Soft robotic devices have the potential to be widely used in daily lives for their inherent compliance and adaptability, which result in high safety under unexpected situations. System complexity and requirements are much lower, comparing with conventional rigid-bodied robotic devices, which also result in significantly lower costs. This paper presents a robotic glove by utilizing soft artificial muscles providing redundant degrees of freedom (DOFs) to generate both flexion and extension hand motions for daily grasping and manipulation tasks. Different with the existing devices, to minimize the weight applied to the user’s hands, pneumatic soft actuators were located on the fore arm and drive each finger via cable-transmission mechanisms. This actuation mechanism brings extra adaptability, motion smoothness, and user safety to the system. This design makes wearable robotic gloves more light-weight and user-friendly. Both theoretical and experimental analyses were conducted to explore the mechanical properties of pneumatic soft actuators. In addition, the fingertip trajectories were analyzed using Finite Element Methods, and a series of experiments were conducted evaluating both the technical and practical performances of the proposed glove.
Glove-type wearable robotic devices are developed to assist people with impaired hand functions both in their activities of daily living (ADLs) and in rehabilitation –. Most of such wearable robotic devices generate hand movements with linkage systems actuated by electrical motors which usually are heavy and inconvenient for using. Moreover, because of the human hand variation, most wearable robotic devices require customization in order to fulfill the geometrical fitting requirements between the exoskeleton device and the human hand joints. Approximating the high dexterity of human hands usually requires high complexity in both the mechanical and controller structures of the robotic systems, and hence also results in high costs for most users.
via A soft robotic glove for hand motion assistance – IEEE Conference Publication
This paper describes our efforts to employ the Microsoft Kinect as a low cost vision control system for the MIT-Skywalker, a robotic gait rehabilitation device. The Kinect enables an alternative markerless solution to control the MIT-Skywalker and allows a more user-friendly set-up. A study involving eight healthy subjects and two stroke survivors using the MIT-Skywalker device demonstrates the advantages and challenges of this new proposed approach.
Source: MIT-Skywalker: On the use of a markerless system – IEEE Xplore Document
Hybrid rehabilitation robotics combine neuro-prosthetic devices (close-loop functional electrical stimulation systems) and traditional robotic structures and actuators to explore better therapies and promote a more efficient motor function recovery or compensation. Although hybrid robotics and ankle neuroprostheses (NPs) have been widely developed over the last years, there are just few studies on the use of NPs to electrically control both ankle flexion and extension to promote ankle recovery and improved gait patterns in paretic limbs. The aim of this work is to develop an ankle NP specifically designed to work in the field of hybrid robotics. This article presents early steps towards this goal and makes a brief review about motor NPs and Functional Electrical Stimulation (FES) principles and most common devices used to aid the ankle functioning during the gait cycle. It also shows a current sources analysis done in this framework, in order to choose the best one for this intended application.
Source: Towards an ankle neuroprosthesis for hybrid robotics: Concepts and current sources for functional electrical stimulation – IEEE Xplore Document
The occurrence of strokes has been progressively increasing. Upper limb recovery after stroke is more difficult than lower limb. One of the rapidly expanding technologies in post-stroke rehabilitation is robot-aided therapy. The advantage of robots is that they are able to deliver highly repetitive therapeutic tasks with minimal supervision of a therapist. However, from the literature, the focus of robotic design in stroke rehabilitation has been technology-driven. Clinical and therapeutic requirements were not seriously considered in the design of rehabilitation robots. The purpose of this study was twofold: (1) demonstrate the missing elements of current robot-aided therapy; (2) identify design factors and opportunities of rehabilitation robots (in upper-limb training after stroke). In this study, we performed a literature review on articles relevant to rehabilitation robots in upper-limb training after stroke. We identified the design foci of current rehabilitation robots for upper limb stroke recovery. Using the therapeutic framework for stroke rehabilitation in occupational therapy, we highlighted design factors and opportunities of rehabilitation robots. The outcomes of this study benefit the robotics design community in the design of rehabilitation robots.
A robot is defined as a machine programmable to perform and modify tasks in response to changes in the environment . The benefits of robots are noticeable in productivity, safety, and in saving time and money. The advancement of robot technologies in the past decade caused the wide adoption of robots in our lives and in the society. For instance, in education, robots were implemented in undergraduate courses to teach core artificial intelligence concepts, e.g., algorithms for searching tree data structures . In agriculture, robotic milking systems (being able to reduce labor/operational costs) were installed to replace conventional milking that gave cows the freedom to be milked throughout the day . In healthcare, service robots were implemented to provide functional assistance for the elderly in home environments, e.g., bringing medication for the emergency and picking up heavy objects low on the ground .
Source: Design factors and opportunities of rehabilitation robots in upper-limb training after stroke – IEEE Xplore Document
Surface electromyographic (sEMG) signals is one most commonly used control source of exoskeleton for hand rehabilitation. Due to the characteristics of non-invasive, convenient collection and safety, sEMG can conform to the particularity of hemiplegic patients’ physiological state and directly reflect human’s neuromuscular activity. By way of collecting, analyzing and processing, sEMG signals corresponding to identify the target movement model would be translated into robot movement control instructions and input into hand rehabilitation exoskeleton controller. Then patients’ hand can be directed to achieve the realization of the similar action finally. In this paper, the recent key technologies of sEMG-based control for hand rehabilitation robots are reviewed. Then a summarization of controlling technology principle and methods of sEMG signal processing employed by the hand rehabilitation exoskeletons is presented. Finally suitable processing methods of multi-channel sEMG signals for the controlling of hand rehabilitation exoskeleton are put forward tentatively and the practical application in hand exoskeleton control is commented also.
Source: A survey on sEMG control strategies of wearable hand exoskeleton for rehabilitation – IEEE Xplore Document
Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of 7 major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested 6 movement directions and 4 force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.
Source: Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans – IEEE Xplore Document
Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user’s movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.
Stroke is a leading cause of adult disability around the world. A large number of stroke survivors are left with a unilateral arm or leg paralysis. After completing conventional rehabilitation therapy, a significant number of stroke survivors are left with limited reaching and grasping capabilities .
Source: Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient – IEEE Xplore Document
Estimation of fatigue is a required criteria in the field of physiology. The estimation of muscle fatigue and its development in the brain signals can provide a level of endurance among athletes and limits of a persons in doing physical tasks. In this paper a technique for detecting and estimating the fatigue development using regression parameters for EEG signals is discussed. The study of 14 subjects was undertaken and analysed for the fatigue development using Auto-Regression(AR) model. The behaviour of the error function obtained is analysed for the prediction of the stages and limits of muscle fatigue development.
Muscle fatigue is a phenomenon associated with the muscle contraction. It is understood as the reduction in the ability of maximal force generation by the muscle with time, during its stressing, as the muscle contraction keeps on increasing. The nervous system’s limitation to generate sustainable signals and the reduction of ability of muscle fiber to contract are two major factors contributing to fatigue development . Fatigue development limits the performance and capability of the individual in sports, long stretch driving conditions and in rigourous day to day activities. Hence a parameter that can estimate the fatigue levels and provide a break point for maximum fatigue can be useful for physiology and in other areas such as labour. People working under mines can be monitored for the fatigue break point and the overall productivity of such areas can be increased by proper analysis. The fatigue development in a person can be analysed via number of methods based on physiological changes. These include Electroencephalogram (EEG), Elec-tromyography(EMG), and Heart Rate Variability(HRV). Zadry et.al.  reported the increase in alpha band power level of EEG with time for fatigue development . Ali et.al. also reported increase in RMS values of different bands in EEG . Few studies measure brain activity in light repetitive task using EEG  to measure drowsiness or fatigue on drivers   and night work  . The EEG analysis for overall fatigue has been the focus of research, but research for specific muscle fatigue detection has been limited. The EEG based detection of fatigue has the advantage of quantitative based assessment. But, for real time application perspective faster computational power and signal processing methods are required. One of the challenges based on EEG based approach is the disturbances and contamination of the signal from eyes blinking action, muscle noise by movements and instrumental noises like line noise, electronic interferences . Another problem is imposed by the inter-variability and intra-variability in EEG dynamics accompanying loss of alertness .
Source: Fatigue detection and estimation using auto-regression analysis in EEG – IEEE Xplore Document
Hemispheric stroke survivors tend to have persistent motor impairments, with muscle weakness and muscle spasticity observed concurrently in the affected muscles.
The objective of this preliminary study was to identify whether impairment of muscle force transmission could contribute to weakness in spastic-paretic muscles of chronic stroke survivors. To characterize the efficiency of the transmission of muscle forces to the tendon, we activated biceps brachii muscle electrically by stimulating the musculocutaneous nerve with maximum current. The ratio between the elicited maximum twitch force amplitude and the maximum M-wave peak-peak amplitude was calculated as a measure of the efficiency of force transmission.
Based on the preliminary results of two stroke survivors, we show that the Force/M-wave ratio was reduced in the affected biceps brachii muscles in comparison with the contralateral muscles, indicating a potential impairment in the muscle force transmission in the affected muscles.
Our findings suggest that disrupted muscle force transmission to the tendon could contribute to weakness in spastic muscles of chronic stroke survivors.
Source: IEEE Xplore Document – Impairment of muscle force transmission in spastic-paretic muscles of stroke survivors
The torque generation capability of muscles often reduces during a functional electrical stimulation (FES) session due to the rapid onset of muscle fatigue. Hybrid rehabilitation systems that use FES and electric motor assist may overcome this issue.
The primary control challenge in such a system is how to allocate control inputs between electric motor and FES during muscle fatigue and muscle recovery. One strategy is to switch between FES and the electric motor by using an estimate of the muscle fatigue. This would allow the system to switch from using FES to using the electric motor when the muscle torque output has significantly decreased, then switch back to FES once the muscles have sufficiently recovered.
This paper uses a second order sliding mode controller cascaded with a feedback linearization controller for a switched, FES and electric motor, system. The second order sliding mode is achieved through the use of a variable-gain super-twisting algorithm. A Lyapunov stability analysis was used to prove asymptotic stability of the switched control system. Simulations of the developed controller on a hybrid knee extension model illustrate that prolonged knee movements can be elicited through the switched system.
Source: IEEE Xplore Abstract (Abstract) – Switching control of functional electrical stimulation and motor assist for muscle fatigue compensat…