Wearable Robotic Systems Research Articles

An Actively Vision-Assisted Low-Load Wearable Hand Function Mirror Rehabilitation System

The restoration of fine motor function in the hand is crucial for stroke survivors with hemiplegia to reintegrate into daily life and presents a significant challenge in post-stroke rehabilitation. Current mirror rehabilitation systems based on wearable devices require medical professionals or caregivers to assist patients in donning sensor gloves on the healthy side, thus hindering autonomous training, increasing labor costs, and imposing psychological burdens on patients. This study developed a low-load wearable hand function mirror rehabilitation robotic system based on visual gesture recognition. The system incorporates an active visual apparatus capable of adjusting its position and viewpoint autonomously, enabling the subtle monitoring of the healthy side’s gesture throughout the rehabilitation process. Consequently, patients only need to wear the device on their impaired hand to complete the mirror training, facilitating independent rehabilitation exercises. An algorithm based on hand key point gesture recognition was developed, which is capable of automatically identifying eight distinct gestures. Additionally, the system supports remote audio–video interaction during training sessions, addressing the lack of professional guidance in independent rehabilitation. A prototype of the system was constructed, a dataset for hand gesture recognition was collected, and the system’s performance as well as functionality were rigorously tested. The results indicate that the gesture recognition accuracy exceeds 90% under ten-fold cross-validation. The system enables operators to independently complete hand rehabilitation training, while the active visual system accommodates a patient’s rehabilitation needs across different postures. This study explores methods for autonomous hand function rehabilitation training, thereby offering valuable insights for future research on hand function recovery.

Unveiling human biomechanics: insights into lower limb responses to disturbances that can trigger a fall.

Slip-related falls are a significant concern, particularly for vulnerable populations such as the elderly and individuals with gait disorders, necessitating effective preventive measures. This manuscript presents a biomechanical study of how the lower limbs react to perturbations that can trigger a slip-like fall, with the ultimate goal of identifying target specifications for developing a wearable robotic system for slip-like fall prevention. Our analysis provides a comprehensive understanding of the natural human biomechanical response to slip perturbations in both slipping and trailing legs, by innovatively collecting parameters from both the sagittal and frontal plane since both play pivotal roles in maintaining stability and preventing falls and thus provide new insights to fall prevention. We investigated various external factors, including gait speed, surface inclination, slipping foot, and perturbation intensity, while collecting diverse data sets encompassing kinematic, spatiotemporal parameters, electromyographic data, as well as torque, range of motion, rotations per minute, detection, and actuation times. The biomechanical response to slip-like perturbations by the hips, knees, and ankles of the slipping leg was characterized by extension, flexion, and plantarflexion moments, respectively. In the trailing leg, responses included hip flexion, knee extension, and ankle plantarflexion. Additionally, these responses were influenced by gait speed, surface inclination, and perturbation intensity. Our study identified target range of motion parameters of 85.19°, 106.34°, and 95.23° for the hips, knees, and ankles, respectively. Furthermore, rotations per minute values ranged from 17.85 to 51.10 for the hip, 21.73 to 63.80 for the knee, and 17.52 to 57.14 for the ankle joints. Finally, flexion/extension torque values were estimated as -3.05 to 3.22 Nm/kg for the hip, -1.70 to 2.34 Nm/kg for the knee, and -2.21 to 0.90 Nm/kg for the ankle joints. This study contributes valuable insights into the biomechanical aspects of slip-like fall prevention and informs the development of wearable robotic systems to enhance safety in vulnerable populations.

Improving Biological Joint Moment Estimation During Real-World Tasks With EMG and Instrumented Insoles.

Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique. We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities during both activity types. Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p < 0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p < 0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p < 0.05) which was significantly higher than either modality individually (p < 0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p < 0.05). These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.

Soft pneumatic actuators for pushing fingers into extension

BackgroundCompliant pneumatic actuators possess many characteristics that are desirable for wearable robotic systems. These actuators can be lightweight, integrated with clothing, and accommodate uncontrolled degrees of freedom. These attributes are especially desirable for hand exoskeletons, where the soft actuator can conform to the highly variable digit shape. In particular, locating the pneumatic actuator on the palmar side of the digit may have benefits for assisting finger extension and resisting unwanted finger flexion, but this configuration requires suppleness to allow digit flexion while retaining sufficient stiffness to assist extension.MethodsTo meet these needs, we designed an actuator consisting of a hollow chamber long enough to span the joints of each digit while sufficiently narrow not to inhibit finger adduction. We explored the geometrical design parameter space for this chamber in terms of shape, dimensions, and wall thickness. After fabricating an elastomer-based prototype for each actuator design, we measured active extension force and passive resistance to bending for each chamber using a mechanical jig. We also created a finite element model for each chamber to enable estimation of the impact of chamber deformation, caused by joint rotation, on airflow through the chamber. Finally, we created a prototype hand exoskeleton with the chamber parameters yielding the best outcomes.ResultsA rectangular cross-sectional area was preferable to a semi-obround shape for the chamber; wall thickness also impacted performance. Extension joint torque reached 0.33 N-m at a low chamber pressure of 48.3 kPa. The finite element model confirmed that airflow for the rectangular chamber remained high despite deformation resulting from joint rotation. The hand exoskeleton created with the rectangular chambers enabled rapid movement, with a cycle time of 1.1 s for voluntary flexion followed by actuated extension.ConclusionsThe developed soft actuators are feasible for use in promoting finger extension from the palmar side of the hand. This placement utilizes pushing rather than pulling for digit extension, which is more comfortable and safer. The small chamber volumes allow rapid filling and evacuation to facilitate relatively high frequency finger movements.

Bionic blink improves real-time eye closure in unilateral facial paralysis

Facial paralysis is the inability to move facial muscles thereby impairing the ability to blink and make facial expressions. Depending on the localization of the nerve malfunction it is subcategorised into central or peripheral and is usually unilateral. This leads to health deficits stemming from corneal dryness and social ostracization. Objective: Electrical stimulation shows promise as a method through which to restore the blink function and as a result improve eye health. However, it is unknown whether a real-time, myoelectrically controlled, neurostimulating device can be used as assistance to this pathological condition. Approach: We developed NEURO-BLINK, a wearable robotic system, that can detect the volitional healthy contralateral blink through electromyography and electrically stimulate the impaired subcutaneous facial nerve and orbicularis oculi muscle to compensate for lost blink function. Alongside the system, we developed a method to evaluate optimal electrode placement through the relationship between blink amplitude and injected charge. Main results: Ten patients with unilateral facial palsy were enrolled in the NEURO-BLINK study, with eight completing testing under two conditions. (1) where the stimulation was cued with an auditory signal (i.e. paced controlled) and (2) synchronized with the natural blink (i.e. myoelectrically controlled). In both scenarios, overall eye closure (distance between eyelids) and cornea coverage measured with high FPS video were found to significantly improve when measured in real-time, while no significant clinical changes were found immediately after use. Significance: This work takes steps towards the development of a portable medical device for blink restoration and facial stimulation which has the potential to improve long-term ocular health.

Recent Progress of Bioinspired Triboelectric Nanogenerators for Electronic Skins and Human–Machine Interaction

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human–machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices’ responsiveness and adaptability. The exploration begins with an overview of TENGs’ operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs’ potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications.

Design and evaluation of a wearable vascular interventional surgical robot system.

Remote-controlled robotic vascular interventional surgery can reduce radiation exposure to interventional physicians and improve safety. However, inconvenient operation and lack of force feedback limit its application. A new wearable robotic system for vascular interventional surgery is designed, which is more flexible in operation. It ensures the safety of surgery through haptic force feedback. The system was evaluated by human vascular models and animal experiments. The average static error of the system is 0.048mm when the axial motion is 250mm and 1.259° when the rotational motion is 400°. The average error of the force feedback is 0.021N. The results of vascular model experiments and animal experiments demonstrate the feasibility and safety of the system. The proposed robotic system can assist physicians in remotely delivering standard catheters or guidewires. The system is more flexible and uses haptic force feedback to ensure surgical safety.

Advances in the Preparation of Tough Conductive Hydrogels for Flexible Sensors.

The rapid development of tough conductive hydrogels has led to considerable progress in the fields of tissue engineering, soft robots, flexible electronics, etc. Compared to other kinds of traditional sensing materials, tough conductive hydrogels have advantages in flexibility, stretchability and biocompatibility due to their biological structures. Numerous hydrogel flexible sensors have been developed based on specific demands for practical applications. This review focuses on tough conductive hydrogels for flexible sensors. Representative tactics to construct tough hydrogels and strategies to fulfill conductivity, which are of significance to fabricating tough conductive hydrogels, are briefly reviewed. Then, diverse tough conductive hydrogels are presented and discussed. Additionally, recent advancements in flexible sensors assembled with different tough conductive hydrogels as well as various designed structures and their sensing performances are demonstrated in detail. Applications, including the wearable skins, bionic muscles and robotic systems of these hydrogel-based flexible sensors with resistive and capacitive modes are discussed. Some perspectives on tough conductive hydrogels for flexible sensors are also stated at the end. This review will provide a comprehensive understanding of tough conductive hydrogels and will offer clues to researchers who have interests in pursuing flexible sensors.

Development of the Power Assist System for High Efficiency and Lightweight Wearable Robot in Unstructured Battlefield

The wearable robot system is designed to assist human skeletal and muscular systems for enhancing user’s abilities in various fields, including medical, industrial, and military. The military has an expanding need for wearable robots with the integration of surveillance/control systems and advanced equipment in unstructured battlefield environments. However, there is a lack of research on the design and mechanism of wearable robots, especially for power assist systems. This study proposes a lightweight wearable robot system that provides comfortable wear and muscle support effects in various movements for soldiers performing high-strength and endurance missions. The Power assist mechanism is described and verified, and the tasks that require power assist are analyzed. This study explain the system including its driving mechanism, control system, and mechanical design. Finally, the performance of the robot is verified through experiments and evaluations, demonstrating its effectiveness in muscle support.

Design and Implementation of a Portable Knee Actuator for the Improvement of Crouch Gait in Children with Cerebral Palsy.

Common manifestation of spastic Cerebral Palsy (CP) are abnormal gait pathologies. These conditions require greater energy expenditure to successfully ambulate and are linked with significant deterioration in joint health and childhood musculoskeletal development. Crouch gait presents with knee hyperflexion throughout stance due to extensor muscle weakness and spasticity in flexor muscles stemming from neurological damage. The goal of this study was to develop a wearable cable-driven robotic system that applies controlled perturbation to the knee joint during overground walking in children with CP. Two children with spastic CP were recruited in this pilot study. They were tested in two conditions, i.e., applying knee resistance vs. knee assistance during overground walking. Kinematic and EMG data were recorded during overground walking. Data indicated that it was feasible to apply controlled knee perturbation torque during overground walking in children with crouch and preliminary results showed an improvement in crouch gait pattern in children with CP after one session of walking with the robotic system.Clinical Relevance- This study might have a potential clinical significance modifying neuromuscular control of CP patients with Crouch Gait.

Training Induced Improvement on Human Perception of the Force Feedback Provided by A Soft Robotic Glove.

Creating haptic interface by glove-based wearable robotic system has become an increasingly interested topic in the area of human robotic interaction. Many force feedback gloves are constructed based on soft actuators. However, the recent development of haptic and force feedback technology mainly focused on the advancement of the actuating components and mechanism, and innovation of the force feedback rendering algorithms. It seems that another important part of this human-robot-interaction loop, i.e. the human factors, were understudied. Here, this study focused on the learning effect in haptic perception. We designed a pneumatic muscle-based force feedback robotic glove which can provide customized force feedback to the dorsal surface of each finger. An experiment was carried out with a specifically training procedure and evaluation on the force feedback perceptions. The results show that practice-induced improvement can be achieved by this training, allowing people have a better perception of the force feedback provided by this glove.

Human Joint Axis Estimation Algorithm Using a Single Sensor to Recognize the Intention of the Wearer of a Wearable Robot

Wearable robots have been developed to aid or substitute human gait locomotion. To assist gait locomotion based on the intention of the wearer, gait pattern analysis is required with a wearable sensor that measures body information, such as the joint angular velocity. However, it is difficult to measure the joint angular velocity precisely because of the attachment position of a sensor that has a curvature and an anatomical human joint axis that is invisible. Therefore, a sensor calibration algorithm that aligns the sensor axis with an anatomical human joint axis is required to provide appropriate assistance to the wearer. Hence, in this study, a new and simple sensor calibration algorithm that uses a single sensor to simplify the robot system and reduce its weight is proposed. The attachment position of the sensor may change because the wearer shakes the body or collides with the ground when walking. Thus, a continuous sensor-compensation algorithm is proposed to correct for the changes in the location of the sensor. The effectiveness of the proposed algorithm is demonstrated through gait locomotion experiments on various paths. In addition to the gait locomotion experiments, the proposed algorithm is applied to a real wearable robot system. An ankle-assist wearable robot experiment reveals that the proposed algorithm can enhance the sensing and recognition performance of the robot system.

Flexible Impact-Resistant Composites with Bioinspired Three-Dimensional Solid–Liquid Lattice Designs

The ubiquitous solid-liquid systems in nature usually present an interesting mechanical property, the rate-dependent stiffness, which could be exploited for impact protection in flexible systems. Herein, a typical natural system, the durian peel, has been systematically characterized and studied, showing a solid-liquid dual-phase cellular structure. A bioinspired design of flexible impact-resistant composites is then proposed by combining 3D lattices and shear thickening fluids. The resulting dual-phase composites offer, simultaneously, low moduli (e.g., 71.9 kPa, lower than those of many reported soft composites) under quasi-static conditions and excellent energy absorption (e.g., 425.4 kJ/m3, which is close to those of metallic and glass-based lattices) upon dynamic impact. Numerical simulations based on finite element analyses were carried out to understand the enhanced buffering of the developed composites, unveiling a lattice-guided fluid-structure interaction mechanism. Such biomimetic lattice-based flexible impact-resistant composites hold promising potential for the development of next-generation flexible protection systems that can be used in wearable electronics and robotic systems.

Feature Selection using Random Forest Classifier for Foot Strike Event Detection in Toe Walkers

Automated Gait event identification of Foot Strike (FS) and Foot Off (FO) in pathological gait data, can be time saving in comparison to conventional manual annotations done currently. Identification of FS and FO allows breaking walking trials into gait cycles and hence aids in comparison of gait parameters like joint angles, forces and moments across gait cycles. Automated Gait Event Detection is also useful in development of wearable sensor devices and robotic systems that assist gait. Researchers have proposed several automatic gait event detection algorithms based on kinematic parameters and systematic study of the literature suggests specific parameters to have higher contribution in identification of FS event in all common pathological gait patterns. We used Random Forest Classifier Feature selection technique to identify high contributing features in FS event in toe walking pediatric pathological gait dataset and the results suggest high similarity in selected features by the machine learning technique with those suggested by popular event detection algorithms based on kinematic parameters for pathological gait. Hence we conclude that RFC feature selection is suitable for feature selection in toe walkers gait dataset for event detection purpose. Keyword : Feature selection, foot off, foot strike, pathological gait.

Energy-Efficient Actuator Design Principles for Robotic Leg Prostheses and Exoskeletons: A Review of Series Elasticity and Backdrivability

AbstractRobotic leg prostheses and exoskeletons have traditionally been designed using highly-geared motor-transmission systems that minimally exploit the passive dynamics of human locomotion, resulting in inefficient actuators that require significant energy consumption and thus provide limited battery-powered operation or require large onboard batteries. Here we review two of the leading energy-efficient actuator design principles for legged and wearable robotic systems: series elasticity and backdrivability. As shown by inverse dynamic simulations of walking, there are periods of negative joint mechanical work that can be used to increase efficiency by recycling some of the otherwise dissipated energy using series elastic actuators and/or backdriveable actuators with energy regeneration. Series elastic actuators can improve shock tolerance during foot-ground impacts and reduce the peak power and energy consumption of the electric motor via mechanical energy storage and return. However, actuators with series elasticity tend to have lower output torque, increased mass and architecture complexity due to the added physical spring, and limited force and torque control bandwidth. High torque density motors with low-ratio transmissions, known as quasi-direct drives, can likewise achieve low output impedance and high backdrivability, allowing for safe and compliant human-robot physical interactions, in addition to energy regeneration. However, torque-dense motors tend to have higher Joule heating losses, greater motor mass and inertia, and require specialized motor drivers for real-time control. While each actuator design has advantages and drawbacks, designers should consider the energy-efficiency of robotic leg prostheses and exoskeletons during daily locomotor activities besides continuous level-ground walking.

Hamilton–Jacobi Inequality Adaptive Robust Learning Tracking Controller of Wearable Robotic Knee System

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton–Jacobi Inequality (HJI) approach. WRK dynamics are established using the Lagrange approach at the outset of the analysis. Afterwards, the L2 gain technique is applied to enhance the control motion solutions and provide the main features of the designed WRK control systems. To prove the stability of the controlled system, the HJI approach is investigated next using optimization techniques. The synthesized RBF NN algorithm supports the easy implementation of the adaptive controller, as well as ensuring the stability of the WRK system. An analysis of the numerical simulation results is performed in order to demonstrate the robustness and effectiveness of the proposed tracking control algorithm. The results showed the ability of the suggested controller of this study to find a solution to uncertainties.

Reinforcement learning based variable damping control of wearable robotic limbs for maintaining astronaut pose during extravehicular activity.

As astronauts perform on-orbit servicing of extravehicular activity (EVA) without the help of the space station's robotic arms, it will be rather difficult and labor-consuming to maintain the appropriate position in case of impact. In order to solve this problem, we propose the development of a wearable robotic limb system for astronaut assistance and a variable damping control method for maintaining the astronaut's position. The requirements of the astronaut's impact-resisting ability during EVA were analyzed, including the capabilities of deviation resistance, fast return, oscillation resistance, and accurate return. To meet these needs, the system of the astronaut with robotic limbs was modeled and simplified. In combination with this simplified model and a reinforcement learning algorithm, a variable damping controller for the end of the robotic limb was obtained, which can regulate the dynamic performance of the robot end to resist oscillation after impact. A weightless simulation environment for the astronaut with robotic limbs was constructed. The simulation results demonstrate that the proposed method can meet the recommended requirements for maintaining an astronaut's position during EVA. No matter how the damping coefficient was set, the fixed damping control method failed to meet all four requirements at the same time. In comparison to the fixed damping control method, the variable damping controller proposed in this paper fully satisfied all the impact-resisting requirements by itself. It could prevent excessive deviation from the original position and was able to achieve a fast return to the starting point. The maximum deviation displacement was reduced by 39.3% and the recovery time was cut by 17.7%. Besides, it also had the ability to prevent reciprocating oscillation and return to the original position accurately.

Soft wearable flexible bioelectronics integrated with an ankle-foot exoskeleton for estimation of metabolic costs and physical effort

Activities and physical effort have been commonly estimated using a metabolic rate through indirect calorimetry to capture breath information. The physical effort represents the work hardness used to optimize wearable robotic systems. Thus, personalization and rapid optimization of the effort are critical. Although respirometry is the gold standard for estimating metabolic costs, this method requires a heavy, bulky, and rigid system, limiting the system’s field deployability. Here, this paper reports a soft, flexible bioelectronic system that integrates a wearable ankle-foot exoskeleton, used to estimate metabolic costs and physical effort, demonstrating the potential for real-time wearable robot adjustments based on biofeedback. Data from a set of activities, including walking, running, and squatting with the biopatch and exoskeleton, determines the relationship between metabolic costs and heart rate variability root mean square of successive differences (HRV-RMSSD) (R = −0.758). Collectively, the exoskeleton-integrated wearable system shows potential to develop a field-deployable exoskeleton platform that can measure wireless real-time physiological signals.

Asymmetric strategy for enhanced performance of flexible electroadhesive clutch

Flexible electroadhesive clutches with high shear stress and fast response working at low voltage are much desired in wearable electronics and robotic systems. Dielectric materials with opposite charge characteristics could maximize the clutch performance by taking advantages of the boosted electroadhesion between the two contact pads in an asymmetrically structured clutch. In this paper, asymmetrically structured electroadhesive clutches are proposed and reported for the first time. The asymmetric structured clutch exhibits a two-fold increment in the shear force but similar response time by simply reversing the electrode polarity. This work provides a new dimension to realize high-performance electroadhesive clutches based on an asymmetric strategy.

Influence of Input Features and EMG Type on Ankle Joint Torque Prediction with Support Vector Regression.

Reliable and accurate EMG-driven prediction of joint torques are instrumental in the control of wearable robotic systems. This study investigates how different EMG input features affect the machine learning algorithm-based prediction of ankle joint torque in isometric and dynamic conditions. High-density electromyography (HD-EMG) of five lower leg muscles were recorded during isometric contractions and dynamic tasks. Four datasets (HD-EMG, HD-EMG with reduced dimensionality, features extracted from HD-EMG with Convolutional Neural Network, and bipolar EMG) were created and used alone or in combination with joint kinematic information for the prediction of ankle joint torque using Support Vector Regression. The performance was evaluated under intra-session, inter-subject, and inter-session cases. All HD-EMG-derived datasets led to significantly more accurate isometric ankle torque prediction than the bipolar EMG datasets. The highest torque prediction accuracy for the dynamic tasks was achieved using bipolar EMG or HD-EMG with reduced dimensionality in combination with kinematic features. The findings of this study contribute to the knowledge allowing an informed selection of appropriate features for EMG-driven torque prediction.