A disturbance observer based intelligent control for nonstrict-feedback nonlinear systems

A quantitative evaluation of kinetic parameters, the joint’s range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device’s positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user’s vital signs directly from the body in an accurate and non–invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach’s subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post–operative rehabilitation and athletes’ training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user’s health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

/pdf/wearable-sensors-and-smart-devices-to-monitor-rehabilitation-jk0nhkw4.pdf

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms.

/pdf/a-tuning-ensemble-machine-learning-technique-for-cerebral-3gifmtwa.pdf

A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction

During the last decade, soft robotic systems, such as actuators and grippers, have been employed in various commercial applications. Due to the need to integrate robotic mechanisms into devices operating alongside humans, soft robotic systems concentrate increased scientific interest in tasks with intense human–robot interaction, especially for human-exoskeleton applications. Human exoskeletons are usually utilized for assistance and rehabilitation of patients with mobility disabilities and neurological disorders. Towards this direction, a fully functional soft robotic hand exoskeleton system was designed and developed, utilizing innovative air-pressurized soft actuators fabricated via additive manufacturing technologies. The CE-certified system consists of a control glove that copies the motion from the healthy hand and passes the fingers configuration to the exoskeleton applied on the affected hand, which consists of a soft exoskeleton glove (SEG) controlled with the assistance of one-axis flex sensors, micro-valves, and a proportional integral derivative (PID) controller. Each finger of the SEG moves independently due to the finger-dedicated motion control system. Furthermore, the real-time monitoring and control of the fabricated SEG are conducted via the developed software. In addition, the efficiency of the exoskeleton system was investigated through an experimental validation procedure with the involvement of healthy participants (control group) and patients, which evaluated the efficiency of the system, including safety, ergonomics, and comfort in its usage.

/pdf/a-novel-soft-robotic-exoskeleton-system-for-hand-2bhivn1r.pdf

A Novel Soft Robotic Exoskeleton System for Hand Rehabilitation and Assistance Purposes

Featured with high power density, excellent flexibility, shock absorption capacity, etc., pneumatic muscles (PMs) promote the development of exoskeleton robots and rehabilitation equipment. However, the complex nonlinearities of PMs limit efficiency optimization in closed-loop control, while the force-displacement coupling, soft materials, deficient workspace, etc., make it more difficult to simultaneously increase motion speeds and ensure the safety of multiple PM-actuated (PMA) robots. Although force sensors can currently be replaced by applying state estimation techniques, the amplification effects of measurement noises still compromise control accuracy and stability in practice. To this end, this article proposes a reinforcement learning-based robust motion control method with the prescribed performance, which achieves efficient and satisfactory tracking control for PMA robotic arms. In particular, by elaborately incorporating an integral term, a robust generalized proportional integral observer is used to eliminate measurement noises. Meanwhile, by using an actor–critic network to optimize control performance, an error-transformation-based continuous controller is designed to guarantee the uniformly ultimately boundedness of tracking errors. Compared with most existing methods, this article provides the first solution to restrict the entire transient and steady-state performance of PMA robotic arms, improve the noise suppression capability, and optimize the control efficiency simultaneously. Finally, complete stability analysis based on Lyapunov techniques is provided, and several groups of hardware experiments demonstrate the practicability and robustness of the proposed method. 

Reinforcement Learning-Based Prescribed Performance Motion Control of Pneumatic Muscle Actuated Robotic Arms With Measurement Noises

Robot-assisted thumb rehabilitation can improve the hand function of stroke patients because the thumb accounts for 40% of hand function. However, existing thumb rehabilitation robots are limited in terms of portability, comfort, adaptability, and independent joint actuation. This article proposes an untethered adaptive thumb exoskeleton that actively assists the 3-degree-of-freedom movements of the thumb. The exoskeleton is composed of an adaptive thumb mechanism and a spherical mechanism. The kinematics, statics, and performances of the adaptive thumb mechanism and the spherical mechanism are analyzed. Experimental validation is performed to test the workspace, self-alignment, interaction forces, admittance controller, and grasping assistance performance of the exoskeleton. The workspace and self-aligning experiments prove that the proposed exoskeleton can achieve a large workspace of the thumb joints and realize self-alignment. The interaction force experimental results show that the exoskeleton can reduce the tangential interaction forces by 76.8% and improve comfort. Finally, the control and delicate grasping assistance experiments validate the exoskeleton’s ability to realize the delicate grasping of the thumb. These characteristics show that the thumb exoskeleton has the potential to assist delicate thumb rehabilitation.

Development of an Untethered Adaptive Thumb Exoskeleton for Delicate Rehabilitation Assistance

Elastic torque sensors have been widely used for small-scale robots such as hand exoskeletons to achieve torque control. However, designing a miniaturized and lightweight elastic torque sensor with human–machine interaction safety is still a challenge. In this article, a novel miniaturized and lightweight elastomer-based clutched torque sensor is presented. A rubber spring is designed and used to reduce its volume and weight. A wafer disk clutch is devised to improve mechanical safety. The torque sensor is 29.5 mm $\times18$ mm $\times24$ mm in dimension and weighs 23 g. Compared with the state-of-the-art elastic torque sensors for hand exoskeletons, the volume-to-torque ratio is reduced by 15.48%, and its weight is reduced by 23.33%. Since the hysteresis characteristics of the rubber spring leads to a nonlinear deformation–torque relationship, an improved parametric Gaussian process regression (PGPR) method based on the nonlinear autoregressive moving average structure with exogenous inputs (NARMAX) is proposed. A combined kernel function for the improved PGPR is designed to improve the fitting performance. Finally, experiments have been conducted to verify the mechanical safety and torque sensing performance. The force caused by collision on the proposed torque sensor is less than that on the torque sensor without the clutch (reduced by 51.78%). The proposed hysteresis model can reduce the maximum absolute modeling error to 7% compared with those of other intelligent hysteresis models (the modeling error is 12.32%). Therefore, the experimental results indicate that the proposed torque sensor can improve the mechanical safety and achieve accurate torque sensing.

Design and Hysteresis Modeling of a Miniaturized Elastomer-Based Clutched Torque Sensor

Pneumatic artificial muscle (PAM) usually exhibits strong hysteresis nonlinearity and time-varying features that bring PAM modeling and control difficulties. In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy model is established based on nonlinear auto-regression moving average with exogenous input (NARMAX) structure to describe PAM’s characteristics. Experiments show that compared with other phenomenology-based models, the presented model has lower predictive error and better adaptability. Finally, a model predictive controller is designed and validated to verify the adaptive T-S fuzzy model’s practicability.

Adaptive Takagi-Sugeno Fuzzy Model for Pneumatic Artificial Muscles

In recent years, various series elastic actuators (SEAs) have been proposed to enhance the flexibility and safety of wearable exoskeletons. This paper proposes an SEA composed of wave springs and installs it at human-robot interaction port. Considering the hysteresis nonlinear characteristics of the SEA, displacement-force models of the SEA are established based on long short-term memory (LSTM) model and T-S fuzzy model in a nonlinear auto-regression moving average with exogenous input (NARMAX) structure. Based on the established models, the SEA can effectively serve as an interaction force sensor. Subsequently, the SEA is integrated into an elbow exoskeleton, and a compliant admittance controller is designed based on the displacement-force model. Experimental results demonstrate that the proposed approach effectively enhances the flexibility of human-robot interaction. 

A Compliant Elbow Exoskeleton with an SEA at Interaction Port

This paper proposes a novel index finger exoskeleton with semi-wrapped fixtures and elastomer-based clutched series elastic actuators. The semi-wrapped fixture is similar to a clip, which improves the convenience of donning/doffing and connection stability. The elastomer-based clutched series elastic actuator can limit the maximum transmission torque and improve passive safety. Second, the kinematic compatibility of the exoskeleton mechanism for the proximal interphalangeal joint is analyzed, and its kineto-statics model is built. To avoid the damage caused by the force along the phalanx, considering the individual difference in the size of the finger segment, a two-level optimization method is proposed to minimize the force along the phalanx. Finally, the performance of the proposed index finger exoskeleton is tested. Statistical results indicate that the donning/doffing time of the semi-wrapped fixture is significantly less than that of the Velcro. Compared with the Velcro, the average value of the maximum relative displacement between the fixture and the phalanx is reduced by 59.7%. Compared with the exoskeleton before optimization, the maximum force along the phalanx generated by the exoskeleton after optimization is reduced by 23.65%. The experimental results show that the proposed index finger exoskeleton can improve the convenience of donning/doffing, connection stability, comfort, and passive safety.

Design and Optimization of an Index Finger Exoskeleton With Semi-Wrapped Fixtures and Series Elastic Actuators

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