment is required for testing. We have developed a PTP test equipment that can achieve a test accuracy of 0.08ms. With such an approach, we discovered that a range of commercially available products vary in their performance levels, while the top‐performing product offers a PTP latency of less than 10ms. In addition, our method can avoid the influence of motion blur from the VST camera and can test the PTP latency performance of the system for a long time and with high accuracy. At the same time, different testing methods are customized for different users, such as product developers and evaluation agencies.

Locomotion transition recognition with external disturbances is a key issue in lower limb robotic prostheses. Redonning the prosthetic socket and individual differences are encountered frequently in daily life. They change the feature distribution and further fail the previously trained recognition model. To bridge the technical gap between laboratory validation and practical demands, researchers need to construct an adaptive recognition method with high accuracy, quick response, time-efficient calibration, and minimal interference to the human body. Our study developed an adaptive recognition algorithm based on two miniaturized inertial sensors (anterior thigh and forefoot) and a foot pressure sensor, which can easily be integrated into the prosthesis. The algorithm fused probability-based fuzzy classifiers, which took heuristic-based features as inputs with a dynamic-time-warping-based automatic template generation block. It enables the recognition model to quickly fit the parameters for an untrained mode after external disturbances. We validated the proposed adaptive recognition method on 13 healthy subjects, performing 18 motion transitions with random interday intervals and across user evaluation, both offline and online. Even with the data of one subject as the prior knowledge, the other subjects produced an average accuracy of 98.04% and an average transition time latency of −180.99 ms without manual training in interday and intersubject uses. We also tested the method on two subjects using robotic prostheses, one person with a transfemoral amputation and the other person with a transtibial amputation. The average accuracy was still as high as 98.06% (transfemoral) and 100% (transtibial) without manual calibration after seven days of intervals. The results proved that the adaptive recognition method is robust to sensor redonning and user changes. Compared with the state-of-the-art methods, our study makes a further step toward the practical use of robotic prostheses. Future studies will be conducted to implement the algorithm on the robotic prosthesis for more extensive tests.

This article addresses the modeling and control of bioinspired cable-driven actuator that can mimic the three activation modes of human muscles. First, the dynamic model of the actuator is established by utilizing the coordinate transformation and kinetic energy theorem for controller and observer design. Then, a low-complexity output feedback controller with prescribed performance is proposed, so that the driving disc can track the rotation of the cable pulley without overshoot during the operation of the actuator. Specifically, the collision caused by overshoot during the tracking process must be avoided, as it can cause a sudden change of the cable tension, impact noise, and even mechanical parts damage. The presented low-complexity control strategy guarantees that the tracking error does not violate the predefined performance constraint and can converge to a predetermined residual set in finite time by introducing a novel error transformation function and a performance function. In addition, to avoid using angular velocity sensor, a finite-time observer with interval type-2 fuzzy system and adaptive technique is developed to estimate the angular speed of the driving disc. Finally, simulation studies and prototype experiments are performed to validate the effectiveness of the proposed control scheme on the bioinspired actuator.

Wearable robots involve human–robot interactions, and capturing the electrical energy from human dynamics during interactions is gaining increasing interest. However, previous studies have not thoroughly investigated the maximum mechanical energy and the efficiency of mechanical-to-electrical energy conversion between robotic prostheses and humans. In this study, the effects of motor rotation speed $\omega _{\text{mo}}$, pulsewidth modulation (PWM) duty cycle $D$, replacing mosfet and replacing a motor on efficiency $\eta$ were investigated through theoretical analysis, a bench test, and walking experiments (N = 5). The theoretical analysis involved studying the energy flowchart during the mechanical-to-electrical energy conversion using a bench test. Then, walking experiments were conducted and the results showed that replacing a motor led to a 6% and 10% increase in energy regeneration at 1.3 and 1.1 m/s (self-selected), respectively. Based on the efficiency obtained from a bench test and walking experiments, the maximum mechanical power for a robotic prosthesis was obtained and ranged from 4.35 to 8.23 W, which showed the possibility of a 100% self-charged-powered robotic prosthesis. This study presents a feasible approach to analyze and improve the efficiency of mechanical-to-electrical energy conversion between robots and humans.

Wearable robotic devices interact with humans by applying the assistive force in parallel with muscle–tendon systems. Designing actuations in mimicking the natural activation patterns of human muscles is a promising way to optimize the performance of wearable robots. In this article, we propose a bioinspired cable-driven actuation system capable of providing anisometric contractions (including concentric and eccentric contraction) assistance or nearly acting as a transparent device in an efficient manner. A novel clutch–spring mechanism is employed to accomplish switches between assistive modes and the transparent mode. Corresponding control strategies coordinating with the mechanical design were presented and described in detail. Multiple evaluations were conducted on a test bench to characterize the system's performance. The closed-loop bandwidth of the system running concentric assistance control was 18.2 Hz. The R-squared values of linear fitting under eccentric assistance control were above 0.99. The engagement time of the proposed clutch was about 90 ms. Applying the actuation to an ankle exoskeleton, multiple walking experiments with electromyography measurements were performed on five subjects to show its application potential in existing wearable robots. Experimental results revealed that the proposed design could reduce soleus muscle activity by 27.32% compared with normal walking. This study highlights the importance of functional bionic design in human-assistance-related devices and introduces a general actuation system that could be directly applied to existing cable-driven wearable robots.

Accurate estimation of gait phases is crucial in controlling wearable robots designed to restore or enhance human walking capabilities. Asynchronous human-machine motions can lead to reduced efficiency, increased interaction loads, and potential harm to the human body. Variations in walking speeds and terrain ramps pose additional challenges to continuous phase estimation. This article presents a novel synergy method based on muscle deformations measured by flexible wearable sensors to dynamically monitor gait phases while adapting to changes in speed and terrain ramp. Polar coordinate diagrams are used to elucidate the cyclic features of joint angles and muscle deformations during walking. The consistency of muscle deformation synergy is studied to build a phase estimation model, with parameters being determined by neural network algorithms. The average root-mean-square error for the estimated phase is less than 4.4% within a speed range of 3–6 km/h and less than 8.8% in a ramp range of 0°–10°. The muscle deformation presents a viable alternative in scenarios involving changes in speed, outperforming joint kinematics in gait phase estimation amidst variations in ramp inclinations. The proposed unified muscle synergy model, adaptable across a wide spectrum of walking speeds and ramp inclinations, holds promise for enhancing human-machine coordination for robotic assistance and rehabilitation.

Motivated by the need for underwater exoskeleton control, swimming phase monitoring is critical to adjust robotic assistance to human motions. However, harsh underwater environments bring in challenges of poor robustness and serious disturbances to the few available sensing methods leading to coarse phase segmentation. Given skeletal muscle activation for limb joint motions, this article proposes a muscle–joint synergy method to combine muscle mechanics captured by a novel capacitive sensor with joint kinematics by traditional inertial measurements. Development of the wearable sensing system is provided with details on design, fabrication, and assembly. The muscle–joint synergy model is rigorously formulated and experimentally validated with breaststroke data for the first time. Immediate applications are illustrated with discrete mode identification and continuous phase monitoring, justifying the robustness of the proposed method to various artificial intelligent algorithms and different tested subjects. This work is anticipated to be valuable to unveil the mechanism of human natural motions for human-centered robotics, sport science, and rehabilitation engineering in the future.

Flexible ankle motions directly affect the kick effect of breaststroke. Accurate detection of critical moments of ankles [Feet Kick Start (FKS) and Feet Kick End (FKE)] in breaststroke is significant for sports and underwater assistance. In this article, we propose a wearable sensing system that can be used to label and detect critical moments of ankles in breaststroke based on the idea of supervised pattern recognition. Using the optical system as a reference, the feasibility of the flex sensor as a label was verified by the land experiment. Combined with the label data from the flex sensor AD value, the model was trained using the inertial measurement unit (IMU) data on the foot and shank at normal swimming speed. Based on the classification model, critical moment detection experiments were conducted on three subjects with different swimming speeds (slow, normal, and fast). The time differences of FKS detection were −6.0, 0.7, and −7.5 ms, which accounted for −0.23%, 0.12%, and −0.39% of the corresponding period, respectively. The time differences of FKE detection were −20.7, 3.50, and −23.5 ms, which accounted for −0.90%, 0.29%, and −1.21% of the corresponding period, respectively. To compare with the recognition algorithm, we have studied the threshold detection method for four variables. The results show that the wearable sensing system and recognition algorithm proposed in this article can be used to label and detect critical moments of ankles in breaststroke effectively, and detection results have high accuracy and small time difference, as well as certain speed adaptability.

Working noise is an important consideration that hinders the acceptance of powered prostheses by amputees. In this article, we present a powered knee prosthesis with low noise and high backdrivability. The powertrain of the proposed prosthesis consists of a motor and a four-stage synchronous belt to decrease working noise. At the same time, benefiting from the low impedance of the motor and low impedance design, the proposed prosthesis is endowed with the characteristics of low impedance, and thus, high backdrivability. A three-layer control method based on a finite state machine is implemented, and walking experiments have been carried out on a transfemoral amputee. The results show the characteristics of the proposed prosthesis in low impedance, low noise, and the ability to reproduce a natural gait. In particular, the working noise of the proposed prosthesis is less than 46 dBA most time, which is lower than common household appliances, and may have positive effects on the acceptance of daily uses.

Knee exoskeletons have gained increasing attention for gait training and intervention. The effectiveness of conventional rigid exoskeletons is degraded by bulkiness, high inertia, and joint misalignment. The soft configurations relax the constraints on the knee joint, thus circumvent the misalignment issue, and reduce the system size and weight. However, the soft exoskeletons sacrifice the load sharing capability from rigid support, and also the mechanical stop for constraining joint working range to avoid unsafe knee hyperextension. This article presents a novel rigid-soft hybrid exoskeleton to provide bidirectional interventions safely for knee joint motion. The design is lightweight (1.2 kg to wear), avoids the rigid rotation center that induces knee joint misalignment, has minimal impact on normal walking kinematics, and is able to provide support to the human body. A gait intervention strategy is designed under the hybrid model of human and exoskeleton. Comprehensive experiments are conducted to demonstrate the capability of the presented exoskeleton on gait intervention.

This article presents a robotic trunk support system that provides dynamic support and direct force/moment measurement, to assist trunk movement and train related core muscle groups. The proposed system has four degrees of freedom, three in the coronal plane and one in flexion/extension motion, which has a larger range than the existing robotic braces. The system was designed based on the serial–parallel mechanism. The serial module is responsible for the flexion/extension motion, and the parallel module is responsible for three degrees of freedom in the coronal plane. The kinematic modeling and singularity analysis of the parallel mechanism were discussed in detail. Then, the position control and force control of the serial control actuator were implemented, and the experiments were carried out to evaluate the performance of the controller. The effects on human body in force control were discussed and explained. A reduction in anterior muscles activation and an increase of dorsal muscles activation were exhibited when flexion torque was applied, while the trunk core muscle groups have opposite activation when extension torque was applied. The results indicate that the proposed robotic brace has potentials in the application to trunk core muscle groups training and corresponding rehabilitation.

Due to the inherent safety and high mass/heat transfer efficiency of microreactor, the continuous micro-reaction technology has been widely applied in hazardous chemistry recently. In this work, a continuous ozonolysis system based on micro-packed bed reactors (µPBRs) was developed with the synthesis of benzaldehyde as a model reaction. The effects of operating variables (e.g., stirring time, molar ozone/olefin ratio, reaction pressure, reaction temperature and liquid residence time) on the olefins conversion and product distribution were investigated. Based on the experimental results, the optimum reaction conditions are as follows: stirring time 1 h, molar ratio of ozone to olefin 1.2, reaction pressure 0.1 MPa, and the reaction temperature ranging from − 15 to 10℃, as opposed to the low temperature (<-50℃) routinely employed for batch operation. In addition, the full conversion of styrene and a benzaldehyde yield of ~ 93% was observed with the liquid residence time of 3.8–30.8 s. Consequently, the flow ozonolysis technique upon µPBRs allows for a sustainable, safe and efficient approach to oxidize olefins to aldehydes/ketones compared to traditional methods.

Powerful tactile perception is keystone for robots to achieve human-like dexterous manipulation. However, endowing robots with tactile perception capabilities that approach or even surpass those of humans is a challenge. Organisms can perceive complex environments rapidly, and almost all perception is related to the directional transport of ions. Inspired by this mechanism, we design a flexible dual-responsive skin (FDRS) based on ions migration for proximity and tactile somatosensation, which is consisting of single-electrode triboelectric nanogenerator and iontronic sensor, achieving surpassing-skin capabilities. The single-electrode triboelectric nanogenerator can encode the proximity information of approaching targets into a series of voltage pulses with fast response time (100 μs). The iontronic tactile unit based on the hierarchical micro-hemispherical gel layer achieves high linearity (R2= 0.998) over a broad range of 0–700 kPa. We demonstrate the applications of FDRS for three-dimensional object recognition and robotic control. These capabilities of the multilayer integrated FDRS provide new perspectives for the future development of human-like dexterous robotic manipulation.

This research demonstrates the production of mesoporous activated carbon from sargassum fusiforme viaphysical activation with carbon dioxide. Central composite design was applied to conduct the experiments at different levels by altering three operating parameters. Activation temperature (766–934 °C), CO2flow rate (0.8–2.8 L⋅min−1) and activation time (5–55 min) were the variables examined in this study. The effect of parameters on the specific surface area, total pore volume and burn-out rate of activated carbon was studied, and the influential parameters of methylene blue adsorption value were identified employing analysis of variance. The optimum conditions for maximum methylene blue adsorption value were: activation temperature = 900 °C, activation time = 29.05 min and CO2flow rate = 1.8 L⋅min−1. The activated carbon produced under optimum conditions was characterized by BET, FTIR and SEM. The adsorption behavior on congo red was studied. The effect of parameters on the adsorbent dosage, temperature, PH and initial congo red concentration was investigated. The adsorption properties of the activated carbon were investigated by kinetics. The equilibrium removal rate and maximum adsorption capacity reaches up to 94.72%, 234 mg⋅g−1, respectively when initial congo red concentration is 200 mg⋅L−1under adsorbent dosage (0.8 g⋅L−1), temperature (30 °C), PH7.

The adsorption of CO2using layered double hydroxides (LDHs) such as Mg-Al LDH is both technically feasible and economical. Although some approaches to the modification of LDHs have proved fruitful, there is still a need for further improvement in the adsorption capacity of modified LDHs. Here, a new optimization direction for amino-modified Mg-Al-LDHs is proposed based on using DFT and AIMD simulations to elucidate the mechanism of CO2diffusion. The proposed approach was then evaluated experimentally. Theoretical calculations showed that the adsorption energy of ordinary amino-modified LDHs was low, at − 0.56 eV. DFT simulations showed that the number of interlayer water molecules affects the CO2adsorption energy, with the optimum adsorption energy of − 0.78 eV obtained in the presence one interlayer water molecule. Experimental analysis showed that the drying time affects the amount of interlayer water and thus the CO2adsorption capacity, with the maximum adsorption capacity of 1.7424 mmol/g obtained after drying for 5 h. This study provides useful insights towards the development of materials such as Mg-Al LDHs for efficient CO2adsorption and highlights the critical role of interlayer-bound water on the adsorption performance.

Since genetic algorithm (GA) presented decades ago, large amount of intelligent algorithms and their improvements and mixtures have been putting forward one after another. However, little works have been done to extend their applications and verify their competence in different problems. For each specific complex problem, people always take a long time to find appropriate intelligent algorithm and develop improvements. To overcome these shortcomings, new dynamic configuration methods for intelligent algorithms (DC-IA) is presented in this paper on the basis of the requirements of three kinds of algorithm users. It separates the optimization problems and intelligent algorithms, modularizes each step of algorithms and extracts their core operators. Based on the coarse-grained operator modules, three-layer dynamical configurations, i.e., parameter-based configuration, operator-based configuration and algorithm-based configuration, are fully exploited and implemented. Under these methods, dozens of hybrid and improved intelligent algorithms can be easily produced in a few minutes just based on several configurable operator modules. Also, problem-oriented customizations in configurations can further extend the application range and advance the efficiency of the existing operators enormously. Experiments based on the established configuration platform verify the new configuration ways of applying and improving intelligent algorithm for both numerical and combinatorial optimization problems in industries on aspects of flexibility, robustness, and reusability.

This paper presents a hierarchical control scheme for a robotic transtibial prosthesis to realize smooth locomotion transitions between level ground and ramps. The high level controller identifies current terrain with a fuzzy logic based method and decides the corresponding parameters for lower level controllers. The middle level controller detects different gait phases of one gait cycle on a specific terrain and decides which control method to be used for the current phase. Based on the identified terrain and gait phase, the low level controller performs damping control for controlled plantarflexion/dorsiflexion, and angle control for the swing phase. To evaluate the effectiveness of the proposed control scheme, we design and construct a robotic transtibial prosthesis prototype. Experimental results on normalized stance time, normalized peak ground reaction force, and center of pressure shift in anterior/posterior show improved gait symmetry and walking stability of an amputee subject on ramps with the proposed control scheme. A 17-s long trial that includes different terrains and terrain transitions indicates that the proposed scheme can realize smooth locomotion transitions between level ground and ramps.

Current finite-state control strategies for powered below-knee prosthesis, though effective to the normal gait, can not eliminate the disturbance of abnormal gaits such as slip and stamp. In addition, toe joint is not taken into consideration. This paper presents a finite-state control strategy for a powered below-knee prosthesis with ankle and toe. We first introduce the concerned prosthesis prototype in detail. By dividing the walking gait with toe and joint into more states and setting stricter transition conditions, the gait identification becomes more accurate and gait disturbance such as slip and stamp can be eliminated. Experimental results show that the proposed method improves the accuracy of gait identification. It makes the motion of the prosthesis more natural and provides better bases for control.

Flexible pressure sensors with high sensitivity are highly desired in wearable electronics and human–machine interaction. Introducing the surface microstructures to the capacitive-type sensors can improve sensitivity and reduce response time. However, conventional techniques for the fabrication of highly sensitive and large-area pressure sensors still remain challenging. Here, a template synthesis approach is reported for fabrication of a large-area and low-cost ionic micropillar array templated from track-etch membranes. The pressure sensors based on the ionic micropillars gel dielectric layers exhibit a low limit of detection (∼0.5 Pa) and high sensitivity (14.83 kPa–1) in the low-pressure regime (0–5 kPa) and linear sensitivity (1.96 kPa–1) over a wide pressure range of 24–230 kPa. The versatility of the sensors is demonstrated in various human physiological signal detection scenarios and spatial pressure distribution. Furthermore, a real-time pressure mapping insole was fabricated on the basis of a large-area micropillared ionic gel dielectric layer combined with the screen-printing technique. The scalable and low-cost fabrication of pressure sensors with micropillars templated from a track-etch membrane provides new insights into the future development of health monitoring and human–machine interaction.

Locomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs) and eight force sensors. The system was built to measure both kinematic (tilt angles) and dynamic (ground contact forces) signals of human gaits. To evaluate the recognition performance of the system, seven motion modes and sit-to-stand transition were monitored. With a linear discriminant analysis (LDA) classifier, the proposed system can accurately classify the current states. The overall motion mode recognition accuracy was 99.9% during the stance phase and 98.5% during the swing phase. For sit-to-stand transition recognition, the average accuracy was 99.9%. These promising results show the potential of the designed system for the control of intelligent prostheses.

Stable bipedal walking is one of the most important components of humanoid robot design, which can help us better understand natural human walking. In this paper, to study gait selection and gait transition of efficient bipedal walking, we proposed a dynamic bipedal walking model with an upper body, flat feet and compliant joints. The model can achieve stable cyclic motion with different walking gaits. The hip actuation and ankle stiffness behavior of the model are quite similar to those of human normal walking. In simulation, we studied the influence of hip actuation and ankle stiffness on walking performance of each gait. The effects of ankle stiffness on gait selection are also analyzed. Gait transition is realized by adjusting ankle stiffness during walking.