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Prosthetic knees represent a prevalent solution for above-knee amputation rehabilitation. However, satisfying the ambulation requirements of users while achieving their comfort needs in terms of lightweight, bionic, shock-absorbing, and user-centric, remains out of reach. Soft materials seem to provide alternative solutions as their properties are conducive to the comfort aspect. Unfortunately, the pronounced flexibility restricts the application of soft robots on prosthetic knees regarding morphological computation and weight-bearing performance. Here, we innovate a soft prosthetic knee for transfemoral amputees, addressing current challenges through the integration of origami technology and bioinspired weight-bearing principle, achieving its lightweight, compactness, low cost, and simple fabrication. The soft knee can hold the weight of a human (more than 75 kg), perform biomimetic polycentric flexion, absorb impacts during walking (absorbing 11.5% to 17.3% more impact forces), and actively support amputees to walk across ramps, stairs, and obstacles. The efficacy of the proposed design has been corroborated through bench-top and ambulation experiments. The proposal might lead to a paradigm shift in the lower limb prosthetic design. Prosthetic knees face challenges in meeting user ambulation and comfort needs, with soft materials offering potential solutions but being constrained by their limited load-bearing capacity. Here, the authors innovate a soft prosthetic knee for transfemoral amputees. [ABSTRACT FROM AUTHOR]

The modeling and simulation of coupled neuromusculoskeletal-exoskeletal systems play a crucial role in human biomechanical analysis, as well as in the design and control of exoskeletons. This study incorporates the integration of exoskeleton models into a reflex-based gait model, emphasizing human-exoskeleton interaction. Specifically, we introduce an optimization-based dynamic simulation framework that integrates a neuromusculoskeletal feedback loop, multibody dynamics, human-exoskeleton interaction, and foot-ground contact. The framework advances in human-exoskeleton interaction and muscle reflex model refinement. Without relying on experimental measurements or empirical data, our framework employs a stepwise optimization process to determine muscle reflex parameters, taking into account multidimensional criteria. This allows the framework to generate a full range of kinematic and biomechanical signals, including muscle activations, muscle forces, joint torques, etc., which are typically challenging to measure experimentally. To evaluate the validity of the framework, we compare the simulated results with experimental data obtained from a healthy subject wearing an exoskeleton while walking at different speeds (0.9, 1.0, and 1.1 m/s) and terrains (flat and uphill). The results demonstrate that our framework can capture the qualitative differences in muscle activity associated with different functions, as well as the evolutionary patterns of muscle activity and kinematic signals with respect to varying walking conditions, with the Pearson correlation coefficient R > 0.7. Simulations of the human walking with the exoskeleton in both passive mode and assisting mode at a peak torque of 20 N⋅m are further conducted to investigate the effect of exoskeleton assistance on human biomechanics. The simulation framework we propose has the potential to facilitate gait analysis and performance evaluation of coupled human-exoskeleton systems, as well as enable efficient and cost-effective testing of novel exoskeleton designs and control strategies. [ABSTRACT FROM AUTHOR]

Akin to the impact that digital microelectronics had on electronic devices for information technology, digital microfluidics (DMF) was anticipated to transform fluidic devices for lab-on-a-chip (LoC) applications. However, despite a wealth of research and publications, electrowetting-on-dielectric (EWOD) DMF has not achieved the anticipated wide adoption, and commercialization has been painfully slow. By identifying the technological and resource hurdles in developing DMF chip and control systems as the culprit, we envision democratizing DMF by building a standardized design and manufacturing platform. To achieve this vision, we introduce a proof-of-concept cloud platform that empowers any user to design, obtain, and operate DMF chips (https://edroplets.org). For chip design, we establish a web-based EWOD chip design platform with layout rules and automated wire routing. For chip manufacturing, we build a web-based EWOD chip manufacturing platform and fabricate four types of EWOD chips (i.e., glass, paper, PCB, and TFT) to demonstrate the foundry service workflow. For chip control, we introduce a compact EWOD control system along with web-based operating software. Although industrial fabrication services are beyond the scope of this work, we hope this perspective will inspire academic and commercial stakeholders to join the initiative toward a DMF ecosystem for the masses. [ABSTRACT FROM AUTHOR]

In response to the issue of existing muscle force calculation methods lacking consideration for the muscle synergy and muscle force relationships during motions, a biomechanical calculation method based on the muscle force relationships and muscle synergy during walking is proposed. This method incorporates the muscle synergy and the relative force of muscles into inverse dynamics calculation based on the quadratic optimization. The proposed method is applied for the calculation of forces of major lower-extremity muscles of human walking. Muscle forces are obtained based on a musculoskeletal model, which effectively reflects the human motion mechanisms and muscle synergy during walking. This method provides significance for exploring the laws of human movement, biomechanical principles, and humanoid robot motion control. [ABSTRACT FROM AUTHOR]

Background Tunnel placement is a key step in anterior cruciate ligament (ACL) reconstruction. The purpose of this study was to evaluate the accuracy of bone tunnel drilling in arthroscopic ACL reconstruction assisted by a three-dimensional (3D) image-based robot system. Methods Robot-assisted ACL reconstruction was performed on twelve freshly frozen knee specimens. During the operation, three-dimensional images were used for ACL bone tunnel planning, and the robotic arm was used for navigation and drilling. Twelve patients who underwent traditional arthroscopic ACL reconstruction were included. 3D computed tomography was used to measure the actual position of the ACL bone tunnel and to evaluate the accuracy of the robotic and traditional ACL bone tunnel. Results On the femoral side, the positions of robotic and traditional surgery tunnels were 29.3 ± 1.4% and 32.1 ± 3.9% in the deep-to-shallow direction of the lateral femoral condyle (p = 0.032), and 34.6 ± 1.2% and 21.2 ± 9.4% in the high-to-low direction (p < 0.001), respectively. On the tibial side, the positions of the robotic and traditional surgical tunnels were located at 48.4 ± 0.9% and 45.8 ± 2.8% of the medial-to-lateral diameter of the tibial plateau (p = 0.008), 38.1 ± 0.8% and 34.6 ± 6.0% of the anterior-to-posterior diameter (p = 0.071), respectively. Conclusions In this study, ACL reconstruction was completed with the assistance of a robot arm and 3D images, and the robot was able to drill the bone tunnel more accurately than the traditional arthroscopic ACL reconstruction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigates local stability of a four-link limit cycle walking biped with flat feet and compliant ankle joints. Local stability represents the behavior along the solution trajectory between Poincare sections, which can provide detailed information about the evolution of disturbances. The effects of ankle stiffness and foot structure on local stability are studied. In addition, we apply a control strategy based on local stability analysis to the limit cycle walker. Control is applied only in the phases with poor local stability. Simulation results show that the energy consumption is reduced without sacrificing disturbance rejection ability. This study may be helpful in motion control of limit cycle bipedal walking robots with flat feet and ankle stiffness and understanding of human walking principles. [ABSTRACT FROM AUTHOR]

High mechanical loads generated during walking may accelerate the wear of the knee joint. General knee exoskeletons mainly reduce knee joint load by applying pure torque assistance to reduce the force required by knee extensor muscles. This study aims to further reduce the knee joint load during the early and middle stance phases through a gait intervention strategy that combines torque assistance and vertical force assistance. Comprehensive experiments were conducted to verify the gait intervention strategy in reducing the knee joint load. The results demonstrated that the strategy significantly reduced the maximum and mean knee joint force during the early and middle stance phases. Our studies indicated that the strategy may have the potential to reduce the knee joint load from multiple factors, including muscles, ligaments, and the ground reaction force. [ABSTRACT FROM AUTHOR]

This study presents a wireless wearable portable system designed for the automatic quantitative spatio-temporal analysis of continuous thoracic spine motion across various planes and degrees of freedom (DOF). This includes automatic motion segmentation, computation of the range of motion (ROM) for six distinct thoracic spine movements across three planes, tracking of motion completion cycles, and visualization of both primary and coupled thoracic spine motions. To validate the system, this study employed an Inter-days experimental setting to conduct experiments involving a total of 957 thoracic spine movements, with participation from two representatives of varying age and gender. The reliability of the proposed system was assessed using the Intraclass Correlation Coefficient (ICC) and Standard Error of Measurement (SEM). The experimental results demonstrated strong ICC values for various thoracic spine movements across different planes, ranging from 0.774 to 0.918, with an average of 0.85. The SEM values ranged from 0.64° to 4.03°, with an average of 1.93°. Additionally, we successfully conducted an assessment of thoracic spine mobility in a stroke rehabilitation patient using the system. This illustrates the feasibility of the system for actively analyzing thoracic spine mobility, offering an effective technological means for non-invasive research on thoracic spine activity during continuous movement states. [ABSTRACT FROM AUTHOR]

The synthesis of transfer-free graphene is necessary for expanding its industrial applications. Although the direct synthesis of graphene on the insulating substrate via a metal sacrificial film was reported, the growth mechanism of transfer-free graphene still remains to be studied. Herein, a detailed synthesis model of graphene grown from different carbon sources has been established to help in selecting the growth conditions for high-quality graphene. A detailed discussion on the critical influence of dissolution and the diffusion rate of carbon atoms on the growth process has also been presented. The high decomposition temperature carbon sources promote the formation of high-quality monolayers of graphene. The carbon diffusion rate of the Cu film is significantly higher than that of Ni. This promotes the synthesis of graphene from methane and diamond-like carbon. However, adverse effects are exerted on polymethyl methacrylate. Ion implantation technology and different components of the Ni–Cu alloy were used to understand this growth mechanism. This work could guide the growth conditions of transfer-free, large-scale, and high-quality graphene that can be potentially used for the fabrication of a semiconductor or an insulation substrate in theory. The reported method can generate interest in the field and increase the industrial applications of graphene-based devices that exhibit rough or patterned surfaces. [ABSTRACT FROM AUTHOR]

During Fischer–Tropsch synthesis, O atoms are dissociated on the surface of Fe-based catalysts. However, most of the dissociated O would be removed as H2O or CO2, which results in a low atom economy. Hence, a comprehensive study of the O removal pathway as formic acid has been investigated using the combination of density functional theory (DFT) and kinetic Monte Carlo (kMC) to improve the economics of Fischer–Tropsch synthesis on Fe-based catalysts. The results show that the optimal pathway for the removal of dissociated O as formic acid is the OH pathway, of which the effective barrier energy (0.936 eV) is close to that of the CO activation pathway (0.730 eV), meaning that the removal of dissociated O as formic acid is possible. The main factor in an inability to form formic acid is the competition between the formic acid formation pathway and other oxygenated compound formation pathways (H2O, CO2, methanol-formaldehyde); the details are as follows: 1. If the CO is hydrogenated first, then the subsequent reaction would be impossible due to its high effective Gibbs barrier energy. 2. If CO reacts first with O to become CO2, it is difficult for it to be hydrogenated further to become HCOOH because of the low adsorption energy of CO2. 3. When the CO + OH pathway is considered, OH would react easily with H atoms to form H2O due to the hydrogen coverage effect. Finally, the removal of dissociated O to formic acid is proposed via improving the catalyst to increase the CO2 adsorption energy or CO coverage. [ABSTRACT FROM AUTHOR]

The next generation of all-optical computation platforms prefers the light-guiding-light (LGL) scheme inside a medium that envisions circuitry-free and rapidly reconfigurable systems powered by dynamic interactions between light beams. Currently, suitable LGL materials and corresponding mechanisms are in urgent need. In this work, we proposed ubiquitous air as a restorable LGL signal manipulation medium with transient air-plasma waveguide circuits. Briefly, by focusing femtosecond laser beams in free space, the created atmospheric plasma filament array via photoionization was able to guide terahertz (THz) pulses along its epsilon-near-zero zone with a 1/f-profile spectral response. Consequently, this achieved a time-domain integration of the THz pulse in broad bandwidth. When the pumping laser was sequentially turned off and on, this air-plasma multi-filament structure was erased and rebuilt within nano- and femto-seconds, respectively, allowing rapid and repeated rearrangements of the all-optical stage. Furthermore, this air-based LGL information processing approach is promising to pave the way toward all-optical calculations during free-space directional transmission of THz waves, in which way the delivered THz signal can be remotely controlled. [ABSTRACT FROM AUTHOR]

This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features. Evidence of its effectiveness, both in terms of accuracy and operational time consumption, is substantiated through tests conducted using open-source dataset and closed-loop evaluations. Comprehensive experiments were undertaken to validate its prediction accuracy across various test conditions such as subjects, scenarios, sensor mounting positions, camera views, step sizes, walking directions, and disturbances from moving obstacles. A comprehensive prediction accuracy rate of 99.00% confirms the efficacy, generality, and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This study reports the preliminary results of a technique for redistributing muscles at the wrist in the stump of hand amputees by suturing the tendons to the dermis. The technique has the potential to improve control of hand prostheses by detecting movement intentions. [ABSTRACT FROM AUTHOR]

Human wrist motion decoding with a biological-signal-based interface is a key technique in the upper-limb exoskeleton and prosthesis control. One critical issue in this field is achieving high recognition precision and fast time response while against external disturbances of sensor re-wearing. In this study, we proposed a high-framerate Electrical Impedance Tomography (EIT) system combined with an adaptive recognition algorithm for real-time wrist kinematics decoding. The high-framerate EIT system was developed by a parallel stimulation-measurement sequence, and the sampling rate was as high as 104 Hz. Compared to the most widely used myoelectric techniques, the EIT-based interface can provide extra deep muscular spatial information with similar surface electrodes. It greatly benefited the subsequent recognition algorithms, in which the key EIT regions indicating muscle morphology kept consistent after an arbitrary sensor re-donning. The designed adaptive algorithm achieved equally high performance with an automatic update of the classifier mean values with a fast self-operated calibration process. We validated the approach on 12 subjects with a 2-dimensional Fitts’ law test. The wrist gestures and joint angles were mapped to the direction and speed of the cursor movement, respectively. The average throughputs (TPs) of Fitts’ law tests were 1.0269 ± 0.0971 bits/s and 1.0095 ± 0.0931 bits/s without and with sensor re-donning, respectively, which were comparable to the TPs of sEMG-based studies. The results showed the promise of the EIT-based interface on real-time human motion intent recognition. Future endeavors are worth being paid in this direction for more complicated robotic tasks. [ABSTRACT FROM AUTHOR]

Monitoring sleep conditions is of importance for sleep quality evaluation and sleep disease diagnosis. Accurate respiration detection provides key information about sleep conditions. Here, we propose a perforated temperature sensor that can be worn below the nasal cavity to monitor breath. The sensing system consists of two perforated temperature sensors, signal conditioning circuits, a transmission module, and a supporting analysis algorithm. The perforated structure effectively enhances the sensitivity of the system and shortens the response time. The sensor's response time is 0.07 s in air and sensitivity is 1.4‰°C−1. The device can achieve a monitoring respiratory temperature range between normal room temperature and 40 °C. The simple and standard micromachining process ensures low cost and high reproducibility. We achieved the monitoring of different breathing patterns, such as normal breathing, panting, and apnea, which can be applied to sleep breath monitoring and exercise information recording. [ABSTRACT FROM AUTHOR]

Geometric or electronic confinement of guests within nanoporous hosts holds promise for imparting catalytic functionality, including single-metal-atom catalytic functionality, to existing materials. When the nanoporous host is a metal–organic framework (MOF), single-metal-ion catalysts have typically been installed by grafting to an open site on an inorganic node, with the node effectively becoming the support for the catalyst. This approach, however, imposes compositional constraints, as the node not only needs to be receptive to grafting, but also must be capable of stabilizing the framework against solvent evacuation, chemical exposure, and heating. Here, we show that disk-like, Anderson polyoxometalate clusters (RhMo6O24n− and Mo7O24m−; POMs) can be confined in pore-specific and orientation-specific fashion within the hierarchically porous, Zr(IV)-based MOF, NU1K. Self-limiting loading of one cluster per pore, and associated nano-confinement, serve to isolate each POM and prevent consolidation caused by sintering. Additionally, the oriented confinement serves to expose individual rhodium atoms to candidate gas-phase reactants, while enabling the rhodium atom to employ a well-defined oxy-molybdenum cluster, rather than a MOF node, as a support. Synchrotron-based difference-electron-density maps and differential pair-distribution-function analyses of scattered X-rays establish cluster siting and orientation and confirm isolation. Nanoconfined (i.e., MOF- and POM-confined) single-rhodium(III)-atoms are catalytically competent for an illustrative gas-phase reaction, CO oxidation by O2, with the MOF-isolated POM enormously outperforming nonporous, MOF-free, solid (NH4)3[H6RhMo6O24]·6H2O. This work highlights the value of MOF-based nano-confinement and oriented isolation of planar POMs as a means of uniformly presenting and stabilizing potent single-metal-atom catalysts, in reactant-accessible form, on well-defined supports. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Human-machine interfaces for hand gesture recognition across multiple sessions and days of doffing and re-donning while maintaining acceptable recognition accuracy are still challenging. In this paper, a flexible wristband, which was integrated with a highly sensitive capacitive pressure sensing array, was used for inter-day hand gesture recognition. The performance of the entire system was further improved by utilizing a triplet network for deep feature embedding. Seven hand gestures were included into the gesture set, and inter-day experiments which lasted for five consecutive days with three sessions on each day were conducted. Five healthy subjects participated in the experiment. Between each session, the wristband was doffed, and re-donned before the next session. The triplet network achieved an average recognition accuracy of 91.98% across all the sessions of all the subjects, and yielded a higher classification result (p < 0.05) over the convolutional neural network trained with softmax-cross-entropy loss (with an average accuracy of 84.65%). Furthermore, we also found that the capacitive array size had an evident influence on the inter-day classification result. The array with the full size (thirty-two channels) achieved a higher average recognition accuracy over all the down-sampled arrays. This work demonstrated the feasibility of improving the hand gesture recognition performance over days of usage by fabricating a wearable, flexible multi-channel capacitive wristband and implementing the triplet network. [ABSTRACT FROM AUTHOR]

Trajectory planning of the knee joint plays an essential role in controlling the lower limb prosthesis. Nowadays, the idea of mapping the trajectory of the healthy limb to the motion trajectory of the prosthetic joint has begun to emerge. However, establishing a simple and intuitive coordination mapping is still challenging. This paper employs the method of experimental data mining to explore such a coordination mapping. The coordination indexes, i.e., the mean absolute relative phase (MARP) and the deviation phase (DP), are obtained from experimental data. Statistical results covering different subjects indicate that the hip motion possesses a stable phase difference with the knee, inspiring us to construct a hip-knee Motion-Lagged Coordination Mapping (MLCM). The MLCM first introduces a time lag to the hip motion to avoid conventional integral or differential calculations. The model in polynomials, which is proved more efficient than Gaussian process regression and neural network learning, is then constructed to represent the mapping from the lagged hip motion to the knee motion. In addition, a strong linear correlation between hip-knee MARP and hip-knee motion lag is discovered for the first time. By using the MLCM, one can generate the knee trajectory for the prosthesis control only via the hip motion of the healthy limb, indicating less sensing and better robustness. Numerical simulations show that the prosthesis can achieve normal gaits at different walking speeds. [ABSTRACT FROM AUTHOR]

Recognition of continuous foot motions is important in robot-assisted lower limb rehabilitation, especially in prosthesis and exoskeleton design. For instance, perceiving foot motion is essential feedback for the robot controller. However, few studies have focused on perceiving multiple-degree of freedom (DOF) foot movements. This paper proposes a novel human-machine interaction (HMI) recognition wearable system for continuous multiple-DOF ankle-foot movements. The proposed system uses solely kinematic signals from inertial measurement units and multiclass support vector machines by creating error-correcting output codes. We conducted a study with multiple participants to validate the performance of the system using two strategies, a general model and a subject-specific model. The experimental results demonstrated satisfactory performance. The subject-specific approach achieved 98.45% ± 1.17% (mean ± SD) overall accuracy within a prediction time of 10.9 ms ± 1.7 ms, and the general approach achieved 85.3% ± 7.89% overall accuracy within a prediction time of 14.1 ms ± 4.5 ms. The results prove that the proposed system can more effectively recognize multiple continuous DOF foot movements than existing strategies. It can be applied to ankle-foot rehabilitation and fills the HMI high-level control demand for multiple-DOF wearable lower-limb robotics. [ABSTRACT FROM AUTHOR]

Oxy-fuel combustion technology is an effective way to reduce CO2 emissions. An ionic liquid [emim][Tf2N] was used to capture the CO2 in flue gas from oxy-fuel combustion plant. The process of the CO2 capture was simulated using Aspen Plus. The results show that when the liquid–gas ratio is 1.55, the volume fraction of CO2 in the exhaust gas is controlled to about 2%. When the desorption pressure is 0.01 MPa, desorption efficiency is 98.2%. Additionally, based on the designability of ionic liquids, a hypothesis on the physical properties of ionic liquids is proposed to evaluate their influence on the absorption process and heat exchanger design. The process evaluation results show that an ionic liquid having a large density, a large thermal conductivity, and a high heat capacity at constant pressure is advantageous. This paper shows that from capture energy consumption and lean circulation, oxy-fuel combustion is a more economical method. Furthermore, it provides a feasible path for the treatment of CO2 in the waste gas of oxy-fuel combustion. Meanwhile, Aspen simulation helps speed up the application of ionic liquids and oxy-fuel combustion. Process evaluation helps in equipment design and selection. [ABSTRACT FROM AUTHOR]

A defining characteristic of nearly all catalytically functional MOFs is uniform, molecular-scale porosity. MOF pores, linkers and nodes that define them, help regulate reactant and product transport, catalyst siting, catalyst accessibility, catalyst stability, catalyst activity, co-catalyst proximity, composition of the chemical environment at and beyond the catalytic active site, chemical intermediate and transition-state conformations, thermodynamic affinity of molecular guests for MOF interior sites, framework charge and density of charge-compensating ions, pore hydrophobicity/hydrophilicity, pore and channel rigidity vs. flexibility, and other features and properties. Collectively and individually, these properties help define overall catalyst functional behaviour. This review focuses on how porous, catalyst-containing MOFs capitalize on molecular-scale confinement, containment, isolation, environment modulation, energy delivery, and mobility to accomplish desired chemical transformations with potentially superior selectivity or other efficacy, especially in comparison to catalysts in homogeneous solution environments. [ABSTRACT FROM AUTHOR]

Background: For people with lower-limb amputations, wearing a prosthetic limb helps restore their motor abilities for daily activities. However, the prosthesis's potential benefits are hindered by limited somatosensory feedback from the affected limb and its prosthesis. Previous studies have examined various sensory substitution systems to alleviate this problem; the prominent approach is to convert foot-ground interaction to tactile stimulations. However, positive outcomes for improving their postural stability are still rare. We hypothesized that the sensory substiution system based on surrogated tactile stimulus is capable of improving the standing stability among people with lower-limb amputations.Methods: We designed a wearable device consisting of four pressure sensors and two vibrators and tested it among people with unilateral transtibial amputations (n = 7) and non-disabled participants (n = 8). The real-time measurements of foot pressure were fused into a single representation of foot-ground interaction force, which was encoded by varying vibration intensity of the two vibrators attached to the participants' forearm. The vibration intensity followed a logarithmic function of the force representation, in keeping with principles of tactile psychophysics. The participants were tested with a classical postural stability task in which visual disturbances perturbed their quiet standing.Results: With a brief familiarization of the system, the participants exhibited better postural stability against visual disturbances when switching on sensory substitution than without. The body sway was substantially reduced, as shown in head movements and excursions of the center of pressure. The improvement was present for both groups of participants and was particularly pronounced in more challenging conditions with larger visual disturbances.Conclusions: Substituting otherwise missing foot pressure feedback with vibrotactile signals can improve postural stability for people with lower-limb amputations. The design of the mapping between the foot-ground interaction force and the tactile signals is essential for the user to utilize the surrogated tactile signals for postural control, especially for situations that their postural control is challenged. [ABSTRACT FROM AUTHOR]

This article presents a robotic transfemoral prosthesis that consists of an electrohydraulic knee and a motor-driven ankle. The electrohydraulic knee includes a controllable hydraulic damper to provide stance control and an electrical actuator to provide swing assistance. The motor-driven ankle provides damping adaptation for different terrains. The embedded finite-state-based controller enables the knee–ankle prosthesis to facilitate the amputee's ambulation on five different terrains. The proposed knee is lightweight and compact, with low electrical energy consumption. Experimental results demonstrate the potential advantages of the proposed transfemoral prosthesis compared with the hydraulic prosthesis daily used by the subject. [ABSTRACT FROM AUTHOR]

Adaptation to the operator's intents and the external physical uncertainties is critical to the performances of the human–robot collaboration. In this study, we proposed a control interface that combined the electrical impedance tomography (EIT) based sensing approach with the robotic controllers to produce proper assistance with external uncertainties in the collaborative task. The interface first estimates the continuous forearm muscle contractions (represented by the grasp forces) by the optimized EIT features with an easily worn fabric band. The recognition decisions then serve as the control inputs to adjust the state transitions and the desired interaction forces in real time. We evaluated the interface in the tasks of grasp force estimation and human–robot sawing by recruiting more than 20 subjects in total. For grasp force estimation, the interface produced an average of $R^{2}\geq 0.9$ in both offline and online validations with the feature optimization procedure and a sigmoid regression function. The interface was robust to external disturbances without retraining or manual calibrations. The average $R^{2}=0.86$ with the untrained dynamic postures, and the average $R^{2}$ values ranged from 0.85 to 0.88 in the tests with redonning the front-end in interday and intraday uses. For human–robot sawing, the interface accomplished the tasks with a high success rate in controlling the states ($>$ 96%) and intuitive adjusting of the sawing forces, being combined with the designed hybrid admittance/position controller. It was also adaptive to the sawing frequency changes and the sawing directions according to the operator's intents. The interface's performances are comparable, if not better, to the state of the art on both biological signal based grasp force estimation and human–robot sawing. Future efforts are worth being paid in this new direction to get more promising outcomes. [ABSTRACT FROM AUTHOR]

This study proposed a multiple degree-of-freedom (DoF) continuous wrist angle estimation approach based on an electrical impedance tomography (EIT) interface. The interface can inspect the spatial information of deep muscles with a soft elastic fabric sensing band, extending the measurement scope of the existing muscle-signal-based sensors. The designed estimation algorithm first extracted the mutual correlation of the EIT regions with a kernel function, and second used a regularization procedure to select the optimal coefficients. We evaluated the method with different features and regression models on 12 healthy subjects when they performed six basic wrist joint motions. The average root-mean-square error of the 3-DoF estimation task was 7.62°, and the average R 2 was 0.92. The results are comparable to state-of-the-art with sEMG signals in multi-DoF tasks. Future endeavors will be paid in this new direction to get more promising results. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET. To improve the sampling efficiency, the sampling interval was calculated based on linear probability. The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training. With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment. AUC, MAP, and Error Rate were employed as evaluation indexes. The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy. [ABSTRACT FROM AUTHOR]

Microneedles are able to penetrate into the skin and offer great chances in various biomedical applications including transdermal drug delivery, stable bio-signal monitoring, sampling and bio-sensing. Owing to the micro-scale feature size, microneedles have advantages of minimal invasiveness, pain, high biosafety and simple operation. However, it is still challenging to fabricate solid microneedles on a flexible substrate. This review primarily introduces recent studies about microneedles on the flexible substrate, various fabrication methods of microneedles on the flexible substrate, including micromolding, silicon dry/wet etching, electrochemical etching, drawing-lithography, electro-drawing lithography, magnetorheological drawing lithography, three-dimensional printing, vapor–liquid–solid grown, hot embossing, and assembly, and corresponding materials have been summarized and discussed in details. In the end, regular biomedical applications of microneedle on the flexible substrate are categorized into drug delivery, vaccine delivery, electrophysiological interaction, sampling and bio-sensing. Recent advances of each application have been discussed. [ABSTRACT FROM AUTHOR]

Maximum dorsiflexion (MDF) is an important gait event corresponding to the maximum ankle dorsiflexion angle in each gait cycle. MDF timing plays an important role in the control of robotic prosthesis. This article puts forward an on-board adaptive algorithm to detect MDF timing of robotic transtibial prosthesis in different walking conditions (at different speeds and on different ramps) and for different users. Based on the adaptive algorithm, we can get a time-variant detection model. The framework of the adaptive algorithm is composed of: 1) training data collecting and labeling; 2) model training and real-time detection; and 3) model updating according to the detection results. Based on the adaptive algorithm, we conducted speed and ramp experiments to detect MDF timings at slow, normal, and fast speeds, and on ramps with different inclination angles (10°, 5°, 0°, −5°, and −10°). Three transtibial amputee participated in the experiments. The model training/updating time ranges from 3.6 to 4.1 s and the detection time ranges from 0.95 to 1.17 ms for different speeds and ramps. In real-time detection, there is false detection (1.67%) at normal walking speed. In addition, all MDF timings are detected correctly (accuracy: 100%) based on the adaptive algorithm. The mean detection delays are 7.23, 18.27, and 7.5 ms corresponding to slow, normal and fast speeds and 10.60, 10.30, 18.27, 10.27, and 15.63 ms for ramps of different inclination angles (10°, 5°, 0°, −5°, and −10°). Compared with the proposed adaptive algorithm, both the nonadaptive and adaptive threshold decision methods cause more false detections. The results show that the proposed approach for MDF timing detection has adaptations to different walking conditions (speeds and ramps) and prosthesis users, which indicates that the adaptive algorithm is effective and shows the potential in robotic prosthesis control in the future. Note to Practitioners—This article proposes an on-board adaptive algorithm to detect the maximum dorsiflexion (MDF) timing based on inertial measurement unit (IMU) and ankle angle sensor for robotic transtibial prosthesis users in each gait cycle. IMU and angle sensor are integrated in the prosthesis, and the adaptive algorithm is embedded in the control circuit of prosthesis. The adaptive algorithm can realize the model updating continuously for real-time MDF timing detection with collected and labeled training data. The proposed adaptive algorithm shows satisfactory adaptation for MDF timing detection in different walking speed and ramp conditions. In addition, the adaptive algorithm also shows some generalizations for prosthesis users, which are useful to improve prosthesis control. [ABSTRACT FROM AUTHOR]

The paper puts forward an on-board strategy for a training model and develops a real-time human locomotion mode recognition study based on a trained model utilizing two inertial measurement units (IMUs) of robotic transtibial prosthesis. Three transtibial amputees were recruited as subjects in this study to finish five locomotion modes (level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending) with robotic prostheses. An interaction interface was designed to collect sensors' data and instruct to train model and recognition. In this study, the analysis of variance ratio (no more than 0.05) reflects the good repeatability of gait. The on-board training time for SVM (Support Vector Machines), QDA (Quadratic Discriminant Analysis), and LDA (Linear discriminant analysis) are 89, 25, and 10 s based on a 10,000 × 80 training data set, respectively. It costs about 13.4, 5.36, and 0.067 ms for SVM, QDA, and LDA for each recognition process. Taking the recognition accuracy of some previous studies and time consumption into consideration, we choose QDA for real-time recognition study. The real-time recognition accuracies are 97.19 ± 0.36% based on QDA, and we can achieve more than 95% recognition accuracy for each locomotion mode. The receiver operating characteristic also shows the good quality of QDA classifiers. This study provides a preliminary interaction design for human–machine prosthetics in future clinical application. This study just adopts two IMUs not multi-type sensors fusion to improve the integration and wearing convenience, and it maintains comparable recognition accuracy with multi-type sensors fusion at the same time. [ABSTRACT FROM AUTHOR]

Wearable robotic devices often need electrical energy. An interesting idea is to collect mechanical energy during walking and convert it into electrical energy to recharge these devices directly. In this article, we built a light-weight robotic prosthesis (1.3 kg) with the feature of self-charging. During stance phase, the prosthetic ankle joint with damping, is driven by human dynamics. The rotated ankle joint backdrives the motor, and the motor works as a generator according to the electromagnetic induction theory. Five subjects participated in experiments to verify the feasibility and five speeds walking were studied (0.7, 0.9, 1.1, and 1.3 m/s treadmill speeds and one self-selected outdoor walking speed). Experimental results demonstrate that the electrical regenerative energy per step is 1.53 $\pm \;\text{0.29}\,\text{J}$ on average. Meanwhile, an average consumed energy per step of the robotic prosthesis is 4.64 $\pm \;\text{0.15}\;\text{J}$ , which means 33 $\pm \;5\%$ energy can be returned to the active prosthesis (battery, 24 $\,\text{V}$ , 2.6 $\,\text{Ah}$). [ABSTRACT FROM AUTHOR]

Real-time human intent recognition is important for controlling low-limb wearable robots. In this paper, to achieve continuous and precise recognition results on different terrains, we propose a real-time training and recognition method for six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending. A locomotion recognition system is designed for the real-time recognition purpose with an embedded BPNN-based algorithm. A wearable powered orthosis integrated with this system and two inertial measurement units is used as the experimental setup to evaluate the performance of the designed method while providing hip assistance. Experiments including on-board training and real-time recognition parts are carried out on three able-bodied subjects. The overall recognition accuracies of six locomotion modes based on subject-dependent models are 98.43% and 98.03% respectively, with the wearable orthosis in two different assistance strategies. The cost time of recognition decision delivered to the orthosis is about 0.9 ms. Experimental results show an effective and promising performance of the proposed method to realize real-time training and recognition for future control of low-limb wearable robots assisting users on different terrains. [ABSTRACT FROM AUTHOR]

In order to achieve high-level control for an active postural support brace, it is important for a wearable robot to be capable of recognizing human motion intentions. An inertial sensors-based torso motion mode recognition method is proposed in this study. The experiments are conducted to define range of torso motion, capture human motion signals by using four inertial sensors on seven healthy subjects, and utilize a classification method to achieve torso motion recognition based on human intent. Up to sixteen modes for torso motion recognition are investigated, and cascaded classification methods combining a quadratic discriminant analysis (QDA) classifier and a support vector machine (SVM) classifier are deployed. With selected cascaded classification strategies, cross-validation yielded classification accuracies of 95.18% (QDA) and 96.24% (SVM). The obtained results of the study show that inertial sensors based motion recognition is viable to achieve in high recognition accuracy which is promising for future robotic applications. [ABSTRACT FROM AUTHOR]

Summary: People with severe neuromuscular trunk impairment cannot maintain or control upright posture of the upper body in sitting while reaching. Passive orthoses are clinically available to provide support and promote the use of upper extremities in this population. However, these orthoses only position the torso passively without any degree of trunk movement. We introduce for the first time a novel active-assistive torso brace called Wheelchair Robot for Active Postural Support (WRAPS). It consists of two rings over the hips and chest connected by a 2RPS-2UPS parallel robotic device. WRAPS can modulate the displacement of the upper ring and/or the forces applied on the torso through the ring in four degrees-of-freedom (DOF), including rotations and translation in the sagittal and frontal planes. In the present study, we evaluate the design of WRAPS and its functions. Moreover, we discuss the potential effectiveness of WRAPS as a therapeutic robotic tool in people with severe trunk control deficits. The performance of WRAPS was evaluated in seated healthy subjects. Kinematics and surface electromyography (sEMG) were collected when the participants performed selective trunk movements. First, the torso range of motion (tROM) was calculated with WRAPS in transparent mode—zero-force control mode—which was compared with free-guided tROM (no WRAPS) with motion capture system. Second, a position control mode was configured to mobilize the torso along the trajectories obtained with the transparent mode. Our results show that the design of WRAPS suited well the subject's anthropometrics while supporting the weight of the torso. Importantly, WRAPS can be programmed to replicate the subject's tROM, without the full activation of torso muscles. This can be critical in individuals with no trunk control. Altogether, these preliminary results indicate the potential applicability of WRAPS to promote active-assistive trunk mobility in people who cannot sit independently because of trunk dysfunction. [ABSTRACT FROM AUTHOR]

Summary: Using wearable robots is an effective means of rehabilitation for stroke survivors, and effective recognition of human motion intentions is a key premise in controlling wearable robots. In this paper, we propose an inertial measurement unit (IMU)-based gait phase detection system. The system consists of two IMUs that are tied on the thigh and on the shank, respectively, for collecting acceleration and angular velocity. Features were extracted using a sliding window of 150 ms in length, which was then fed into a quadratic discriminant analysis (QDA) classifier for classification. We recruited five stroke survivors to test our system. They walked at their own preferred speed on the level ground. Experimental results show that our proposed system has the ability of recognizing the gait phase of stroke survivors. All recognition accuracy results are above 96.5%, and detections are about 5–15 ms in advance of time. In addition, using only one IMU can also give reliable recognition results. This paper proposes an idea about the further research on human–computer interaction for the control of wearable robots. [ABSTRACT FROM AUTHOR]

Summary: For a real-time robotic prosthetic control, gait event detection plays an important role. In this paper, one novel sensor was proposed to realize gait event detection. The sensor includes one strain gauge bridge, which can reflect the entire deformation of carbon-fiber footplate on a robotic prosthesis. Three unilateral transtibial amputees participated in the experiments. Experimental results show that using the proposed sensor method, gait event detection (stance phase and swing phase) accuracy is approximately 100%. Based on the detected gait events, three locomotion modes (sit, stand, and walk) and the corresponding transition modes could be determined. Difference between different gait event detection systems was further conducted. [ABSTRACT FROM AUTHOR]

For partial lower-limb exoskeletons, an accurate real-time estimation of the gait phase is paramount to provide timely and well-tailored assistance during gait. To this end, dedicated wearable sensors separated from the exoskeletons mechanical structure may be preferable because they are typically isolated from movement artifacts that often result from the transient dynamics of the physical human–robot interaction. Moreover, wearable sensors that do not require time-consuming calibration procedures are more easily acceptable by users. In this paper, a robotic hip orthosis was controlled using capacitive sensors placed in orthopedic cuffs on the shanks. The capacitive signals are zeroed after donning the cuffs and do not require any further calibration. The capacitive-sensing-based controller was designed to perform online estimation of the gait cycle phase via adaptive oscillators, and to provide a phase-locked assistive torque. Two experimental activities were carried out to validate the effectiveness of the proposed control strategy. Experiments conducted with seven healthy subjects walking on a treadmill at different speeds demonstrated that the controller can estimate the gait phase with an average error of 4%, while also providing hip flexion assistance. Moreover, experiments carried out with four healthy subjects showed that the capacitive-sensing-based controller could reduce the metabolic expenditure of subjects compared to the unassisted condition (mean $\pm$  SEM, $-$ 3.2% $\pm$  1.1). [ABSTRACT FROM AUTHOR]

Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis. In this study, we proposed an adaptive recognition strategy to against the time-varying features of on-prosthesis mechanical signals in inter-session and inter-day recognition tasks. The strategy was designed with an automatic training algorithm which could update the classifiers with the data of the most recent completed gait cycles to seize the changes of the features brought by external disturbances. After implementation, we measured multiple experimental sessions on six transtibial amputees with intervals from a few hours (inter-session experiment) to several months (108 days at most in inter-day experiment). In each session, they performed five locomotion modes and eight locomotion transitions using the robotic prosthesis. Between each two experimental sessions, the subjects were required to doff the robotic prosthesis (including the socket). The proposed adaptive recognition algorithm significantly improved recognition accuracies in both experiments. In inter-session experiment, the proposed method increased the recognition accuracy from 89.3% to 92.8% than previous non-adaptive recognition method. In inter-day experiments, it increased the recognition accuracy from 60% to 88.8%. If taking three modes (level walking, stair ascending/descending) and four locomotion transitions into calculation, the recognizer produced an accuracy up to 96.6% (swing phase) for static mode and an accuracy of 96.9% for locomotion transitions on inter-day tasks without manual intervenes. Compared with state-of-the-art, our study extends the ability of the robotic transtibial prosthesis in locomotion mode recognition across multiple sessions and days. Future efforts are worth being paid in this direction to get more promising results. [ABSTRACT FROM AUTHOR]

Superparamagnetic nanoparticles have broad applications in biology and medicines. Quantitative measurements of magnetic beads in solution are essential in gaining comprehensive understanding of their dynamics and developing applications. Here, using synchrotron X‐ray sources combined with well controlled magnetic fields, the results from small‐angle X‐ray scattering (SAXS) experiments on superparamagnetic particles in solution under the influence of external magnetic fields are reported. The particles mostly remain in monodispersed states and the linear aggregates tend to be aligned with the external magnetic field. After removing the magnetic fields, the superparamagnetic nanoparticles quickly recover to their original states indicating high reversibility of the rearrangement under the control of a magnetic field. The external magnetic field instrument composed of paired permanent magnets is integrated into the SAXS beamline at the Shanghai Synchrotron Radiation Facility providing a platform for studying time‐resolved dynamics induced by magnetic fields. [ABSTRACT FROM AUTHOR]

Locomotion mode recognition can contribute to precise control of active lower-limb prostheses in different environments. In this paper, we propose a novel locomotion mode recognition method based on convolutional neural network and strain gauge signals. The strain gauge only provides one-dimensional signals and is also used in the control strategy of the robotic prosthesis. The convolutional neural network takes the raw noisy signals as inputs. Three transtibial amputee subjects were recruited in the experiments, and three locomotion modes were recognized. The overall three-class locomotion mode recognition accuracy is in the hold-out test and in the 5-fold cross-validation. The results show that the strain gauge contains information of locomotion modes, and the convolutional neural network has the capacity of extracting features from raw signals. [ABSTRACT FROM AUTHOR]

This study presents a noncontact capacitive sensing method for forearm motion recognition. A method is proposed to record upper limb motion information from muscle contractions without contact with human skin, compensating for the limitations of existing sEMG-based methods. The sensing front-ends are designed based on human forearm shapes, and the forearm limb shape changes caused by muscle contractions will be represented by capacitance signals. After implementation of the capacitive sensing system, experiments on healthy subjects are conducted to evaluate the effectiveness. Nine motion patterns combined with 16 motion transitions are investigated on seven participants. We also designed an automatic data labeling method based on inertial signals from the measured hand, which greatly accelerated the training procedure. With the capacitive sensing system and the designed recognition algorithm, the method produced an average recognition of over 92%. Correct decisions could be made with approximately a 347-ms delay from the relaxed state to the time point of motion initiation. The confounding factors that affect the performances are also analyzed, including the sliding window length, the motion types and the external disturbances. We found the average accuracy increased to 98.7% when five motion patterns were recognized. The results of the study proved the feasibility and revealed the problems of the noncontact capacitive sensing approach on upper-limb motion sensing and recognition. Future efforts in this direction could be worthwhile for achieving more promising outcomes. [ABSTRACT FROM AUTHOR]

Human intent recognition is important to the control of robotic prosthesis. In this paper, we propose a multi-level real-time on-board system to recognize continuous locomotion modes. A cascaded classification strategy is designed for the recognition of six steady locomotion modes and 10 transitions. On-board signals of the robotic prosthesis include two inertial measurement units and one load cell. Three transtibial amputees are recruited in the experiments. The prediction decision time of the real-time on-board cascaded classification system is about 3.3 ms, which is enough short compared with the sliding window increment 10 ms. It is easy to recognize the standing and ambulation in the first-level classification with a 99.86% accuracy by quadratic discriminant analysis (QDA) classifier. In the second-level classification, threshold method is adopted to divide one stride into swing and stance phases. In swing phase, five steady modes are recognized with a total accuracy of 96.40%. In stance phase, all these five steady modes are recognized with a total accuracy of 91.21%. The average recognition accuracy of the three subjects is 93.21% by QDA classifier. Besides, for transitions, the proposed system could recognize all transitions rightly. The designed system is feasible and effective to realize real-time on-board recognition of continuous locomotion modes, which is promising for the further control of the prosthesis. [ABSTRACT FROM AUTHOR]

Transtibial prostheses play an important role in below-knee amputees’ daily activities. Our previous study presented a lightweight prosthesis with damping behaviors, which can adapt to different terrains. However, the proposed prosthesis cannot provide push-off during the stance phase. In this paper, we redesigned the mechanical structure, combining push-off and damping behaviors. The Peking University Robotic Trantibial Prosthesis (PKU-RoboTPro-) II makes a trade-off between push-off power and lightweight. Three unilateral transtibial subjects participated in the study. Gait symmetry and metabolic cost of walking with the proposed prosthesis were evaluated under conditions of three terrains and four walking speeds. The gait symmetry of the subjects using the prosthesis combining push-off and damping behaviors in stance time and ground reaction force distribution are improved. Meanwhile, the PKU-RoboTPro-II with a push-off mode can reduce energy expenditure, with an average 14% $\pm$ 8% reduction and a maximal 31% reduction under different conditions, compared with a non-push-off mode. When the weight of the lithium-ion rechargeable battery is 0.28 kg (2.6 Ah), it enables the new prosthesis combining push-off and damping behaviors to walk approximately 10 000 strides, on average. [ABSTRACT FROM PUBLISHER]

Real-time recognition of locomotion-related activities is a fundamental skill that a controller of lower limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy-logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10 000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities. [ABSTRACT FROM PUBLISHER]

This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact capacitive sensing and adaptive oscillators (AOs). The capacitive sensing system is designed with two sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-mean-square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on capacitive sensing and AOs is a promising alternative for the control of exoskeleton/orthosis. [ABSTRACT FROM AUTHOR]

This paper focuses on chaos control of a seven-link torque-stiffness-controlled dynamic walking model, actuated by a bio-inspired control system. The biped consists of compliant hip, knee and ankle joints and flat feet. We employed Ott–Grebogi–Yorke and delayed feedback control methods, responsible for small errors around the equilibrium solution and large errors far away, respectively. In simulation, we study the stabilization of bifurcations and chaotic behaviors under diverse actuation parameters, and the convergence speed to 1-period gaits. The results of this paper may provide insights into motion control of dynamic walking robots and principles of human locomotion. [ABSTRACT FROM AUTHOR]