Posts Tagged Force
Abstract
Previously, we reported a novel bilateral upper-limb rehabilitation system, an adaptive admittance controller and a related bilateral recovery strategy. In this study, we want to get a stronger evidence to verify the robustness of the proposed system, controller and recovery strategy as well as to further investigate the possibility of bilateral trainings for clinical applications. To this end, ten healthy subjects took part in a 60-minute experiment. Trajectories of robots and interaction force were recorded under the proposed bilateral recovery strategy which contained four exercise modes. For mode-l and mode-2, results showed that the trajectories of master and slave robots can catch the reference trajectory very well, and be changed with active interaction force applied by participants. For mode-3 and mode-4, participants finished tasks very well by drawing the ‘square-shaped’ trajectories through their own force. In conclusion, the experimental results were good enough to provide a strong and positive evidence for the proposed system and controller. Moreover, according to the feedbacks from participants, the bilateral recovery strategy can be treated as a new and interesting training as compared to the traditional unilateral training, and could be tested in clinical applications further.
I. Introduction
Compared to the traditional manual therapy, the robot involved therapy can alleviate labor-intensive aspects of conventional rehabilitation trainings, and provide precise passive/active repetitive trainings in a sufficiently long timeframe [1], [2]. In terms of upper-limb rehabilitation trainings, some robotic systems have been developed for bilateral exercises, and figured out a problem that performing most activities of daily living tasks with one-hand is awkward, difficult and time-consuming [2].
1. M. Cortese, M. Cempini, P. R. de Almeida Ribeiro, S. R. Soekadar, M. C. Carrozza, N. Vitiello, “A mechatronic system for robot-mediated hand telerehabilitation”, IEEE/ASME Transactions on Mechatronics, vol. 20, pp. 1753-1764, September 2015.
2. P. S. Lum, C. G. Burgar, P. C. Shor, “Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke”, Archives of physical medicine and rehabilitation, vol. 83, pp. 952-959, July 2002.
3. B. Sheng, Y. Zhang, W. Meng, C. Deng, S. Xie, “Bilateral robots for upper-limb stroke rehabilitation: State of the art and future prospects”, Medical engineering & physics, vol. 38, pp. 587-606, July 2016.
4. P. R. Culmer, A. E. Jackson, S. Makower, R. Richardson, J. A. Cozens, M. C. Levesley et al., “A control strategy for upper limb robotic rehabilitation with a dual robot system”, IEEE/ASME Transactions on Mechatronics, vol. 15, pp. 575-585, September 2010.
5. Z. Song, S. Guo, M. Pang, S. Zhang, N. Xiao, B. Gao et al., “Implementation of resistance training using an upper-limb exoskeleton rehabilitation device for elbow joint”, J. Med. Biol. Eng, vol. 34, pp. 188-196, 2014.
6. R. C. Loureiro, W. S. Harwin, K. Nagai, M. Johnson, “Advances in upper limb stroke rehabilitation: a technology push”, Medical & biological engineering & computing, vol. 49, pp. 1103, July 2011.
7. S. Hesse, C. Werner, M. Pohl, S. Rueckriem, J. Mehrholz, M. Lingnau, “Computerized arm training improves the motor control of the severely affected arm after stroke”, Stroke, vol. 36, pp. 1960-1966, August 2005.
8. C.-L. Yang, K.-C. Lin, H.-C. Chen, C.-Y. Wu, C.-L. Chen, “Pilot comparative study of unilateral and bilateral robot-assisted training on upper-extremity performance in patients with stroke”, American Journal of Occupational Therapy, vol. 66, pp. 198-206, March 2012.
9. E. Taub, G. Uswatte, R. Pidikiti, “Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation-a clinical review”, Journal of rehabilitation research and development, vol. 36, pp. 237, July 1999.
10. S. B. Brotzman, R. C. Manske, “Clinical Orthopaedic Rehabilitation E-Book: An Evidence-Based Approach-Expert Consult” in Elsevier Health Sciences, 2011.
11. K. C. Lin, Y. F. Chang, C. Y. Wu, Y. A. Chen, “Effects of constraint-induced therapy versus bilateral arm training on motor performance daily functions and quality of life in stroke survivors”, Neurorehabilitation and Neural Repair, vol. 23, pp. 441-448, December 2009.
12. J. Chen, N. Y. Yu, D. G. Huang, B. T. Ann, G. C. Chang, “Applying fuzzy logic to control cycling movement induced by functional electrical stimulation”, IEEE transactions on rehabilitation engineering, vol. 5, pp. 158-169, Jun 1997.
13. D. A. Winter, “Biomechanics and motor control of human movement” in John Wiley & Sons, 2009.
via A Bilateral Training System for Upper-limb Rehabilitation: A Follow-up Study – IEEE Conference Publication
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adaptive admittance controller, bilateral recovery strategy, Bilateral upper-limb rehabilitation system, clinical applications, Force, Manipulators, Protocols, Trajectory, UE, UL, Upper Extremity, upper limb
Abstract:
Objective: Loss of arm function is common in individuals with neurological damage, such as stroke or cerebral palsy. Robotic devices that address muscle strength deficits in a task-specific manner can assist in the recovery of arm function; however, current devices are typically large, bulky, and expensive to be routinely used in the clinic or at home. This study sought to address this issue by developing a portable planar passive rehabilitation robot, PaRRo. Methods: We designed PaRRo with a mechanical layout that incorporated kinematic redundancies to generate forces that directly oppose the user’s movement. Cost-efficient eddy current brakes were used to provide scalable resistances. The lengths of the robot’s linkages were optimized to have a reasonably large workspace for human planar reaching. We then performed theoretical analysis of the robot’s resistive force generating capacity and steerable workspace using MATLAB simulations. We also validated the device by having a subject move the end-effector along different paths at a set velocity using a metronome while simultaneously collecting surface electromyography (EMG) and end-effector forces felt by the user. Results: Results from simulation experiments indicated that the robot was capable of producing sufficient end-effector forces for functional resistance training. We also found the endpoint forces from the user were similar to the theoretical forces expected at any direction of motion. EMG results indicated that the device was capable of providing adjustable resistances based on subjects’ ability levels, as the muscle activation levels scaled with increasing magnet exposures. Conclusion: These results indicate that PaRRo is a feasible approach to provide functional resistance training to the muscles along the upper extremity. Significance: The proposed robotic device could provide a technological breakthrough that will make rehabilitation robots accessible for small outpatient rehabilitation centers and in-home therapy.
via A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training – IEEE Journals & Magazine
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Actuators, Brakes, Force, Immune system, Kinematics., Magnetic Braking, Passive, Portable, Reaching, Resists, robots, Training
Abstract:
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.
I. Introduction
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 [1]–[12]. 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
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Actuators, dexterous manipulators, Finite Element Analysis, Force, glove, Hand, minimisation, Muscles, pneumatic actuators, Soft robotics, thumb, Upper Extremity, upper limb
Abstract:
After leaving hospital, patients can carry out rehabilitation by using rehabilitation devices. However, they cannot evaluate the recovery by themselves. For this problem, a device which can both carry out the rehabilitation and evaluation of the degree of recovery is required. This paper proposes the method that quantifies the recovery of the paralysis of fingers to evaluate a patient automatically. A finger movement is measured by a pressure sensor on the rehabilitation device we have developed. A measured data is used as a time-series signal, and the recovery of the paralysis is quantified by calculating the dissimilarity between a healthy subject’s signal and the patient’s signal. The results of those dissimilarities are integrated over all finger to be used as a quantitative scale of recovery. From the experiment conducted with hemiplegia patients and healthy subjects, we could trace the process of the recovery by the proposed method.
Source: Quantification method of motor function recovery of fingers by using the device for home rehabilitation – IEEE Conference Publication
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Fingers, Force, Force measurement, home rehabilitation, Indexes, Keyboards, motor function recovery, thumb, Time series analysis, UE, UL, Upper Extremity, upper limb
Abstract:
Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.
Source: The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation – IEEE Xplore Document
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chronic, DC motors, eWrist, exoskeletons, Force, Gears, sEMG, Sensors, Stroke, surface electromyography, Training, wearable, Wrist
Abstract:
Stroke survivors who experience severe hemipare-sis often cannot completely recover the use of their hand and arm. Many of the rehabilitation devices currently available are designed to increase the functional recovery right after the stroke when, in some cases, biological restoring and plastic reorganization of the central nervous system can take place. However, this is not always the case. Even after extensive therapeutic interventions, the probability of regaining functional use of the impaired hand is low. In this respect, we present a novel robotic system composed of a supernumerary robotic finger and a wearable cutaneous finger interface. The supernumerary finger is used to help grasping objects while the wearable interface provides information about the forces exerted by the robotic finger on the object being held. We carried out two experiments, enrolling 16 healthy subjects and 2 chronic stroke patients. Results showed that using the supernumerary finger greatly improved the grasping capabilities of the subjects. Moreover, providing cutaneous feedback significantly improved the performance of the considered task and was preferred by all subjects.
Source: A soft robotic supernumerary finger and a wearable cutaneous finger interface to compensate the missing grasping capabilities in chronic stroke patients – IEEE Xplore Document
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Belts, chronic stroke, finger, Force, grasping, Robot kinematics, Robot sensing systems, Robotic, wearable
Background
The aim of this study was to examine the effect of the side of brain lesion on the ipsilesional hand function of stroke survivors.
Methods
Twenty-four chronic stroke survivors, equally allocated in 2 groups according to the side of brain lesion (right or left), and 12 sex- and age-matched healthy controls performed the Jebsen-Taylor Hand Function Test (JTHFT), the Nine-Hole Peg Test (9HPT), the maximum power grip strength (PwGSmax) test, and the maximum pinch grip strength (PnGSmax) test. Only the ipsilesional hand of the stroke survivors and both hands (left and right) of the controls were assessed.
Results
PwGS max and PnGS max were similar among all tested groups. Performances in JTHFT and 9HPT were affected by the brain injury. Individuals with left brain damage showed better performance in 9HPT than individuals with right brain damage, but performance in JTHFT was similar.
Conclusions
Individuals after a brain injury have the capacity to produce maximum strength preserved when using their ipsilesional hand. However, the dexterity of their hands and digits is affected, in particular for stroke individuals with right brain lesion.
Source: Assessment of the Ipsilesional Hand Function in Stroke Survivors: The Effect of Lesion Side – Journal of Stroke and Cerebrovascular Diseases
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brain lesion, Cerebrovascular accident, dexterity, Fingers, Force, ipsilesional, manipulation, Stroke, UE, UL, Upper Extremity, upper limb
Abstract:
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
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biofeedback, Biological control systems, Electromyography, Force, muscle synergies, Muscles, Robot sensing systems, robotic rehabilitation, Wrist
Abstract:
This paper reports on the development of a low-profile exoskeleton module to enable training of the fingers and thumb in grasp and release tasks. The design has been made as an add-on module for use with the ArmAssist arm rehabilitation system (Tecnalia, Spain). Variable-position springs and adjustable link lengths provide adaptability to fit a variety of users. Additive manufacturing has been utilized for the majority of components allowing easy modifications. A few structural components were machined from aluminum or steel to produce a functional prototype with sufficient strength for direct evaluation. The design includes independent and adjustable assistance in finger and thumb extension using various width elastic bands, and measurement of user grasp/release forces in finger flexion/extension, thumb flexion/extension, and thumb adduction/abduction using low-profile force sensitive resistors.
Source: IEEE Xplore Document – Design of a spring-assisted exoskeleton module for wrist and hand rehabilitation
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Exoskeleton, Force, Force measurement, grasping, Hand, Resistance, Sensors, spring-assisted, thumb, UE, UL, Upper Extremity, upper limb, Wrist
Abstract:
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
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chronic, Electrodes, Electromyography, Force, Force measurement, Muscles, Spasticity, Tendons