Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in exoskeleton technology published since 2020, with particular emphasis on the development of structural designs for lower-limb exoskeletons employed in locomotion assistance. We employed a systematic literature review methodology, categorizing the included studies into three main types: rigid exoskeleton, soft exoskeleton, and tethered platform. The current development status of robotic exoskeletons is analyzed based on publication year, system weight, target assistive joints, and main effects. Furthermore, we examine the factors driving these advancements and their implications for the field. The key challenges and opportunities that may influence the future development of exoskeleton technologies are also highlighted in this review.

Flapping flight is an important mode of insect flight, and its unique flapping motion pattern enables it to fly efficiently in complex environments. This paper takes a biomimetic moth flapping-wing aircraft as the research object and proposes a periodic function composed of two sine functions with different frequencies as the flapping function. This paper explores the effect of this flapping function on the flight performance of flapping-wing aircraft and verifies whether it can be applied to the flight control of flapping-wing aircraft. Firstly, through the study of biomimetic mechanisms, the basic structure of the flapping-wing aircraft is roughly designed; then, the flapping motion is simplified, a rigid wing flapping motion model is established, and the key parameters affecting the average lift are determined. Next, a virtual wind tunnel simulation platform is built, and the key parameters of the flapping function that affect lift generation are simulated and calculated. Finally, an experimental prototype of a biomimetic moth flapping-wing aircraft is designed and manufactured. Through flight experiments, the effects of flapping amplitude, flapping frequency, and mid-position angle in the flapping function on the flight performance of the biomimetic flapping-wing aircraft are verified. The key control parameters are clarified, the control strategy of the flapping-wing aircraft is optimized, and the maneuverability and controllability of the aircraft are improved, providing a theoretical basis and practical support for the development of control methods for biomimetic flapping-wing aircraft.

This study aims to develop a “sensing-actuation integrated” intelligent pressurized thigh band to assist the quadriceps, indirectly alleviate knee joint load, and achieve high-precision recognition of movement modes. The system comprises a portable integrated controller and a textile-integrated flexible pneumatic actuator. Experiments were conducted to evaluate the effects of different air bladder pressure conditions on metabolic rate and muscle activity. Simultaneously, pneumatic data corresponding to six common activities were collected, and a lightweight deep learning model was developed to enable high-precision motion classification. Finally, the model was deployed to an embedded platform to demonstrate its application potential. Results indicate that appropriate air bladder pressure significantly reduces quadriceps muscle activation and average metabolic cost. Furthermore, the deep learning model achieved 99.17% accuracy in recognizing the six activities and was successfully deployed to the embedded platform. This study validates the effectiveness of the intelligent pressurized thigh band in improving locomotor performance under static pressures and demonstrates the potential of air bladder pressure variations as a proxy indicator for movement intent for future closed-loop control.

Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios.

Lightweight hip exoskeletons typically rely on proximal sensing, which limits access to distal lower-limb kinematics and may compromise gait phase synchronization during dynamic locomotion. Adding dedicated distal encoders can improve perception but increases system mass, wiring complexity, and power consumption. To address this trade-off, this paper presents a functionally integrated knee device that combines passive magnetic-strain motion sensing with mechanical energy harvesting to provide knee kinematic information for hip exoskeleton gait phase estimation and explore harvested-energy supply for the local sensing node. The knee device employs a unidirectional pawl-ratchet planetary transmission to harvest mechanical energy during knee extension, achieving an electrical power output of up to 0.378 W. Meanwhile, a passive magnetic-strain sensing array converts knee rotation into high-frequency electrical signals without active electromagnetic excitation. A dual-head 1D-CNN-LSTM network is developed to simultaneously classify locomotion modes and reconstruct continuous knee joint trajectories from 1000 Hz strain signals. The network achieves an overall locomotion classification accuracy of 99.72%, with a trajectory reconstruction MAE of 3.2989°, RMSE of 4.1664°, and R² of 0.9801 on the independent test dataset covering level walking and running at speeds of 1–4 m/s and stair ascent. The reconstructed knee kinematics are further fused with hip motor encoder feedback to drive a speed-adaptive finite state machine for real-time stance/swing phase estimation. Compared with plantar-pressure-based reference events, the mean phase switching errors remain within 0.36% of the gait cycle for swing-to-stance transitions and 2.07% of the gait cycle for stance-to-swing transitions across locomotion speeds of 1–4 m/s. The observed phase advance at the stance-to-swing transition also indicates the potential of the proposed timing strategy to compensate for actuator response latency. These results demonstrate that functionally integrating passive distal perception with energy harvesting can provide reliable kinematic information for phase-synchronized hip exoskeleton control while reducing the need for dedicated distal sensing hardware.

Wearable electronic systems require compact and sustainable power sources together with reliable motion-sensing functions. This study presents a gear-driven plantar energy harvester that integrates biomechanical energy conversion with self-sensing locomotion recognition. The device converts low-frequency vertical foot loading into rotary motion through a wedge–lever transmission and amplifies the rotational speed using a multistage gear train with a total transmission ratio of 12. A one-way bearing enables directional power transmission during loading and prevents reverse rotation during recovery. The generated voltage serves both as the electrical output and as the sensing signal for locomotion recognition. Human-subject experiments were conducted under six locomotion modes: walking at 1, 2 and 3 m/s; running; ascending; and descending. Voltage signals were sampled at 2000 Hz and segmented into overlapping sequences. A CNN–LSTM model was used to extract local waveform features and temporal dependencies from the nonstationary signals. The model achieved an overall recognition accuracy of 98.8%, with most errors occurring between ascending and descending. The results demonstrate that a single plantar device can simultaneously harvest biomechanical energy and provide motion-related information, offering a compact solution for integrated energy harvesting and self-sensing in wearable systems.