As passive rehabilitation training with fixed trajectory ignores the active participation of patients, in order to increase the active participation of patients and improve the effect of rehabilitation training, this paper proposes an innovative adaptive sliding mode variable admittance (ASMVA) controller for the Lower Limb Rehabilitation Exoskeleton Robot. The ASMVA controller consists of an outer loop with variable admittance controller and an inner loop with an adaptive sliding mode controller. It estimates the wearer’s active muscle strength and movement intention by judging the deviation between the actual and standard interaction force of the wearer’s leg and the exoskeleton, thereby adaptively changing admittance controller parameters to alter training intensity. Three healthy volunteers engaged in further experimental studies, including trajectory tracking experiments with no admittance, fixed admittance, and variable admittance adjustment. The experimental results show that the proposed ASMVA control scheme has high control accuracy. Besides, the ASMVA can not only increase training intensity according to the active muscle strength of the patient during positive movement intention (so as to increase active participation of the patient), but also increase the amount of trajectory adjustment during negative movement intention to ensure the safety of the patient.

The lower-limb assist exoskeleton plays the role of torque assiting and compliant tracking for wearers to perform tasks. Accurate torque generation, backdrivability performance, low output impedance, and hardware compactness are essential factors for lower-limb exoskeleton to achieve better compliant physical interaction. This research studies a crank-slider series elastic actuator (CS-SEA) that can be used as a compact exoskeleton joint module. The device has a unique crank slider mechanism, and a set of linear springs are equipped inside the slider to guarantee the nonlinear stiffness of its physical impedance so that the torque effect can be improved and a high level of transparency can be achieved. The RBF-based sliding mode controller is chosen as the output torque controller of the exoskeleton, and the adaptive neuro-fuzzy sliding mode control law is designed and its stability is verified. The precise output force control performance of CS-SEA is verified by experiments. The actuator is incorporated into a knee exoskeleton prototype and was worn by the subjects. The experimental results demonstrate the precision of the compliant transparent and torque assisting control while interacting with the human wearer.

This paper presents the E-LEG, a novel semi-passive lower-limb exoskeleton for worker squatting assistance, with motorized tuning of the assistive squatting height. Compared with other passive industrial exoskeletons for the lower-limbs, the E-LEG presents novel design features namely inertial sensor for measuring the tilt angle of thigh and the novel electromagnetic switch for adjusting squat height. These features could enhance the effectiveness of the system. In addition to the introduction to exoskeleton design, this paper also reports the systematic experimental evaluation of human subjects. With the assistance of different conditions, the variability of muscular activity was evaluated in long-term static squatting task. The set of metrics to evaluate the effect of the device included leg muscle activity, plantar pressure fluctuation, plantar pressure center fluctuation and gait angles. Results show that the exoskeleton can reduce the muscular activity of the user during squatting, and it will have little affect the normal gait of the user during walking. In this study, we found that the E-LEG exoskeleton has potential effectiveness in reducing the muscular strain on long-term continuous squatting activities.

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

This article presents a design of an electrically powered spine-hip assistive exoskeleton (SHAE) to relieve back muscle fatigue of industrial workers and prevent back injuries. The rigid–flexible coupling driving realizes the performance of the system in terms of comfort, weight, flexibility, system durability, and industrial feasibility. It uses only two series elastic actuators (SEAs), coupled to drive two hip joints and one bio-inspired spine mechanism. The SEA is realized by the ball screw driving the elastic slider, which is, respectively, transmitted to the spine mechanism and the hip joints in cable driving mechanism and crank-slider mechanism. This device can distribute power to wearer's back and hips during a stoop–lifting event. In the state of lateral bending, axial rotation, and walking of the wearer, it can meet the needs of flexible movement of human body with the advantages of good back driving performance and low impedance. Furthermore, in order to transmit the torque determined by a high-level controller, an SEA force controller is implemented by measuring the elastic displacement of the spring unit installed in the slider. To evaluate the effectiveness of the developed exoskeleton mechanism, we experimentally verified the feasibility of different motion modes. Finally, the effectiveness of SHAE in assisting the waist was verified by collecting and comparing the electromyography signals of the relevant muscles of the waist during stoop–lifting exercise of the subjects with and without wearing the SHAE.

This article presents the design of an amphibious spherical robot with a sole focus on its mechanical design to achieve high torque, versatile motion modes, compact and lightweight structure, and high stability and traction, without relying on optimal control strategies. To achieve this, we unified the use of twin eccentric pendulums for high torque, flywheels for inertial stabilization and enhanced traction, and flywheel brakes for instantaneous torque to overcome obstacles. Dynamic analysis is performed, which gives an insight into the robot's motion and helps in making design decisions. High-level and low-level design decisions are discussed based on robot dynamics and intuition. Finally, a prototype of the designed spherical robot is assembled, and subjected to performance evaluation through terrestrial and aquatic locomotion experiments. The experimental results validated our design decisions, showing the design approach yields a spherical robot with stable and versatile locomotion, building the foundation for further improvements via optimal control and sensing.