By accurately recognizing the wearer’s motion, the underwater exoskeleton enables more efficient human-machine collaboration and provides enhanced assistance in complex and dynamic underwater environments. In this study, we propose a soft underwater exosuit motion mode recognizer based on a long short-term memory network and convolutional neural networks, referred to as LSTM-CNN. This model is designed to perform two tasks: motion mode classification and state transition label recognition. First, the LSTM network extracts features from the time-series data, followed by further feature extraction and classification using the convolutional and fully connected networks. The recognition of motion modes relies on three IMU sensors placed on the left and right legs and the back of the torso of the soft underwater exosuit. On the dataset containing four classes, including non-assist, breaststroke, flutter kick, and underwater walking, LSTM-CNN achieved an overall accuracy of 99.943±0.006% in motion mode classification and 92.101±0.054% in state transition label recognition. The experimental results indicate that the LSTM-CNN achieves better accuracy and performs optimally across various evaluation metrics compared to the other methods.

Underwater assistance is crucial for individuals who depend on diving for their livelihood. In this paper, we propose a novel underwater exosuit actuator designed to assist with flutter kicking during diving, thereby decreasing the effort the diver has to exert. The actuator can provide bidirectional assistance to the up and downbeats when the diver kicks underwater, and has no restriction on leg movements when it is deactivated. Both the benchtop experiment and human subject tests were conducted to verify its performance. The benchtop experiment verified its kinematic features, while tests with five participants validated its assistive performance. The results indicate that the actuator delivers a peak torque of 0.0947 Nm/kg and a peak force of 100 N in both directions, while allowing free leg movement during walking or kicking when not powered, thus ensuring safety during diving.

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.

The design and development of new exoskeleton robots can help patients with lower limb paralysis realize autonomous walking. During the motion of an exoskeleton robot carrying patients, the inertia of the hip joint will have time-varying characteristics, which will cause fluctuations in the rotation angle and affect the walking stability of the exoskeleton robot. In this paper, we present the rotation angle control strategy for an exoskeleton robot assisted by paraplegic patients with the BP neural network tuning control strategy. First, based on the skeleton structure of human lower limbs, an exoskeleton robot with 12 degrees of freedom is designed and manufactured to help patients autonomously walk. Next, the dynamic model of the exoskeleton robot hip joint is established, which takes into account nonlinear factors such as transfer flexibility, friction torque, and time-varying load inertia. Then, BP neural networks are used to adjust the parameters of the position loop PID controllers in the hip joint, and the tracking error is reduced by adjusting the controller parameters in real time. Finally, walking experiments of the physical prototype of the exoskeleton robot show that the exoskeleton robot designed in this paper can help patients with lower limb paralysis walk autonomously, and the proposed control strategy can reduce the rotation angle tracking error of the hip joint.Note to Practitioners—This paper addresses the importance of dynamic modeling and control for the hip joint in the motion accuracy of exoskeleton robots. The mechanical structure of the proposed exoskeleton robot can realize the autonomous walking of patients with lower limb paralysis. The proposed dynamic modeling method is suitable for the servo system time-varying model of split-limb robots. In addition, the proposed BP neural network control strategy can improve the position control accuracy of time-varying systems, which is suitable for the real-time control of robots. Numerical simulation and physical experiments demonstrate the effectiveness of the proposed control strategy.

Assisting underwater movements improves divers' efficiency and reduces the risk of decompression sickness from physical activity. Although exoskeletons have been developed for numerous land-based scenarios, their application in underwater diving remains unexplored. This article proposes a soft underwater lower-limb exosuit designed to assist three aquatic movements: flutter kick, breaststroke kick, and underwater walk. We presented the mechanical design of the exosuit that is capable of assisting bidirectional leg movements in full kicking/gait cycle, while ensuring natural leg mobility without impeding normal leg function. A cascade force integral controller is also designed to resolve issues related to uncontrollable states and stiffness variations within the system. To verify the assistive performance of the system, experiments were conducted with nine participants to assess how the proposed exosuit aids in reducing metabolic cost across various motion patterns and frequencies. The findings indicate that the underwater exosuit effectively reduces the air consumption rate by$29.77\pm 7.68$% during flutter kick,$25.70\pm 5.99$% during breaststroke kick, and$18.35\pm 4.53$% during underwater walk.

Multi-sensor based motion tracking is of great interest to the robotics community as it may lessen the need for expensive optical motion capture equipment. However, the traditional convolution algorithms have difficulty adapting to the data due to the changes of joints’ relative position during motion. The time-series networks often used in the past ignore the spatial characteristics of sensors. We tackle this challenge by combining long short-term memory (LSTM) with graph convolution network (GCN), adding the prior knowledge of sensor distribution, and integrating it into the motion law through the adjacency matrix. This article proposes a novel shortcut enhanced LSTM-GCN network (SE-LSTM-GCN). It connects LSTM and GCN in sequence and extracts temporal and spatial features of data. At the same time, the shortcut is used in the network to enhance the output of two middle layers and to restore the filtered information. Our experimental results on two different motion tracking datasets show that the proposed network is able to learn the mapping relationship with better universality, less tracking error, and without increasing much training time, and can better perform human motion tracking tasks.Note to Practitioners—Accurate and real-time multiple soft sensors motion tracking suits are more accepted for their low cost. However, the soft-sensor based motion tracking is not comparable to the traditional optical equipment in prediction error. To this end, we present a novel network shortcut enhanced LSTM-GCN (SE-LSTM-GCN), consisting of shortcuts, long short-term memory (LSTM), and graph convolution network (GCN). The LSTM solves the non-linear and hysteresis of soft strain sensors, and GCN is integrated into the network since the knowledge of sensor location can be put into the adjacency matrix generated by the k-nearest neighbor (KNN). While shortcuts are used to enhance the output of middle layers to form combined features. Experimental results on two public datasets show that the proposed network is superior to competing algorithms in terms of prediction error. The network can be deployed in embedded devices, such as VR gloves to provide a better gaming experience. The current algorithm is based on the relationship between sensor data and distance. In future research, we will focus on adding other human kinematics laws to the network.

People who suffer from paraplegia completely lose sensory and locomotor functions; there are no known treatment methods for their recovery at this time. Exoskeleton robots have the potential to dramatically improve the locomotor ability of these individuals. Although some exoskeleton robots for paraplegic patients have been commercialized and are able to restore walking motion at present, the pilot must acquire the ability to maintain their balance and shift their weight using forearm crutches, which is very challenging for paraplegics. To make this easier, we propose a new automated intelligent gait planning method that integrates a finite-state machine (FSM) model as an underlying foundation and a gait generation model in addition to the exoskeleton system. The underlying FSM model is defined using an inverted pendulum model and a minimum jerk algorithm. To compare the planning gait, 33 volunteers provide normal walking gaits; there are two more volunteers (paraplegic and nonparaplegic) wearing the Shenzhen Institute of Advanced Technology (SIAT) exoskeleton robot to validate the effects of the proposed gait and offer the groups of surface electromyogram (sEMG) data for analysis. As a result, the input of the proposed gait planning method is simplified to two parameters. The proposed walking gait significantly reduces the arm muscle output. Note to Practitioners-This article was motivated by the problem that the four-degree of freedom (DoF) underactuated paraplegic rehabilitation lower limb exoskeleton robot lacks of the center of gravity (COG) transfer process when coordinating with paraplegia patients during the training process for beginner. The existing approach to deal with this problem generally is to train the pilot for obtaining the COG transfer ability by using crutches. This article suggests a gait planning method for the four-DOF underactuated rehabilitation lower limb exoskeleton robot considering the COG transfer process to make the exoskeleton robot coordinate with a pilot and ensure safety. The gait planning method is based on the inverted pendulum model and simplified to several parameters. By adjusting these parameters, the step length, step height, walking speed, and the shape of gait can be adjusted according to the requirements of the exoskeleton robot and pilot. In this article, we mathematically characterize a gait planning method for the exoskeleton control strategy. Preliminary online experiments suggest that this approach is feasible and can significantly reduce the arm muscle output of pilot. In future research, we will adjust the gait by estimating the velocity of center of mass (COM) of the pilot to make the exoskeleton robot coordinate with pilot actively.

The advent of myoelectric control schemes provides promising chances for locomotion empowerment and restoration of those with disabilities. Despite substantial efforts have been made into advancing sEMG-based motion recognition, it may be a little tricky to determine appropriate muscles and features for people with muscle disorders or different muscle use preferences. To mitigate it, an advantageous sEMG channel and feature selection method based on ReliefF algorithm was proposed. Related experiments were conducted on a eight able-bodied subject database to showcase the feasibility and efficiency of the proposed approach, that is, considerably high classification performance was maintained with the original feature set reduced by more than half. Ulteriorly, we also investigated the influences of different number of neighbors or features on classification accuracy for ascertaining the optimal values. The strengths of our proposed method lie in not only customizing channel and feature selection for individual users, but also offering preliminary insight for a general mapping mechanism between human muscles and corresponding motions.

Though the rapidly development in exoskeleton robot area, there are still some challenges, such as the safe and friendly human-machine interaction. In this paper, we proposed a novel knee exoskeleton driven by Series Elastic Actuator (SEA) which is a compliant actuator has the characteristic low output impedance, low friction, high quality force control and back-drivability. As for walking assistance, the force control is vital. Here, we mainly apply two control method to achieve the robust force control, one is the feedback control with enchaned distrubance observer, another is the Integral Sliding Mode Control (ISM). We test both the stability and force tracking performance of the two controler. The result showed that both algorithms can achieve the satisfactory performance and the ISM algorithm obtained the slightly better result.