Posts Tagged Robot sensing systems

[Abstract] A Review: Hand Exoskeleton Systems, Clinical Rehabilitation Practices, and Future Prospects

Abstract

Spinal cord injury (SCI) and stroke are pathologies that often result in the loss of/decrease in hand functionality. Hand function is a critical component of everyday life and therefore, a primary focus of clinical SCI/stroke rehabilitation is hand function recovery/improvement. In recent years, there has been a surge in hand exoskeleton research due to the potential for exoskeletons to improve clinical rehabilitation efficiency through automation. However, there is a disconnect between current clinical practice and exoskeleton research, resulting in a minority of hand exoskeletons being tested on individuals with SCI and/or stroke. This review article provides a comprehensive analysis and review of hand exoskeleton studies based on clinical rehabilitation practices to bridge the knowledge gap between clinical application and laboratory research. The key findings from this paper are: 1) current hand exoskeletons can successfully complete simple ADL tasks but lack the precision for fine motor control, 2) most hand exoskeletons exhibit a low number of degrees-of-freedom compared to the human hand, which may limit movement capability, 3) the majority of hand exoskeletons lack sensing capabilities, restricting viable control methods and user interfaces, and 4) inconsistent evaluation methods across studies do not allow for accurate performance assessment for different exoskeletons. The comparative assessments performed by this survey article show that there remain deficits between clinical hand rehabilitation practices and the current state of hand exoskeletons. By delineating these shortcomings, the information presented in this work can help inform future developments in the field of assistive and rehabilitative hand exoskeletons such that the gap between research and application may be closed.

Published in: IEEE Transactions on Medical Robotics and Bionics ( Early Access )

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[Abstract] Machine Learning in Robot Assisted Upper Limb Rehabilitation: A Focused Review

Abstract

Robot-assisted rehabilitation, which can provide repetitive, intensive and high-precision physics training, has a positive influence on motor function recovery of stroke patients. Current robots need to be more intelligent and more reliable in clinical practice. Machine learning algorithms (MLAs) are able to learn from data and predict future unknown conditions, which is of benefit to improve the effectiveness of robot-assisted rehabilitation. In this paper, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. Firstly, the current status of upper rehabilitation robots is presented. Then, we outline and analyze the designs and applications of MLAs for upper limb movement intention recognition, human-robot interaction control and quantitative assessment of motor function. Meanwhile, we discuss the future directions of MLAs-based robotic rehabilitation. This review article provides a summary of MLAs for robotic upper limb rehabilitation and contributes to the design and development of future advanced intelligent medical devices.

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[Abstract] Robotic Assisted Passive Wrist and Forearm Rehabilitation: Design of an Exoskeleton and Implementation

Abstract

An exoskeleton for human wrist and forearm rehabilitation has been designed and manufactured. Considering the torque values required for daily life activities, a structural analysis study has been presented. It has three degrees of freedom (DOF) which must be fitted to real human wrist and forearm. Anatomical motion ranges of human limbs have been taken into account during design. IMU has been used in order to get the kinematic values of the limbs and to evaluate the performance level of the therapy. Adapting a six DOF Denso robot to rehabilitation has been completed and experiments have been performed.

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[Abstract + References] Real-Time Evaluation of Hand Motor Function Recovery in Home Use Finger Rehabilitation Device Using Gaussian Process Regression – IEEE Conference Publication

Abstract:Continuous hand rehabilitation after discharge is important for hemiplegic patients to regain an independent finger movement. However, most patients cannot rehabilitate by themselves without therapists. For this problem, robotic rehabilitation has been investigated to support patients even at home. Most of the programs performed by these robots are focusing on the assistance for voluntary movement. However, the approach to the voluntary movement is not enough for regaining dexterous movement. Voluntary suppression of body parts that should not move is important. However, previous studies focusing on voluntary suppression are few. In this paper, we show a detailed program for voluntary suppression rehabilitation. The program is performed by our robotic finger rehabilitation device aiming at home use. In this program, a patient is requested to flex and extend an index finger independently. During moving, individual pressure sensors monitor the other fingers. If the device detects unnecessary movements such as compensatory movement at some fingers, the patient is notified that unnecessary movements are found there. The detection is based on 3σ range of healthy subject’s finger pressure data which was constructed by using Gaussian Process Regression. Through experiments with hemiplegic patients, we have shown that the frequency of deviation of patients’ data from 3σ range of healthy subjects decreases according to the degree of recovery.

References

1.C. L. Jones, F. Wang, R. Morrison, N. Sarkar and D. G. Kamper, “Design and Development of the Cable Actuated Finger Exoskeleton for Hand Rehabilitation Following Stroke”, IEEE/ASME Transactions on Mechatronics, vol. 19, no. 1, pp. 131-140, 2014.Show Context View Article Full Text: PDF (740KB) Google Scholar 2.D. Leonardis et al., “An EMG-Controlled Robotic Hand Exoskeleon for Bilateral Rehabilitation”, IEEE Transactions on Haptics, vol. 8, no. 2, pp. 140-151, 2015.Show Context View Article Full Text: PDF (1941KB) Google Scholar 3.S. Biggar and W. Yao, “Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 10, pp. 1071-1080, 2016.Show Context View Article Full Text: PDF (1597KB) Google Scholar 4.P. Polygerinos, K. C. Galloway, S. Sanan, M. Herman and C. J. Walsh, “EMG Controlled Soft Robotic Glove for Assistance during Activities of Daily Living”, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 55-60, 2015.Show Context View Article Full Text: PDF (2640KB) Google Scholar 5.I. Ben Abdallah, Y. Bouteraa and C. Rekik, “Design and Development of 3D Printed Myoelectric Robotic Exoskeleton for Hand Rehabilitation”, International Journal on Smart Sensing and Intelligent Systems, vol. 10, pp. 341-366, 2017.Show Context CrossRef  Google Scholar 6.K. Yamamoto, Y. Furudate, K. Chiba, Y. Ishida and S. Mikami, “Home Robotic Device for Rehabilitation of Finger Movement of Hemiplegia Patients”, 2017 IEEE/SICE International Symposium on System Integration (SII), pp. 300-305, 2017.Show Context View Article Full Text: PDF (1305KB) Google Scholar 7.C. D. Takahashi, L. Der-Yeghiaian, V. Le, R. R. Motiwala and S. C. Cramer, “Robot-based Hand Motor Therapy after Stroke”, Brain, vol. 131, no. Pt 2, pp. 425-437, 2008.Show Context CrossRef  Google Scholar 8.L. Dovat et al., A Technique to Train Finger Coordination and Independence after Stroke, Disability and Rehabilitation:Assistive Technology, vol. 5, no. 4, pp. 279-287, 2010.Show Context Google Scholar 9.Y. Furudate, N. Onuki, K. Chiba, Y. Ishida and S. Mikami, “Automated Evaluation of Hand Motor Function Recovery by Using Finger Pressure Sensing Device for Home Rehabilitation”, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 207-214, 2018.Show Context View Article Full Text: PDF (500KB) Google Scholar 10.Y. Furudate, N. Onuki, K. Chiba, Y. Ishida and S. Mikami, “Hand Motor Function Evaluation by Integrating Multi-Tasks Using Home Rehabilitation Device”, 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), pp. 272-274, 2020.Show Context View Article Full Text: PDF (2090KB) Google Scholar 

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[Abstract + References] Ultrasound Based Wrist Intent Recognition Method for Robotic-Assisted Stroke Rehabilitation – IEEE Conference Publication

Abstract

Rehabilitation is the most effective procedure for the stroke patients to regain their physical skills and improve activities of daily living. Recovering upper limb function after stroke requires intensive rehabilitation under the guidance of physical therapists, a costly and protracted process. Rehabilitation protocols that can be performed using robotic systems remotely at home with minimal assistance would decrease the cost of rehabilitation while reducing recovery time. Exoskeleton based robotic-assisted rehabilitation devices that can deliver high-intensity high-frequency training have been recently introduced. Such systems can be used independently without supervision of physical therapist, utilizes actuators and kinematic sensors to improve voluntary wrist movement of the stroke survivor while interacting with a goal-oriented interface. Although it has been clinically shown to improve functional abilities, motivation, and commitment to the rehabilitation programs, it requires users to have some degree of voluntary movement on their upper limb. This limits severely impaired stroke survivors who have very limited or even no motion on their limbs to benefit from these robotic-assisted systems. In this study, we present an ultrasound imaging-based method which can augment rehabilitation assistance capabilities of robotic systems by providing continuous motion intent recognition of the wrist. We implemented a recurrent neural network-based model which classifies extension, flexion and no movement wrist sequences with 87.10% accuracy.

References

Citation Map1.E. J. Benjamin, M. J. Blaha, S. E. Chiuve, M. Cushman, S. R. Das, R. Deo, et al., “Heart disease and stroke statistics-2017 update: a report from the American Heart Association”, Circulation, vol. 135, no. 10, pp. e146-e603, 2017.Show Context CrossRef  Google Scholar 2.R. Sacco, “Stroke”, Journal of the Neurological Sciences, vol. 381, pp. 12, 2017.Show Context CrossRef  Google Scholar 3.S. Balasubramanian, J. Klein and E. Burdet, “Robot-assisted rehabilitation of hand function”, Current opinion in neurology, vol. 23, no. 6, pp. 661-70, 2010.Show Context CrossRef  Google Scholar 4.M. Dam, P. Tonin, S. Casson, M. Ermani, G. Pizzolato, V. Iaia, et al., “The effects of long-term rehabilitation therapy on poststroke hemiplegic patients”, Stroke, vol. 24, no. 8, pp. 1186-91, 1993.Show Context CrossRef  Google Scholar 5.S. Housley, A. Garlow, K. Ducote, A. Howard, T. Thomas, D. Wu, et al., “Increasing access to cost effective home-based rehabilitation for rural veteran stroke survivors”, Austin journal of cerebrovascular disease & stroke, vol. 3, no. 2, pp. 1, 2016.Show Context Google Scholar 6.S. K. Lui and M. H. Nguyen, “Elderly stroke rehabilitation: overcoming the complications and its associated challenges”, Current gerontology and geriatrics research 2018, 2018.Show Context CrossRef  Google Scholar 7.B. Volpe, H. Krebs, N. Hogan, L. Edelsteinn, C. Diels and M. Aisen, “Robot training enhanced motor outcome in patients with stroke maintained over 3 years”, Neurology, vol. 53, no. 8, pp. 1874, 1999.Show Context CrossRef  Google Scholar 8.A. Waldner, C. Tomelleri and S. Hesse, “Transfer of scientific concepts to clinical practice: recent robot-assisted training studies”, Functional Neurology, vol. 24, no. 4, pp. 173, 2009.Show Context Google Scholar 9.A. C. Lo, P. D. Guarino, L. G. Richards, J. K. Haselkorn, G. F. Wittenberg, D. G. Federman, et al., “Robot-assisted therapy for long-term upper-limb impairment after stroke”, New England Journal of Medicine, vol. 362, no. 19, pp. 1772-83, 2010.Show Context CrossRef  Google Scholar 10.S. L. Wolf, K. Sahu, R. C. Bay, S. Buchanan, A. Reiss, S. Linder, et al., “The HAAPI (Home Arm Assistance Progression Initiative) trial: a novel robotics delivery approach in stroke rehabilitation”, Neurorehabilitation and neural repair, vol. 29, no. 10, pp. 958-68, 2015.Show Context CrossRef  Google Scholar 11.S. C. Cramer and R. J. Nudo, Brain repair after stroke, Cambridge University Press, 2010.Show Context CrossRef  Google Scholar 12.P. Maciejasz et al., “A survey on robotic devices for upper limb rehabilitation”, Journal of neuroengineering and rehabilitation, vol. 11.1, pp. 3, 2014.Show Context CrossRef  Google Scholar 13.S. Barreca, S. L. Wolf, S. Fasoli and R. Bohannon, “Treatment interventions for the paretic upper limb of stroke survivors: a critical review”, Neurorehabilitation and neural repair, vol. 17, no. 4, pp. 220-6, 2003.Show Context CrossRef  Google Scholar 14.S. N. Housley, D. Wu, K. Richards, S. Belagaje, M. Ghovanloo and A. J. Butler, “Improving upper extremity function and quality of life with a tongue driven exoskeleton: a pilot study quantifying stroke rehabilitation”, Stroke research and treatment, 2017.Show Context CrossRef  Google Scholar 15.G. Gurun, C. Tekes, J. Zahorian, T. Xu, S. Satir, M Karaman, et al., “Single-chip CMUT-on-CMOS front-end system for real-time volumetric IVUS and ICE imaging”, IEEE transactions on ultrasonics ferroelectrics and frequency control, vol. 61, no. 2, pp. 239-250, 2014.Show Context View Article Full Text: PDF (6532KB) Google Scholar 16.C. Castellini and D. S González, “Ultrasound imaging as a human-machine interface in a realistic scenario”, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1486-1492, 2013.Show Context View Article Full Text: PDF (1830KB) Google Scholar 17.J. Shi, J. Y. Guo, S. X. Hu and Y. P. Zheng, “Recognition of finger flexion motion from ultrasound image: a feasibility study”, Ultrasound in medicine & biology, vol. 38, no. 10, pp. 1695-1704, 2012.Show Context CrossRef  Google Scholar 18.S. Sikdar et al., “Novel method for predicting dexterous individual finger movements by imaging muscle activity using a wearable ultrasonic system”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22.1, pp. 69-76, 2013.Show Context Google Scholar 19.X. Yang et al., “Towards wearable A-mode ultrasound sensing for real-time finger motion recognition”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26.6, pp. 1199-1208, 2018.Show Context View Article Full Text: PDF (1994KB) Google Scholar 

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[Abstract] A Smart-Band Operated Wrist Rehabilitation Robot

Abstract

Many people in the world are increasingly suffering from stroke issues. Survivors often tend to suffer from hemiplegia or related conditions, in which some portion of their body may be rendered useless. The wrist is one such part. But this injury can be recovered by conventional rehabilitation processes like physical therapy. In this paper, a device for robot-assisted physical therapy is presented for wrist rehabilitation. It can overcome the lack of availability of physical therapists and reduce the cost incurred in long-term therapy. Also, it can provide accurate regular exercises without missing any step even in the absence of the therapist. These two DOF robotic devices can learn the physical exercise (i.e. wrist-based movements) from the trained therapist through an electronic smart-band. It can also replicate these exercises when the patient wears this device over his/her wrist. Here, an accelerometer sensor and a magnetometer sensor-based smart-band are used for recognizing the wrist motions like flexion, extension, abduction, and adduction. The objective of this preliminary work is to drive accurately all the motor actuators which are attached to the robot and calibrate the feedback sensor to reflect the movement of the smart-band. In the future, this robot can be used as a teleoperated rehabilitation device through an IoT platform.

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[Abstract] A Multi-Functional Lower-and Upper-Limb Stroke Rehabilitation Robot

Abstract

It is estimated that about 15 million people a year suffer from stroke worldwide, with 5 million stroke survivors experiencing permanent motor disability requiring therapeutic services. It has been shown that early involvement in rehabilitation therapies has a desirable effect on the long-term recovery of patients. There are, however, several challenges with the current state of delivering rehabilitation services, including limitations on the number of clinics, financial needs, and human resources. Robotic systems have been proposed in the literature to help with these challenges. However, most of the existing robotic systems are expensive, not-portable, and cannot be used for both upper-and lower-limb rehabilitation. This paper presents a 3-DOF robotic device that has been designed to deliver both upper-and lower-limb therapy and incorporates a novel mechanical safety mechanism. The device is capable of teleoperation which makes it particularly suitable for telerehabilitation in the current COVID-19 environment. The rehabilitation robot can deliver therapy in assistive and resistive modes to aid patients at all stages of recovery. In the assistive mode, the robot’s motion provides input to help the patient in completing the therapy task, while in the resistive mode, the robot opposes the motions generated by the patient thereby requiring additional muscle actuation. The robot has been tested by physiotherapists to assess its validity in a clinical setting, and by healthy participants to assess its functionality, safety, and engineering design. The study found that 80physiotherapists agreed the platform has the potential to improve patient outcomes.

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[Abstract + References] Iterative Learning Control of Gravity Compensation for Upper-Arm Robot-Assisted Rehabilitation

Abstract

Robot-assisted rehabilitation allows patients e.g. suffering from a stroke to practice without continuous supervision from a therapist. To activate neuroplasticity, the patient has to actively participate in the rehabilitation therapy and the robot should only provide as much assistance as required based on the patient’s needs and abilities. For this purpose, gravity compensation is a promising approach as simplifying movements enables the patient to increase the training’s intensity and number of repetitions. Thus, the aim of this paper is the application and implementation of an iterative learning control scheme to adjust the gravity compensation during therapy based on the patient’s abilities. For this purpose, a norm-optimal iterative learning control scheme and an optimization-based proportional-type iterative learning control algorithm are used. To validate and compare them, an experiment with a linear and a second one with a circular motion trajectory is done, while a slowly changing repetitive disturbance in form of an artificial force is applied to imitate the patient. In this case, the measured number of samples per cycle differs due to the underlying control scheme of the robot. For this reason, a mapping process based on the Dijkstra method is done. The results illustrate that both algorithms are robust against disturbances and yield good tracking performance. Thus, also other factors such as the computation effort of both algorithms should be considered in future research.

References

Source: https://ieeexplore.ieee.org/abstract/document/9143805

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[Abstract] A Wearable Hand Rehabilitation System with Soft Gloves

Abstract

Hand paralysis is one of the most common complications in stroke patients, which severely impacts their daily lives. This paper presents a wearable hand rehabilitation system that supports both mirror therapy and task-oriented therapy. A pair of gloves, i.e., a sensory glove and a motor glove, was designed and fabricated with a soft, flexible material, providing greater comfort and safety than conventional rigid rehabilitation devices. The sensory glove worn on the non-affected hand, which contains the force and flex sensors, is used to measure the gripping force and bending angle of each finger joint for motion detection. The motor glove, driven by micromotors, provides the affected hand with assisted driving-force to perform training tasks. Machine learning is employed to recognize the gestures from the sensory glove and to facilitate the rehabilitation tasks for the affected hand. The proposed system offers 16 kinds of finger gestures with an accuracy of 93.32%, allowing patients to conduct mirror therapy using fine-grained gestures for training a single finger and multiple fingers in coordination. A more sophisticated task-oriented rehabilitation with mirror therapy is also presented, which offers six types of training tasks with an average accuracy of 89.4% in real-time.

via A Wearable Hand Rehabilitation System with Soft Gloves – IEEE Journals & Magazine

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