Posts Tagged human-robot interaction

[ARTICLE] Physiological and kinematic effects of a soft exosuit on arm movements – Full Text

Abstract

Background

Soft wearable robots (exosuits), being lightweight, ergonomic and low power-demanding, are attractive for a variety of applications, ranging from strength augmentation in industrial scenarios, to medical assistance for people with motor impairments. Understanding how these devices affect the physiology and mechanics of human movements is fundamental for quantifying their benefits and drawbacks, assessing their suitability for different applications and guiding a continuous design refinement.

Methods

We present a novel wearable exosuit for assistance/augmentation of the elbow and introduce a controller that compensates for gravitational forces acting on the limb while allowing the suit to cooperatively move with its wearer. Eight healthy subjects wore the exosuit and performed elbow movements in two conditions: with assistance from the device (powered) and without assistance (unpowered). The test included a dynamic task, to evaluate the impact of the assistance on the kinematics and dynamics of human movement, and an isometric task, to assess its influence on the onset of muscular fatigue.

Results

Powered movements showed a low but significant degradation in accuracy and smoothness when compared to the unpowered ones. The degradation in kinematics was accompanied by an average reduction of 59.20±5.58% (mean ± standard error) of the biological torque and 64.8±7.66% drop in muscular effort when the exosuit assisted its wearer. Furthermore, an analysis of the electromyographic signals of the biceps brachii during the isometric task revealed that the exosuit delays the onset of muscular fatigue.

Conclusions

The study examined the effects of an exosuit on the characteristics of human movements. The suit supports most of the power needed to move and reduces the effort that the subject needs to exert to counteract gravity in a static posture, delaying the onset of muscular fatigue. We interpret the decline in kinematic performance as a technical limitation of the current device. This work suggests that a powered exosuit can be a good candidate for industrial and clinical applications, where task efficiency and hardware transparency are paramount.

Background

In the never-ending quest to push the boundaries of their motor performance, humans have designed a wealth of wearable robotic devices. In one of the earliest recorded attempts to do so, in 1967, Mosher aspired to create a symbiotic unit that would have the “…alacrity of man’s information and control system coupled with the machine’s power and ruggedness” [1]. His design of the Hardiman, although visionary, ran into fundamental technological limitations.

Advances in materials science, electronics and energy storage have since enabled an exponential growth of the field, with state-of-the-art exoskeletons arguably accomplishing Mosher’s vision [2]. Wearable robotic technology has been successful in augmenting human strength during locomotion [3], reducing the metabolic cost of human walking [45], restoring ambulatory capabilities to paraplegic patients [6], assisting in rehabilitating stroke patients [789], harvesting energy from human movements [10] and helping to study fundamental principles underlying human motor control [1112].

These feats were achieved with machines made of rigid links of metal and capable of accurately and precisely delivering high forces to their wearer. While this is undeniably an advantage, it comes at a cost: 1) a significant inertia, which affects both the kinematics of human movement and the power requirements of the device; 2) the need for the joints of the robot to be aligned with the biological joints [13], resulting in increased mechanical complexity and size [14]; 3) a strong cosmetic impact, shown to be linked with psychological health and well-being [15].

The recent introduction of soft materials to transmit forces and torques to the human body [16] has allowed to design wearable robotic devices on the other side of the spectrum: lightweight, low-profile and compliant machines that sacrifice accuracy and magnitude of assistance for the sake of portability and svelteness.

Soft exoskeletons, or exosuits, are clothing-like devices made of fabric or elastomers that wrap around a person’s limb and work in parallel with his/her muscles [1718]. Characteristic of exosuits is that they rely on the structural integrity of the human body to transfer reaction forces between body segments, rather than having their own frame, thus acting more like external muscles than an external skeleton. Their intrinsic compliance removes the need for alignment with the joints and their low-profile allows to wear them underneath everyday clothing.

Exosuits actively transmit power to the human body either using cables, moved by electric motors, or soft pneumatic actuators, embedded in the garment. The latter paradigm was probably among the first to be proposed [19] and has been explored to assist stroke patients during walking [20], to increase shoulder mobility in subjects with neuromuscular conditions [21], to help elbow movements [22] and for rehabilitation purposes to train and aid grasping [232425].

Cable-driven exosuits, instead, include a DC motor that transmits power to the suit using Bowden cables. This flexible transmission allows to locate the actuation stage where its additional weight has the least metabolic impact on its wearer. Using this paradigm to provide assistance to the lower limbs has resulted in unprecedented levels of walking economy in healthy subjects [26] and improved symmetry and efficiency of mobility in stroke patients [27]. Similar principles were used to provide active support to hip and knee extension, reducing activation of the gluteus maximus in sit-to-stand and stand-to-sit transitions [28].

Cable-driven exosuits seem to work particularly well for lower-limbs movements, where small bursts of well-timed assistance can have a big impact on the dynamics and metabolic cost of locomotion [29]. Yet, Park et al. have shown that they have the potential for assisting the upper-limbs in quasi-static movements too: using a tendon-driving mechanism, a textile interface and an elastic component they found a significant reduction in the activity of the deltoid muscle when supporting the weight of the arm [30].

Similar results were reported by Chiaradia et al., where a soft exosuit for the elbow was shown to reduce the activation of the biceps brachii muscle in dynamic movements [31], and by Khanh et al., where the same device was used to improve the range of motion of a patient suffering from bilateral brachial plexus injury [32].

While there is extensive work on the analysis of the effects of wearing a soft exosuit on the kinematics, energetics and muscular activation during walking [33], the authors are unaware of comparable studies on movements of the upper limbs, whose variety of volitional motions is fundamentally different from the rhythmic nature of walking.

Understanding how these devices affect the physiology and mechanics of human movements is fundamental for quantifying their benefits and drawbacks, assessing their suitability for different applications and guiding a continuous data-driven design refinement.

In this study we investigate the kinematic and physiological effects of wearing a cable-driven exosuit to support elbow movements. We hypothesize that the low inertia and soft nature of the exosuit will allow it to work in parallel with the user’s muscles, delaying the onset of fatigue while having little to no impact on movement kinematics.

We propose a variation of the design and controller presented in [3234] and introduce a controller that both detects the wearer’s intention, allowing the suit to quickly shadow the user’s movements, and compensates for gravitational forces acting on the limb, thus reducing the muscular effort required for holding a static posture. We collect kinematic, dynamic and myoelectric signals from subjects wearing the device, finding that the exosuit affects motion smoothness, significantly reduces muscular effort and delays the onset of fatigue. The analysis offers interesting insights on the viability of using this technology for human augmentation/assistance and medical purposes.

Methods

Exosuit design

An exosuit is a device consisting of a frame made of soft material that wraps around the human body and transmits forces to its wearer’s skeletal structure. In a cable-driven exosuit, artificial tendons are routed along a targeted joint and attached to anchor points on both of its sides. When the tendons are tensioned they deliver an assistive moment to the joint.

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Continue —>  Physiological and kinematic effects of a soft exosuit on arm movements | Journal of NeuroEngineering and Rehabilitation | Full Text

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[ARTICLE] Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation – Full Text

In the therapist-centered rehabilitation program, the experienced therapists can observe emotional changes of stroke patients and make corresponding decisions on their intervention strategies. Likewise, robotic-assisted stroke rehabilitation systems will be more appreciated if they can also perceive emotional states of the stroke patients and enhance their engagements by exploring emotion-based dynamic difficulty adjustments. Nevertheless, few research have addressed this issue. A two-phase pilot study with anxiety as the target emotion state was conducted in this article. In phase I, the motor performances and the physiological responses to the stroke subject’s anxiety with high, medium, and low intensities were statistically analyzed, and anxiety models with three intensities were offline developed using support vector machine–based classifiers. In phase II, anxiety-based closed-loop robot-aided training task adaptation and its impacts on patient–robot interaction engagements were explored. As a comparison, a performance-based robotic behavior adaptation was also implemented. Experimental results with 12 recruited stroke patients conducted on the Barrett WAMTM manipulator verified that the rehabilitation robot can implicitly recognize the anxiety intensities of the stroke survivors and the anxiety-based real-time robotic behavior adaptation shows more engagements in the human–robot interactions.

Task-oriented repetitive rehabilitation training is becoming the state-of-the-art therapy approach for poststroke patients. These therapy tasks are traditionally implemented by physical therapists. In recent years, there is an increasing interest in using robotic devices to help providing motor rehabilitation therapy.1Compared with the therapist-centered therapy, robot-assisted stroke rehabilitation can not only provide a variety of highly repetitive movements and training protocols for stroke patients, but also offer objective measurements of stroke patients’ functional improvements.

Stroke patients’ active engagements in rehabilitation training have been shown to be a very positive factor to the success of rehabilitation.2 Early rehabilitation robots are able to provide active assistance to stroke patients, but do not take into account individual properties, spontaneous intentions, or voluntary efforts of that particular person. These problems were addressed by integrating the patients into the sensorimotor control loop. By recognizing the patients’ active motor abilities or movement intentions, the human-in-the-loop rehabilitation robotic systems are able to optimize participation and support the patients only as little as needed.3 However, stroke patients’ active involvements in the existing rehabilitation robotic systems are mostly considered from biomechanical and bioelectrical viewpoints, where the patients’ active force/position signals4,5 or electrical activities of the brain and the muscles6,7 were recorded.

In the therapist-centered program, the therapists who work with the stroke patients can not only perceive the patients’ active motor involvements, but also continuously monitor the patients’ emotion changes in order to make appropriate decisions on their intervention strategies. The stroke patients are particularly vulnerable to anxiety and frustration, which requires to plan tasks at an appropriate level of difficulty. Likewise, robotic-assisted stroke rehabilitation training systems will be more appreciated if they can perceive the stroke patients’ emotion changes and make emotion-based dynamic difficulty adjustment. Offering insights into the patients’ emotional changes and adapting emotion-based behavioral interventions are known as another critical factor to successful stroke rehabilitation.8 Nonetheless, very few research on robot-assisted stroke rehabilitation are specifically addressed how to automatically recognize and respond to the emotion changes of the stroke survivors. One possible reason is that there are some difficulties in perceiving the stroke patient’s emotion states.

There are several modalities such as facial expression,9 vocal intonation,10 body gesture,11 and physiology12 that can be utilized to recognize the emotion states of individuals in human–robot/computer interaction. Nevertheless, the patients with chronic stroke are often characterized by dull facial expression, severe aphasia, and limb motor dysfunction. These vulnerabilities place limits on observational, conversational, and limb methodologies to recognize the stroke survivors’ emotional states. Physiology-based measurements are far more robust against these difficulties because they are noninvasive and further the psycho-physiological signals can be continuously available without the stroke patient’s active cooperation. Besides, evidences show that the transition from one emotion state to another is accompanied by dynamic shifts in indicators of autonomous and central nervous system activity.13,14

In this article, anxiety, which can be easily evoked by training tasks with different difficulties in clinical rehabilitation therapy, was chosen as the target emotion state. The primary focuses of the current research were firstly to offline evaluate anxiety with high, medium, and low intensities and then to carry out real-time anxiety-based robot-aided rehabilitation training task adaptation.

The block diagram of the anxiety detection and subsequent anxiety-based robot-aided training task adaptation system are shown in Figure 1. It consists of two consecutive phases: offline anxiety modeling (phase I) and online anxiety-based robot-aided training task adaptation (phase II). In phase I, the features, from the physiological and the motor performances recordings of the stroke subjects under anxiety with high, medium, and low intensities, were firstly extracted and then subjected to analysis of variance (ANOVA)-based statistical analyses to obtain the features with significant differences among three anxiety intensities. Anxiety with three intensities was further offline evaluated using support vector machine (SVM)–based anxiety classifier, in which the features with significant differences were adopted as inputs while the self-reported questionnaires as outputs. In phase II, robot-aided rehabilitation training tasks were online adapted to the recognized intensities of anxiety from the stroke subjects. Further, to demonstrate the effect of introducing anxiety of stroke patients into robot-assisted stroke rehabilitation, the impacts of anxiety-based robot-aided behavior adaptation on the stroke patient’s engagements were explored using the performance-based robot-aided training task adaptation as a comparison. The details on the enrolled subjects are given in the “Subjects” section while the experimental system setup is depicted in the “Experimental setup” section. “Phase I: Offline anxiety modeling” section demonstrates the offline modeling of the anxiety with high, medium, and low intensities (phase I). The online anxiety-based robot-aided rehabilitation training task adaptation and its impacts on the stroke patient’s engagements are shown in the “Phase II: Online anxiety-based robot-aided rehabilitation training task adaptation” section (phase II).

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Figure 1. Overview of the anxiety-based robot-aided training task adaptation system. SVM: support vector machine; EMG: electromyogram; ECG: electrocardiogram; SC: skin conductance; ANOVA: analysis of variance.

Subjects

The stroke patients, with upper extremity motor impairments and similar Brunnstrom Recovery Scale (BRS) evaluation scores, were recruited as representative of hemiparesis participants. Participants were excluded from the study if they had severe neurological disorder, senile dementia, or cognitive intact.15 Twelve stroke participants (mean age: 53.6 years, 7 males, 5 females, mean stroke time: 12.6 months, 6 right-sided hemiplegia, 6 left-sided hemiplegia, 10 stage-4 and 2 stage-3 BRS scores of upper extremity, and 9 stage-4 and 3 stage-3 BRS scores of hand) were recruited, and all were received motor rehabilitation therapy at the Rehabilitation Medicine Center of the Nanjing Tongren Hospital of China. Before the tasks began, ethical approval was obtained from the Medical Ethics Committee of the Nanjing Tongren Hospital of China, and all subjects were informed about the procedure and that they would be video-recorded and photo-taken during the experiment. All subjects gave written informed consent concerning the use of their video footage and questionnaire data for further analysis. Of the 12 stroke participants, one stroke subject (upper extremity and hand BRS scores were both stage-3) was not able to complete phase I experiments, and the rest also took part in phase II closed-loop experiments.

 

Continue —> Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation – Guozheng Xu, Xiang Gao, Lizheng Pan, Sheng Chen, Qiang Wang, Bo Zhu, Jinfei Li, 2018

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[Abstract] Modelling and control of a novel walker robot for post-stroke gait rehabilitation

Abstract:

In this paper, a novel walker robot is proposed for post-stroke gait rehabilitation. It consists of an omni-directional mobile platform which provides high mobility in horizontal motion, a linear motor that moves in vertical direction to support the body weight of a patient and a 6-axis force/torque sensor to measure interaction force/torque between the robot and patient. The proposed novel walker robot improves the mobility of pelvis so it can provide more natural gait patterns in rehabilitation. This paper analytically derives the kinematic and dynamic models of the novel walker robot. Simulation results are given to validate the proposed kinematic and dynamic models.

I. Introduction

Stroke is one of the leading causes of death overall the world [1]. According to a report from the American Heart Association, around 8 million population experience stroke onset every year worldwide [2]. It remains many sequalae including a pathological walking pattern. Impaired walking function refrains stroke survivors from not only activities of daily living but also social participation, which causes poststroke depression in stroke survivors [3]. Unfortunately, the depressed mood also negatively influences on the recovery of daily functions [4]–[6]. Moreover, decreased mobility is associated with other diseases such as obesity which leads to comorbidity then raise the possibility to get recurrent strokes [7], [8]. This might become a vicious circle and form a huge economic burden for governments [9].

via Modelling and control of a novel walker robot for post-stroke gait rehabilitation – IEEE Conference Publication

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[ARTICLE] A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual reality – Full Text

Motor rehabilitation strategies for treating motor deficits after stroke are based on the understanding of the neural plasticity. In recent years, various upper limb rehabilitation robots have been proposed for the stroke survivors to provide relearning of motor skills by stimulating the motor nerve. However, several aspects including costing, human–robot interaction, and effective stimulation of motor nerve still remain as major issues. In this article, a new upper limb rehabilitation training system named as motion intention-based virtual reality training system is developed to close the aforementioned issues. The system identifies the user’s motion intention via force sensors mounted on the rehabilitation robot to conduct therapeutic exercises and stimulates the user’s motor nerve by introducing the illusion of immersion in virtual reality environment. The illusion of immersion is developed by creating Virtual Exoskeleton Robot model which is driven by user’s motion intention and reflecting the motion states in real time. The users can be present to the training exercises by themselves and fully engage in the virtual reality environment, so that they can relax, move, and recreate motor neuro-pathways. As preliminary phase, six healthy subjects were invited to participate in experiments. The experimental results showed that the motion intention-based virtual reality training system is effective for the upper limb rehabilitation exoskeleton and the evaluations of the developed system showed a significant reduction of the performance error in the training task.

Stroke is a major cause of acquired physical disability in adults worldwide. Motor deficits affecting the upper limb are a common manifestation of stroke and greatly contribute to decreasing the individual’s functional performance.1 It is widely appreciated that motor rehabilitation after stroke plays an essential role in reducing the individual’s physical disability.2 The rehabilitation strategies for treating motor deficits after stroke are based on the understanding of the neural plasticity which is known by the phenomenon that the human brain changes itself in response to different types of experience through the reorganization of its neuronal connections.3 To exhibit the neural plasticity, motor relearning is the most important matter because it can produce changes in synapses, neurons, and neuronal networks within specific brain regions.4 Exoskeletons are robotic systems designed to work linked with parts (or the whole) of the human body. The robotic exoskeleton structure is always maintaining contact with the human operator’s limb. It can be suitably employed in robotic-assisted rehabilitation to assist the users to proceed relearning movement training exercises. And it can also make the process of upper limb rehabilitation repeatable, with objective estimation and decrease the dependence on specialized personnel availability.

About 30 existing robotic exoskeleton devices are reviewed by Proietti et al.5 As it has been mentioned, most publications in the field of exoskeletons focused only on mechatronic design of the devices, while we do believe a paramount aspect for robots potentiality lays on the control side. So the development of innovative and improved human–robot interaction control strategies will make a certain contribution to the upper limb rehabilitation assisted by the robotic exoskeleton devices.

The virtual reality (VR) technology has been proved useful in terms of motivating and challenging patients for longer training duration and cadence, modifying patient’s participating level, and updating subjects with their training performance.6 VR-based rehabilitation protocols may significantly improve the quality of rehabilitation by offering strong functional motivations to the patient who can therefore be more attentive to the movement to be performed. VR can provide an even more stimulating video game-like rehabilitation environment when integrated with force feedback devices, thus enhancing the quality of the rehabilitation.7

An upper limb force feedback exoskeleton for robotic assisted rehabilitation in VR is presented in Frisoli et al.8 A specific VR application focused on the reaching task was developed and evaluated in the system, but the system can’t provide adjustment when the reaching is far away too much. And little details are given to the control aspects of the robotic exoskeleton. An assistive control system with a special kinematic structure of an upper limb rehabilitation robot embedded with force/torque sensors is presented by Chen et al.9 A three-dimensional (3-D) GUI system for upper limb rehabilitation using electromyography and inertia measurement unit sensor feedback is developed by Alhajjar et al.10 It encourages the patients by recording the results and providing 3-D VR arm to simulate the arm movement during the exercise. A haptic device and an inertial sensor are used to implement rehabilitation tasks proposed by Song et al.,11 the system provides the vision through the monitor and force feedback through the haptic device. Gesture therapy was presented by Sucar et al.,12 a VR-based platform for rehabilitation of the upper limb was introduced. Similarly, the patients’ use of a home-based VR system portrayed by Standen et al.13 provides a low-cost VR system that translates movements of the hand, fingers, and thumb into game play which was designed to provide a flexible and motivating approach to increasing adherence to home-based rehabilitation. It is suitable for the patients with slight independence ability, which doesn’t have to be assisted by the robotic exoskeleton.

By considering all the aforementioned limitations, motion intention-based virtual reality training system (MIVRTS) is developed by integrating motion intention identification-based upper limb therapeutic exercises and the illusion of immersion in VR. The system identifies the user’s motion intention via force sensors mounted on the rehabilitation robot to conduct therapeutic exercises and stimulates the user’s motor nerve by introducing the illusion of immersion in VR environment. The illusion of immersion is developed by creating Virtual Exoskeleton Robot model which is driven by user’s motion intention and reflecting the motion states in real time.

The rest of the article is organized as follows. “The rehabilitation robotic exoskeleton” section presents the main features of the rehabilitation robotic exoskeleton system. An overview of the developed MIVRTS system employed in this study for the validation of the exoskeleton in upper limb rehabilitation is given in “MIVRTS system” section. In “Motion intention-based application” section, the motion intention identifying method is described and an application for rehabilitation exercises is developed. “Evaluation on six participants” section explains the experiment and evaluation results, followed by conclusion described in the final section.[…]

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Figure 1. 5-DOF upper limb rehabilitative exoskeleton robot. DOF: degrees of freedom.

Continue —-> A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual realityInternational Journal of Advanced Robotic Systems – Li Xing, Xiaofeng Wang, Jianhui Wang, 2017

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[Abstract] Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton

Abstract:

The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human-robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia.

via Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton – IEEE Journals & Magazine

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