High motion compatibility with the human body is essential for lower limb exoskeletons. However, in most exoskeletons, internal/external rotational degrees of freedom are not provided, which makes accurate alignment between the biological and mechanical joints difficult to achieve. To solve this problem, a novel hip exoskeleton with a parallel structure is developed in this article. The unique parallel structure eliminates the misalignment problem and enables walking free of restrictions. On the other hand, this requires a coordinated control among actuations within the parallel exoskeleton structure. In this light, a model-based controller is proposed in this article. The controller is based on a human–machine integrated dynamic model and can generate coordinated force control references that could increase the closed-loop system's sensitivity to its wearer's movements. The controller requires only kinematic information from the wearer, but not interaction force data that most existing exoskeletons require in their control design, which saves spaces and makes the system compact for use. Experiments were conducted to demonstrate the kinematic compatibility and assistive performance of the proposed hip exoskeleton.

Propulsion during push-off (PO) is a key factor to realize human locomotion. Through the detection of real-time gait stage, assistance could be provided to the human body at the proper time. In most cases, ankle-foot exoskeletons consist of electronic sensors, microprocessors, and actuators. Although these three essential elements contribute to fulfilling the function of the detection, control, and energy injection, they result in a huge system that reduces the wearing comfort. To simplify the sensor-controller system and reduce the mass of the exoskeleton, we designed a smart clutch in this paper, which is a sensor-controller integrated system that comprises a sensing part and an executing part. With a spring functioning as an actuator, the whole exoskeleton system is completely made up of mechanical parts and has no external power source. By controlling the engagement of the actuator based on the signal acquired from the sensing part, the proposed clutch enables the ankle-foot exoskeleton (AFE) to provide additional ankle torque during PO, and allows free rotation of the ankle joint during swing phase, thus reducing the metabolic cost of the human body. There are two striking advantages of the designed clutch. On the one hand, the clutch is lightweight and reliable—it resists the possible shock during walking since there is no circuit connection or power in the system. On the other hand, the detection of gait relies on the contact states between human feet and the ground, so the clutch is universal and does not need to be customized for individuals.

Accurate estimation of lower limb joint kinematics and kinetics using wearable sensors enables biomechanical analysis beyond laboratory settings and facilitates real-time adaptation of exoskeleton assistance profiles. This study introduces a Dual Transformer Network (DTN) designed to concurrently estimate multiple joint angles and moments from multi-channel surface electromyography (sEMG) signals in the lower limbs. The performance evaluation of the predicted joint angles for the hip, knee, and ankle showed average root mean square error (RMSE) values of 1.1827$^{\circ }$, 1.4312$^{\circ }$, and 0.8113$^{\circ }$, Pearson correlation coefficients ($\boldsymbol{\rho }$) of 0.9992, 0.9993, and 0.9991, and coefficients of determination ($\mathbf {{\mathit{R}}}^{2}$) of 0.9847, 0.9858, and 0.9838, respectively. For the predicted joint moments, the corresponding values were RMSE of 0.0458, 0.0341, and 0.0522 Nm/kg, $\boldsymbol{\rho }$ of 0.9978, 0.9972, and 0.9990, and $\mathbf {{\mathit{R}}}^{2}$ of 0.9825, 0.9801, and 0.9902. Angular velocities, derived by differentiating the estimated joint angles, achieved an RMSE below 0.6530 rd/s, $\boldsymbol{\rho }$ exceeding 0.9534, and $\mathbf {{\mathit{R}}}^{2}$ above 0.9552. Additionally, joint power, computed as the dot product of predicted joint moments and angular velocities, resulted in RMSE below 0.3823W/kg, $\boldsymbol{\rho }$ above 0.9771, and $\mathbf {{\mathit{R}}}^{2}$ above 0.8925. These results demonstrate the effectiveness of the proposed network in continuously estimating lower limb kinematics and kinetics, contributing to advancements in assist-as-needed exoskeleton control strategies.

How to optimally synchronize exoskeleton powered assistance remains a problem that limits the broad application of such devices. Kinematic change during frequent switching between go and stop, a common and representative activity of daily living (ADL), makes it challenging to predict the gait phase and deliver assistance due to unpredictable movements. Conventional adaptive oscillators (AO) have been verified to be effective in gait phase prediction in steady-state walking. When walking cadences are changed, it usually requires multiple walking strides to synchronize the assistance, causing inaccurate or even unwanted force disturbances that can be dangerous in some cases. To solve this problem, a gait-based AO is proposed in this paper. It has two-level AO systems. The high-level AO learns from last stride and updates the low-level AO, which is designed to estimate the gait phase of the current stride in real time. This approach significantly decreases the time required to learn walking kinematics, while simultaneously improving the accuracy of predictions. Experiments were conducted on seven participants, and the results showed that the proposed gait-based AO can predict the gait phase faster, more accurate, and more stable than the conventional one during non-steady-state walking Note to Practitioners —This article developed a new gait phase prediction method called gait-based adaptive oscillator. It aims to provide fast and reliable gait phase prediction in situations characterized by frequent transitioning between stop and go (non-steady-state walking). This method has the potential to be an alternative to existing phase prediction methods for robotic exoskeleton assisted walking in daily life, as it requires no model training before use while can synchronize exoskeleton assistance within two strides.

Frozen tofu, as a carbon and nitrogen source, is converted to N (0.6–6.7 at%) and O (3.6–9.5 at%) co-doped porous carbon with a specific surface area of up to 3134 m² g⁻¹ by one-step carbonization-activation. This hierarchical carbon consists of a high volume of mesopores (1.11 cm³ g⁻¹) and micropores (0.71 cm³ g⁻¹) with a typical pore size distribution between 0.8 and 4 nm. Supercapacitors with the obtained carbon as electrodes exhibit a specific capacitance of 243 F g⁻¹ (measured at 0.1 A g⁻¹) in 1 M sulfuric acid (H₂SO₄) aqueous electrolyte with a capacitance retention of 93% after 10 000 cycles at 10 A g⁻¹. The carbon also exhibits a specific capacitance of 170 F g⁻¹ (measured at 1 A g⁻¹) with a good rate capability (135 F g⁻¹ at 20 A g⁻¹) in 1-butyl-3-methylimidazolium tetrafluoroborate (BMIMBF₄) ionic liquid electrolyte, providing an energy density of 72 W h kg⁻¹ (at an average power density of 889 W kg⁻¹). A supercapacitor fabricated from the carbon in the ionic liquid electrolyte can readily power 25 light-emitting diodes in parallel for more than 2 min after being charged for 25 s at a current density of 10 A g^-1. In addition, as an anode for Li-ion batteries, the porous carbon exhibits a high reversible charge capacity of 2120 mA h g⁻¹ in the first cycle (measured at 0.1 A g⁻¹) or 1035 mA h g⁻¹ after 300 cycles (measured at 1 A g⁻¹).