Self-balancing lower limb exoskeletons (SBLLE) hold the promise of helping individuals with diverse mobility impairments regain their ability to walk. Equipping exoskeletons with stable turning capabilities is crucial for their real-world application. This paper presents an effective control framework that enables stable turning motions in SBLLE without relying on external assistive devices. A turn-specific gait planner has been designed to generate stable reference gaits. Moreover, an adaptive variable impedance controller (AVIC) is introduced, which adjusts the center of mass (CoM) motion in real time to maintain balance during walking, even when carrying different patients. The proposed approach is validated through simulations, comparative studies, and multi-stage human-subject experiments with ten participants, ranging from healthy users to individuals with severe lower-limb motor impairment. Results consistently demonstrate stable turning performance, with the Zero Moment Point (ZMP) remaining within the support polygon during the entire walking process.

Self-balancing walking exoskeletons (SBWEs), which enable paralyzed individuals to walk without assistive devices, have been increasingly employed in rehabilitation training. This paper proposes a Kelvin-Voigt viscoelastic model-based bioinspired viscoelastic compliance controller (BVCC) for a novel SBWE named AutoLEE-II, which features high structural rigidity, low leg inertia relative to center of mass (CoM), and small hip joint axis misalignment between the user and SBWE. First, a novel series-parallel hybrid mechanism is designed for AutoLEE-II. This mechanism, inspired by the lower limbs of humans, reduces hip axis misalignment between the user and SBWE, decreases leg inertia relative to CoM, and improves structural stiffness. Second, a BVCC mimicking biological muscle is proposed to introduce viscoelastic compliance to SBWE to maintain locomotion stability of the SBWE during standing and walking. The BVCC is robust to the variable physical parameters of different users. Finally, self-balancing walking experiments are conducted with AutoLEE-II with empty load, manikin load and human subject load to validate the performance of AutoLEE-II and the proposed compliance controller BVCC. Note to Practitioners—This paper aims to design a self-balancing walking exoskeleton (SBWE) that provides rehabilitation training exercise and walking assistance services for individuals with hemiplegia, paraplegia, and quadriplegia. First, we biomimetically designed the mechanical structure of the SBWE, named AutoLEE-II based on the distribution of human joints and connecting links. The bionic mechanism reduces axis misalignment between the SBWE and users, improves stiffness and reduces the inertia of the legs relative to center of mass. We then designed a bioinspired viscoelastic compliance controller (BVCC) based on the centroid dynamics model, which is robust to the physical properties of the user and introduces the SBWE with active compliance. Finally, self-balancing walking experiments with an empty load, a manikin load and human subject loads are performed to validate mechanical structure of the proposed AutoLEE-II and the locomotion stability of the physical parameter robust BVCC.

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.

The operation accuracy of humanoid robot hands driven by the tendon sheath will be reduced due to the influence of friction torque during rotation, which is not conducive to the dexterous operations of humanoid robot hands. In order to improve the control accuracy of humanoid robot hands, this paper proposes a control strategy based on the disturbance observer compensation, which eliminates the external disturbance torque by compensating the friction torque. Firstly, this article proposes the mechanical structure of humanoid robot hands driven by the tendon sheath with 19 degrees of freedom (DOF). This humanoid robot hands can grasp most irregular objects. Next, the dynamic model of humanoid robot hands’ drive systems is established based on the tendon sheath transmission theory. The driving system’s dynamic model reveals the influence of friction torque on the motion accuracy of the humanoid robot hand. Then, the disturbance observer (DOB) is designed based on the robust stability theorem. The DOB is used to improve the control accuracy of the driving system, thereby improving the operational accuracy of humanoid robot hands. Finally, this article conducts simulation rotation tracking control and prototype grasping control experiments on humanoid robot hands. The experimental results show that the proposed control strategy based on disturbance compensation can effectively improve the operational accuracy of humanoid robot hands. Note to Practitioners—This paper proposes a humanoid hand with 19 degrees of freedom based on the tendon-driven theory and applies the cable theory for its dynamic modeling. To address the issue of decreased precision caused by friction in practical operations, a friction compensation control strategy based on disturbance observer is proposed in this study. This control strategy improves the motion accuracy and stability of the mechanical hand. Finally, the effectiveness of the proposed control strategy is demonstrated through numerical simulation and experimental validation.

Lateral walking gait phase recognition and prediction are the premise of hip exoskeleton application in lateral resistance walk exercise. We presented a fusion network with stacked denoise autoencoder and meta learning (SDA-NN-ML) to recognize gait phase and predict gait percentage from IMU signals. Experiments were conducted to detect the four lateral walking gait phases and predict their percentage across different speeds. The performance of SDA-NN-ML and Support Vector Machine (SVM), Adaptive Boosting (AdaBoost) and Long Short Term Memory (LSTM) were evaluated. The cross-subject recognition accuracy of SDA-NN-ML (89.94%) decreased by 4.62% compared to the training accuracy, which outperformed SVM (8.60%), AdaBoost (5.61%), and LSTM (7.12%). For real-time and cross-subject prediction of gait phase percentage, the RMSE of SDA-NN-ML (0.2043) outperformed that of a single regression network (0.2426). With a signal noise ratio of 100:30, the cross-subject recognition accuracy decreased by a mere 5.70%, while the prediction result (RMSE) of SDA-NN-ML increased by 0.0167 when compared to the noise-free results. SDA-NN-ML demonstrates a stable multi-step-ahead prediction ability with an accuracy higher than 82.50% and an RMSE of less than 0.23 when the ahead time is less than 200 ms. The results demonstrated that the proposed method has high accuracy and robust performance in lateral walking gait recognition and prediction.

3D human pose estimation describes estimating 3D articulation structure of a person from an image or a video. The technology has massive potential because it can enable tracking people and analyzing motion in real time. Recently, much research has been conducted to optimize human pose estimation, but few works have focused on reviewing 3D human pose estimation. In this paper, we offer a comprehensive survey of the state-of-the-art methods for 3D human pose estimation, referred to as pose estimation solutions, implementations on images or videos that contain different numbers of people and advanced 3D human pose estimation techniques. Furthermore, different kinds of algorithms are further subdivided into sub-categories and compared in light of different methodologies. To the best of our knowledge, this is the first such comprehensive survey of the recent progress of 3D human pose estimation and will hopefully facilitate the completion, refinement and applications of 3D human pose estimation.

This paper aims to examine the consumer acceptance of online pharmacies and to find out the different drivers of online medicine purchase intention. Based on the unified theory of acceptance and use of technology (UTAUT) model this paper includes five explanatory constructs. Data from consumers (n=274) questionnaires were collected in China, and were tested against the proposed research model. The results show that performance expectancy, social influence, perceived trust and perceived risk have directly significant influences on consumers' adoption intention of online medicine purchase, effort expectancy has positive influence on performance expectancy, and perceived trust has significant negative influence on perceived risk. Suggestions are made concerning how to develop online pharmacies and future research directions are discussed.