Wearable Robot Research Articles

Wearable robot helps man with Parkinson's disease to walk.

Wearable robot helps man with Parkinson's disease to walk.

Design and Validation of a Pancake Style Planetary Gearbox for an Eddy Current-Based Wearable Gait Training Robot.

Eddy current brakes have been recently used for functional resistance training in individuals with neurological and orthopaedic disorders. These devices consist of a gearbox, a conductive disc, and permanent magnets that can be moved relative to the disc to alter resistance. However, current devices use a commercial planetary gearbox with a tall profile that sticks out from the leg, which affects wearability. This is coupled with the large system inertia, which together impedes potential device transition to clinical and in-home use. In this study, we developed a low-profile, pancake-style planetary gearbox that greatly reduces the protrusion of the device from the leg. We performed a design analysis and optimization to minimize the thickness and inertia of the device while ensuring that it could withstand the maximum expected torque (50 Nm). We then performed human subjects experiments to examine the effectiveness of our new design for functional resistance training. The results indicated that all leg muscles showed a significant increase in activation during resisted conditions. There were also significant after-effects on medial hamstring activation. These results indicate that the new design is a feasible method for functional resistance training and may have a potential clinical value in gait rehabilitation.

Mechanical Structure Design of Wearable Assistive Robot Driven by ADAMS System

Mechanical Structure Design of Wearable Assistive Robot Driven by ADAMS System

A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors.

Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum transfer phase, the extension phase, and the stable standing phase based on the preprocessed acceleration and angular velocity data. We then employed a threshold method to recognize the initial sitting and the stable standing phases. Finally, we designed a novel CNN-BiLSTM-Attention algorithm to identify the three transition phases, namely, the flexion momentum phase, the momentum transfer phase, and the extension phase. Fifteen subjects were recruited to perform sit-to-stand transition experiments under a specific paradigm. A combination of the acceleration and angular velocity data features for the sit-to-stand transition phase identification were validated for the model performance improvements. The integration of the CNN, Bi-LSTM, and Attention modules demonstrated the reasonableness of the proposed algorithms. The experimental results showed that the proposed CNN-BiLSTM-Attention algorithm achieved the highest average classification accuracy of 99.5% for all five phases when compared to both traditional machine learning algorithms and deep learning algorithms on our customized dataset (STS-PD). The proposed sit-to-stand phase recognition algorithm could serve as a foundation for the control of wearable exoskeletons and is important for the further development of intelligent wearable exoskeleton rehabilitation robots.

Direct biomechanical manipulation of human gait stability: A systematic review.

People fall more often when their gait stability is reduced. Gait stability can be directly manipulated by exerting forces or moments onto a person, ranging from simple walking sticks to complex wearable robotics. A systematic review of the literature was performed to determine: What is the level of evidence for different types of mechanical manipulations on improving gait stability? The study was registered at PROSPERO (CRD42020180631). Databases Embase, Medline All, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar were searched. The final search was conducted on the 1st of December, 2022. The included studies contained mechanical devices that influence gait stability for both impaired and non-impaired subjects. Studies performed with prosthetic devices, passive orthoses, and analysing post-training effects were excluded. An adapted NIH quality assessment tool was used to assess the study quality and risk of bias. Studies were grouped based on the type of device, point of application, and direction of forces and moments. For each device type, a best-evidence synthesis was performed to quantify the level of evidence based on the type of validity of the reported outcome measures and the study quality assessment score. Impaired and non-impaired study participants were considered separately. From a total of 4701 papers, 53 were included in our analysis. For impaired subjects, indicative evidence was found for medio-lateral pelvis stabilisation for improving gait stability, while limited evidence was found for hip joint assistance and canes. For non-impaired subjects, moderate evidence was found for medio-lateral pelvis stabilisation and limited evidence for body weight support. For all other device types, either indicative or insufficient evidence was found for improving gait stability. Our findings also highlight the lack of consensus on outcome measures amongst studies of devices focused on manipulating gait.

Embodied Perception Interaction, and Cognition for Wearable Robotics: A Survey

Embodied Perception Interaction, and Cognition for Wearable Robotics: A Survey

Abduction/Adduction Assistance From Powered Hip Exoskeleton Enables Modulation of User Step Width During Walking.

As the first step to show its potential, the objective of this study was to investigate how human's step width reacted to hip exoskeleton's admittance control parameter changes during walking. Ten non-disabled individuals walked on a treadmill at a self-selected speed, while wearing our bilateral robotic hip exoskeleton. We used two equilibrium positions to define the direction of assistance. We studied the influence of multiple stiffness values in the admittance control on the participants' step width, step length, and electromyographic (EMG) activity of the gluteus medius. Step width were significantly modulated by the change of stiffness in exoskeleton control, while step length did not show significant changes. When the stiffness changed from zero to our studied stiffness values, the participants' step width started to modulate immediately. Within 4 consecutive heel strikes right after a stiffness change, the step width showed a significant change. Interestingly, EMG activity of the gluteus medius did not change significantly regardless the applied stiffness and powered direction. Tuning of stiffness in admittance control of a hip exoskeleton, acting in mediolateral direction, can be a viable way for controlling step width in normal walking. Unvaried gluteus medius activity indicates that the increase in step width were mainly caused by the assistive torque applied by the exoskeleton. Our study results pave a new way for future design and control of wearable robotics in enhancing mediolateral walking balance for various rehabilitation 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.

(Invited) Fabriations and Applications Using Liquid Metal Towards Stretchable Electronics

Highly deformable devices have many potential applications, including wearable electronics, robotics and health monitoring. Mechanically deformable devices and sensors can conformally cover electronic systems on curved or soft surfaces. Liquids deform more easily than solids, so sensors and actuators that utilise liquids trapped in soft templates as sensing components are ideal platforms for such applications. Such highly deformable electronics using ultra-flexible conductive materials are called stretchable electronics. Liquid metals (LMs) are one of the leading materials. In the last few years, liquid metals have steadily gained interest, especially in the field of flexible soft electronics and related applications. The term liquid metal has several definitions and many research efforts are underway to utilise liquid metals in various fields. The simplest definition can be given as 'a composite of low-melting alloys that retain their liquid state at room temperature, are easily deformable and have high electrical conductivity'. Here, an overview of liquid metals, new processing methods, sensors and actuators, batteries and smart devices are reported in focus.To date, we have adopted a patterned method using microfluidic channels with regard to processing methods for liquid metals such as EGaIn and Galinstan. Microfluidic channels are one of the most accurate and reliable method of producing electrode patterns among the three processing methods, although they are limited by the substrate material and pattern design for pattern fabrication. The creation of microfluidic channels follows the method used in Micro Electro Mechanical Systems (MEMS) and micro Total Analysis Systems (microTAS). In practice, the molds are fabricated by photolithography using SU-8 photoresist. Rubber materials such as polydimethylsiloxane (PDMS) are poured into the mold and allowed to cure. Microfluidic channels are fabricated by chemically bonding the flat PDMS with the PDMS to which the pattern has been transferred. We have succeeded in fabricating a two-dimensional pattern of liquid metal by the method described above. Furthermore, we have successfully fabricated microfluidic channels by 3D printing of rubber materials and constructed 3D circuits by using liquid metal. As liquid metal is a liquid material, processing methods centred on "printing" processing can be used, and the most suitable processing method can be selected according to the device and application.Physical sensors such as strain sensors and chemical sensors such as oxygen gas sensors have been reported using liquid metal wiring. By optimising the wiring of the liquid metal, capacitor structures and resistors can be fabricated. By encapsulating these in a flexible material, PDMS, it is possible to realize sensors that can measure displacements such as strain. Indeed, by optimising three sensor structures, we have reported a physical sensor capable of sensing three sensing modes: two-dimensional tension and vertical compression. However, taking into account the actual application in wearable devices for healthcare applications such as pulse rate, a detection sensitivity below 1 k Pa is required. The authors realized a high-sensitivity pressure sensor by using a Wheatstone bridge circuit structure for the liquid metal wiring in the PDMS to form a Diaphragm sensor. In fact, a pulse rate sensing device was realized based on the developed device. Furthermore, a PDMS glove capable of measuring 16 points simultaneously was realized by attaching the developed pressure sensor to a hand mold using 3D printing5).Such physical sensors are very easy to construct and can be realized with one type of liquid metal. While there are many different types of liquid metals as described above, liquid metals easily mix when they come into contact with each other. Therefore, the authors proposed a "liquid" state heterojunction using ionic liquids and liquid metals with extremely low vapor pressure. By using ionic liquids [EMIM][Otf], [EMIM][PF6] and [EMPYR][NTf2], sensors for temperature, humidity and oxygen gas were realized. Furthermore, with regard to their sensitivity, the detection sensitivity was 10 times higher than that of normal metal temperature and humidity sensors for temperature and humidity sensors. In this study, heterojunctions were realized using microfluidics. Due to the low Reynolds number of the microfluidics, the robustness of the liquid state heterojunction with respect to deformation was also very high due to the high viscosity effect, making the configuration suitable for stretchable sensors. In addition to electrodes and sensors, development of batteries and smart devices has also been initiated and will be reported in this presentation.

INTENTION TO USE WEARABLE ROBOTS FOR INDEPENDENT LIVING : THE ROLE OF CULTURE AND PERSUASIVE HEALTH COMMUNICATION

Abstract Soft wearable robots are emerging assistive technologies that can prolong independent living among older adults experiencing progressive muscle loss, such as sarcopenia. In order to fulfil the wish of aging-in-place and relieve caregiver burden, it is essential to increase wearable robots acceptance through persuasive health communication strategies. This study draws on the integration of the senior technology acceptance model, health communication, and cultural value orientation to predict the intention to use wearable robots among older adults. A 2 (message framing: gain- versus loss-framed) x 2 (temporal framing: distal- versus proximal-framed) randomized experiment was conducted with 154 older adults aged 50 to 74 years, using a self-administered cross-sectional from December 2022 to March 2023 in Hong Kong. A hierarchical multiple regression analysis was conducted with six blocks of variables. Demographic variables including age, gender, and sarcopenia potential were not significantly associated with intention to use. Horizontal individualism is significantly associated with intention to use (p=.011). Also, horizontal collectivism is significantly associated with intention to use (p=.018). Vertical collectivism interacts with persuasive health communication to predict intention to use among older adults (p=.041). The final model (F(12,141) = 3.699, p<.001, R2=.24) which included cultural value orientations showed significant improvement from the first model which only included age and gender. Overall, the final model accounted for 23.9% of the variance. This study is the first to predict the intention to use wearable robots among older adults and provide valuable insights on future policy development to promote aging-in-place, with the aid of wearable robots.

Development and Evaluation of Arm Lifting Assist Devices

Musculoskeletal disorders are common occupational diseases that have become a major social problem. Mechanization has been promoted as a solution to this problem. However, several tasks still require manual labor, such as fruit harvesting in orchards, making the introduction of machinery difficult in many cases. Recently, from the viewpoint of worker protection and ergonomics, various wearable robots for work support have attracted attention. In Europe and the US, there has been much development of arm-lifting assistive devices that support upward work while holding tools in the hands for industrial applications. However, most of the devices currently on the market are expensive compared to their assistive capabilities. Against this background, we developed three types of arm-lifting assistive devices with different concepts (an exoskeleton arm-lifting assistive device utilizing a gas spring, an exoskeleton arm-lifting assistive device utilizing McKibben-type artificial muscles, and an arm-lifting assistive suit utilizing rubber) to develop inexpensive, high-power devices. Furthermore, comparative verification of the assist effectiveness of each device was conducted.

RealTHASC—a cyber-physical XR testbed for AI-supported real-time human autonomous systems collaborations

Today’s research on human-robot teaming requires the ability to test artificial intelligence (AI) algorithms for perception and decision-making in complex real-world environments. Field experiments, also referred to as experiments “in the wild,” do not provide the level of detailed ground truth necessary for thorough performance comparisons and validation. Experiments on pre-recorded real-world data sets are also significantly limited in their usefulness because they do not allow researchers to test the effectiveness of active robot perception and control or decision strategies in the loop. Additionally, research on large human-robot teams requires tests and experiments that are too costly even for the industry and may result in considerable time losses when experiments go awry. The novel Real-Time Human Autonomous Systems Collaborations (RealTHASC) facility at Cornell University interfaces real and virtual robots and humans with photorealistic simulated environments by implementing new concepts for the seamless integration of wearable sensors, motion capture, physics-based simulations, robot hardware and virtual reality (VR). The result is an extended reality (XR) testbed by which real robots and humans in the laboratory are able to experience virtual worlds, inclusive of virtual agents, through real-time visual feedback and interaction. VR body tracking by DeepMotion is employed in conjunction with the OptiTrack motion capture system to transfer every human subject and robot in the real physical laboratory space into a synthetic virtual environment, thereby constructing corresponding human/robot avatars that not only mimic the behaviors of the real agents but also experience the virtual world through virtual sensors and transmit the sensor data back to the real human/robot agent, all in real time. New cross-domain synthetic environments are created in RealTHASC using Unreal Engine™, bridging the simulation-to-reality gap and allowing for the inclusion of underwater/ground/aerial autonomous vehicles, each equipped with a multi-modal sensor suite. The experimental capabilities offered by RealTHASC are demonstrated through three case studies showcasing mixed real/virtual human/robot interactions in diverse domains, leveraging and complementing the benefits of experimentation in simulation and in the real world.

Liquid Metal Functionalization Innovations in Wearables and Soft Robotics for Smart Healthcare Applications

AbstractSmart healthcare solutions are crucial for early diagnosis, real‐time health monitoring, and personalized therapy. Essential to these advancements are flexible wearable devices and medical robotics, which incorporate a range of elements, including soft sensors, therapeutic modules, human‐machine interfaces, actuators, and systems for communication, computation, and power. Liquid metal‐based materials are rapidly emerging as one of the key elements for these innovations. This review will delve into the latest breakthroughs in liquid metal (LM) functionalization innovation and the applications in healthcare‐related wearables and soft robotics. Firstly, techniques to modify LM are explored, emphasizing strategies to enhance properties such as electromechanical, thermal, electromagnetic, biochemical, self‐healing, and magnetic capabilities. Next, the authors shed light on contemporary wearable sensors, therapeutic on‐skin devices, and other flexible device innovations. They finally conclude by highlighting the challenges and charting the path forward for LM‐driven healthcare advances.

A Lightweight Series Elastic Actuator With Variable Stiffness: Design, Modeling, and Evaluation

This article proposes a lightweight variable stiffness actuator (LVSA) driven by a novel mechanism with four sliders on a shared crank (FS2C). The FS2C mechanism allows the LVSA to simultaneously regulate the preload of four springs using only one motor and hence achieves a wider-range continuous stiffness adaption with reduced weight. A cable transmission system is developed to remotely place motors and further reduce the influence of the LVSA on the mass distribution. A dynamics model is established to study the torque-deflection and the stiffness-deflection relations. Based on the model, a torque-stiffness controller is proposed. Experiments are carried out to validate the performance of the dynamics model, the controller, and the LVSA. The results indicate that the LVSA provides a range of stiffness from 0 to 988 Nm/rad with a weight of 0.412 kg, and the controller is accurate in adjusting the output torque and stiffness at relatively high speeds. The proposed actuator provides a solution for actuation systems that have to be lightweight with variable stiffness, such as wearable robotics and assistive exoskeletons.

Feasibility of a Wearable Soft Robot Triggered by Brain-Computer Interface for Stroke Upper Extremity Rehabilitation

Abstract Conventional robot designs have been applied as feedback stages in brain-computer interfaces (BCI) for stroke upper limb neurorehabilitation, showing promising results. Soft -robotic devices can be simpler and less expensive to manufacture, and provide ergonomic advantages over traditional designs, allowing to increase the efficacy and availableness of BCI systems for stroke neurorehabilitation. However, patients' degrees of control, neurophysiological activity, and system's usability with a BCI, using a soft robotic device as feedback, have not been assessed in stroke. For these reasons, a BCI system with a soft robotic feedback device was assessed in stroke patients. Fifty trials were acquired to setup the system, and another fifty trials were performed for evaluating patients' BCI control and cortical activity during movement intention (MI) and robotic feedback. User experience with the BCI was also assessed. Classification accuracy was in the range of 71.3% to 97.5%. Significant decrease in alpha power was observed during both motor intention and robotic feedback, but significant decrease in beta power was only observed during motor intention. BCI performance was high and in the range of reported BCI stroke interventions that used traditional robotics as feedback. Power decrease observed predominantly in alpha during soft robotic feedback was likely due to the eliciting of motor-related mechanisms. Quantification of user experience with the BCI implied that the system complexity is adequate for stroke patients. Therefore, a BCI system aimed at stroke neurorehabilitation can incorporate a soft robotic design as feedback and has potential for upper extremity interventions.

Dual-porous structured dielectric layer for soft capacitive pressure sensor with high sensitivity

Highly sensitive, soft pressure sensors are urgently required in wearable electronics and robotics applications. In this study, we fabricated a dual-porous structured elastomer using a simple and cost-effective method, in which yeast and sugar particles were introduced into Ecoflex solution. The combination of yeast fermentation and sugar particle dissolution resulted in a dual-porous elastomer consisting of both large closed pores and small open pores. The dual-porous elastomer was employed as the dielectric layer in a soft capacitive pressure sensor. The pressure sensor exhibited a sensitivity over six times higher than that of the sensor with a mono-porous elastomer-based dielectric layer in the pressure range of 1–10 kPa. The finite element simulation demonstrated that the sensitivity enhancement can be attributed to the significantly low stiffness and buckling load resulting from the presence of large closed pores. Finger-attached sensors and motion-monitoring systems demonstrated a wide range of potential applications in wearable electronics.

Finger joint aligned flat tube folding structure for robotic glove design

Pneumatic soft actuators have been widely considered the safest actuation technology for use in wearable rehabilitation robots. For soft robotic gloves, researchers commonly put soft extending or bending actuators on dorsal fingers to assist hand flexion. In this research, we propose a novel pre-folded flat tube actuator (PFTA) to assist finger flex into a pre-set bending angle or contact force. The PFTA has three folds, aligned with each of the finger joint. Different from other soft actuators, the PFTA exerts bending torque directly on each finger joint without large actuator deformation. The PFTA made of heat shrink flat tube has a small profile, with low cost, easy fabrication, and high safety. When actuated, the PFTA has the tendency to unfold as well, we call this effect as unfolding flat tube actuation. This effect is characterized by a range of bending angles and input air pressures in which four distinct response regimes were observed. They are defined as shearing, collapsing, creasing, and flattening regimes. Similarly, experimental characterization of PFTAs is also conducted to evaluate the level of joint flexion assistance based on which design guidelines for robotic gloves are recommended. Finally, we build PFTAs on a soft wearable glove and demonstrate their capability in assisting the grasping operations of various object shapes.

A DMP-Based Motion Generation Scheme for Robotic Mirror Therapy

Hemiparesis has become prevalent in recent years, and robot-assisted rehabilitation provides great therapeutic potential. Robotic mirror therapy is a promising approach for hemiplegic patients by transmitting the motion of the healthy limb (HL) to the impaired limb (IL), where the wearable robot assists the IL to mimic the HL's action to stimulate the active participation of the injured muscles. However, complete replication of the HL's movement trajectory without considering the IL's muscle strength is not satisfactory for rehabilitation. In this article, a motion generation scheme is proposed for robotic mirror therapy. The robot's movement trajectory is modeled with dynamic movement primitive (DMP), and the physical human–robot interaction is formulated as an impedance model and coupled with the DMP model. The subject's muscle strength is evaluated with skin surface electromyography signals and transferred to the robot's joint stiffness. In order to adapt the human–robot coupled DMP model parameters to varying subject performance, a reinforcement learning algorithm is designed to optimize the robot's movement trajectory and stiffness profile, where the training safety and rehabilitation improvement are both guaranteed. The proposed method is validated using a lower extremity rehabilitation robot, and experimental results demonstrate its feasibility and superiority.

A review of upper-limb soft exosuit

A rigid exoskeleton has been developed for decades, and its feasibility has been proven in many areas, such as rehabilitation. Unlike the rigid exoskeleton, the soft exosuit provides a new insight for wearable robotics development and has drawn much attention as the external muscles instead of exoskeletons, especially for supporting users activities of daily living (ADL) and human body augmentation. This paper reviews the upper-limb soft exosuit studies in the last three years, including the core technologies and the current challenges that need to be addressed. Then, the actuator designs were described, including motor-tendon unit, pneumatic artificial muscle, hydraulic artificial muscle, and textile-based actuation. Their advantages and disadvantages were given and the applications were listed. Also, as the other part of core technologies described in this paper, the controller design which contains low-level and high-level control was discussed. Finally, the challenges were listed, which could be the further directions of research.

A wearable system to assist impaired-neck patients: Design and evaluation.

Patients with neurological disorders, such as amyotrophic lateral sclerosis, Parkinson's disease, and cerebral palsy, often face challenges due to head-neck immobility. The conventional treatment approach involves using a neck collar to maintain an upright head position, but this can be cumbersome and restricts head-neck movements over prolonged periods. This study introduces a wearable robot capable of providing three anatomical head motions for training and assistance. The primary contributions of this research include the design of an optimized structure and the incorporation of human-robot interaction. Based on human head motion data, our primary focus centered on developing a robot capable of accommodating a significant range of neutral head movements. To ensure safety, impedance control was employed to facilitate human-robot interaction. A human study was conducted involving 10 healthy subjects who participated in an experiment to assess the robot's assistance capabilities. Passive and active modes were used to evaluate the robot's effectiveness, taking into account head-neck movement error and muscle activity levels. Surface electromyography signals (sEMG) were collected from the splenius capitis muscles during the experiment. The results demonstrated that the robot covered nearly 85% of the overall range of head rotations. Importantly, using the robot during rehabilitation led to reduced muscle activation, highlighting its potential for assisting individuals with post-stroke movement impairments.