Posts Tagged upper limb
[ARTICLE] Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control – Full Text
Modern technology has been improving, as is medical technology. Over the years, rehabilitation medicine is developing and growing. The use of rehabilitation robots to achieve the upper limb motor function of patients with hemiplegia has also become a popular research in academia. Under this background, this paper proposes an upper limb robot rehabilitation system based on Internet of Things remote control. The upper limb robotic rehabilitation system based on the Internet of Things in this paper is composed of upper computer and lower computer. Information is collected by pressure sensor. The transmission process is realized by STM32 controller, which is first transmitted to the upper computer, and then the information needs to be processed After processing, it sends control commands to the lower computer controller to control the motor drive of the rehabilitation robot, so as to realize the rehabilitation training of the patient. In order to verify the reliability of the system in this paper, this paper conducted a motion test and system dynamic performance test. The research results of this paper show that the passive motion accuracy of the system in this paper has reached more than 97%, and the active motion accuracy has reached more than 98%. In addition, the maximum speed response time of the upper limb rehabilitation robot system based on the remote control of the Internet of Things in this paper is 5.7ms. The amount of adjustment is 5.32%, and the dynamic performance is good. The research results of this paper show that the upper limb rehabilitation robot system based on the Internet of Things remote control in this paper has excellent performance, which can provide a certain reference value for the research of rehabilitation robot.
Science and technology and people’s living standards are gradually improving, whether it is China or other countries in the world, and these changes will bring about an aging population problem. In recent years, due to the impact of cardiovascular and cerebrovascular diseases, there have been some changes in middle-aged and elderly patients with hemiplegia. The number of patients has increased and the trend of becoming younger. At the same time, on the other hand, due to the rapid growth of the number of transportation vehicles, more and more The more people suffer from nervous system injuries or limb injuries due to traffic accidents . Strictly speaking, according to medical theory and clinical medicine, in addition to early surgical treatment and necessary medical care, correct and scientific rehabilitation education is also very important for the recovery and improvement of limb motor ability, but these patients have exercise Obstacles, can’t do rehabilitation training alone, and someone needs help, but in view of the fact that there are not enough medical staff in our country, these patients will be in an embarrassing situation. In this respect, the development of a remotely controlled upper limb rehabilitation robot is of great significance for solving the problem of unattended patients with hemiplegia.
Sanja Vukićević once designed a robust controller of a two-degree-of-freedom upper limb rehabilitation robot for the motion characteristics of rehabilitation training and the inherent properties of the robot, so that the robot can drive the precise trajectory of hemiplegic patients according to the given trajectory, ensuring Under the system dynamics model with zero error, the modeling error bounded error remains consistent and bounded, and the tracking error is zero. The simulation results of Sanja Vukićević show that the robust control strategy can make the system tracking error tend to under certain conditions Zero, has a good control effect, although Sanja Vukićević’s method improves the robustness of rehabilitation training robots, but the reliability has decreased , . Dobkin BH used the hemiplegic rehabilitation theory and upper limb physiological structure as the basis, combined with biological science, mechanical engineering, automatic control and other disciplines to design the upper limb functional rehabilitation robot. The control system of impedance control, and Simulink software was used to establish the simulation model of the control system, and the influence of the control parameters based on position impedance on the upper limb function control of rehabilitation robot was analyzed. The results of Dobkin B H show that the rehabilitation robot’s control effect on the upper limb function changes with the change of movement speed. The upper limb rehabilitation robot designed by Dobkin B H has good stability but its accuracy is lacking, and it needs to be improved , . Naranjo-Hernández David once proposed a new upper limb rehabilitation robot system based on virtual reality, which fully utilizes many advantages of robots participating in stroke upper limb rehabilitation. The system has the advantages of small size, light weight and rehabilitation interaction. Naranjo-Hernández David’s system is mainly composed of a haptic device called Phantom Premium, Upper Extremity Exoskeleton Rehabilitation Device (ULERD) and virtual reality environment. It has been experimentally proved that Naranjo-Hernández David’s method is accurate and convenient during the rehabilitation process However, the economy is not strong and needs to be strengthened , .
This article adopts the Internet of Things remote control technology and designs the upper limb rehabilitation robot system. In this paper, the relevant theory of the remote control of the Internet of Things is first elaborated, then from the perspective of human kinematics, the motion model of the upper limb rehabilitation robot is constructed, and finally, the upper limb rehabilitation robot system based on the Internet of Things remote control is designed and set The corresponding experiment was carried out to test the system. The test results show that the system in this paper has good accuracy and dynamic performance.SECTION II.
Internet of Things Remote Control
The so-called remote control technology refers to the technology that the Internet controls and manages remote devices to control and manage signals based on signals. Its software usually includes client-side and server-side programs. As the Internet of Things becomes more and more popular, remote control technology is also popularized. It can achieve the effect of unconventional remote control through IoT media , .
A. Internet of Things
The Internet of Things realizes the mutual exchange, mutual knowledge, and interactive information exchange between “machines and machines”. It can also be understood that through a variety of communication technologies, the Internet of Things is a very complex and diverse system technology.. According to the principles of information generation, transmission, processing and application, the Internet of Things can be divided into four levels: perception recognition layer, network construction layer, management service layer and integrated application layer , .
1) Perception Recognition Layer
What is the core technology of the Internet of Things? It is perception and recognition, so the perception recognition layer is very important for the Internet of Things. So let’s take a look at what the perceptual recognition layer includes. The level of perceptual recognition includes radio frequency identification, wireless sensors and automatic information production equipment. Not only that, but also includes a variety of intelligent information used to artificially produce electronic products. It can be said that as an emerging technology, wireless sensor networks mainly use different types of sensors to obtain large-scale, long-term, real-time information on environmental status and behavior patterns .
2) Network Building Layer
The main function of this layer is to connect lower-level data (perceived recognition-level data) to higher levels such as the Internet for its use. The Internet and next-generation Internet (including IPv6 and other technologies) are the core networks of the Internet of Things. Various wireless networks on the edge can provide network access services anytime and anywhere. The existing WIMAX technology is included in the scope of the wireless metropolitan area network, and its role is to provide high-speed data transmission services in the metro area (about 100 km). On the other hand, the wireless local area network also includes the WIFI that almost every household is currently trying. The use of WIFI is very wide. The main function is to provide network access services for users in a certain area (family, campus, restaurant, airport, etc.). Not only that, the wireless personal area network also includes Bluetooth, ZigBee and other communication protocols. These several things have a common feature, that is, low power consumption, low transmission rate, short distance, generally used for personal electronic product interconnection, industrial equipment Control and other fields. The various types of wireless networks listed above are suitable for different environments and work together to provide convenient network access so that the Internet of Things can be achieved .
3) Management Service Layer
By supporting high-performance computer technology and large-capacity storage, the management service level can efficiently and reliably organize large-scale data and provide an intelligent support platform for high-level industry applications. Storage is the first step in information processing. The database system and various mass storage technologies developed later, even including network storage (such as data centers), have now been widely used in information technology, finance, telecommunications, automation, etc. These industries. Faced with massive amounts of information, how to organize and search for effective data is a key issue. Therefore, the main feature of the management service layer is “wisdom”. Through rich and detailed data, mechanical learning, data mining, expert systems and other means, it serves the management ’s The function is increasingly powerful .
4) Comprehensive Application Layer
What was the original role of the Internet? It is used to achieve computer-to-computer communication, and then developed into a connection between users and people as the main body, and the times are changing. Now, it is moving towards the goal of connecting things-things-people. Not only that, along with this process, network applications have also undergone tremendous changes, from the initial transmission of files and emails with basic functions of data services to user-centric applications. In addition, the layers of the Internet of Things are relatively independent but closely connected. Below the integrated application layer, different technologies at the same layer are complementary and suitable for different environments, forming a complete set of response strategies for this level of technology, and at different levels, providing different technical compositions and combinations to Create a complete solution according to the requirements of the implementation .
Mobile communication network topology.
Wireless sensor network topology.
From Figure 1 and Figure 2 we can see the network topology of the mobile communication network and wireless sensor network. The sensor is the first basic link to realize the automatic monitoring function of the system.It is generally composed of sensitive components, conversion originals, conversion circuits and auxiliary power sources.It can convert the sensed information into electrical signals or other output forms according to certain rules. So as to transmit and process information .
[Abstract + References] Multi-modal Intent Recognition Method for the Soft Hand Rehabilitation Exoskeleton
Stroke has become the second most disabling disease in the world. Due to the intensive demand for physical therapists and the severe dependence on hospitals, the cost for the treatment of stroke patients is huge. As the most flexible limb of the human body, the hand faces more severe challenges, which has a much lower degree of recovery than the upper and lower limbs. In the face of these challenges, a new treatment, exoskeleton-based rehabilitation, has demonstrated new vitality. This paper proposes a novel design of the soft hand exoskeleton based on bionics and anatomy and the exoskeleton could help the users bend and extend their fingers, which would greatly improve the motor ability of stroke patients. Through the control of the six drive motors, the exoskeleton could achieve most of the hand’s freedom of training. At the same time, we propose a multi-modal intent recognition method based on machine vision and machine speech. Under specific rehabilitation training scenarios, both healthy subjects and patients could complete grasping tasks in the wearing of the exoskeleton, overcoming potential security risks caused by misidentification due to using the single-modal intent understanding method.
3. K. B. Lee, S. H. Lim, K. H. Kim, K. J. Kim, Y. R. Kim, W. N. Chang, et al., “Six-month functional recovery of stroke patients: a multi-time-point study”, International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation, vol. 38, no. 2, pp. 173, 2015. Show Context CrossRef Google Scholar
4. P. Polygerinos, S. Lyne, Z. Wang, L. F. Nicolini, B. Mosadegh, G. M. Whitesides, et al., “Towards a soft pneumatic glove for hand rehabilitation”, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1512-1517, 2013. Show Context View Article Full Text: PDF (1334KB) Google Scholar
5. J. Yi, X. Chen and Z. Wang, “A three-dimensional- printed soft robotic glove with enhanced ergonomics and force capability”, IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 242-248, 2017. Show Context View Article Full Text: PDF (676KB) Google Scholar
6. N. Ho, K. Tong, X. Hu, K. Fung, X. Wei, W. Rong, et al., “An emg-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation”, 2011 IEEE international conference on rehabilitation robotics, pp. 1-5, 2011. Show Context View Article Full Text: PDF (848KB) Google Scholar
7. S. Park, L. Weber, L. Bishop, J. Stein and M. Ciocarlie, “Design and development of effective transmission mechanisms on a tendon driven hand orthosis for stroke patients”, 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2281-2287, 2018. Show Context View Article Full Text: PDF (2666KB) Google Scholar
8. L. Gerez and M. Liarokapis, “An underactuated tendon-driven wearable exo-glove with a four-output differential mechanism”, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6224-6228, 2019. Show Context View Article Full Text: PDF (3125KB) Google Scholar
9. T. Bützer, J. Dittli, J. Lieber, H. J. van Hedel, A. Meyer-Heim, O. Lambercy, et al., “Pexo- a pediatric whole hand exoskeleton for grasping assistance in task-oriented training”, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 108-114, 2019. Show Context View Article Full Text: PDF (1216KB) Google Scholar
10. M. Mirakhorlo, N. Van Beek, M. Wesseling, H. Maas, H. Veeger and I. Jonkers, “A musculoskeletal model of the hand and wrist: model definition and evaluation”, Computer methods in biomechanics and biomedical engineering, vol. 21, no. 9, pp. 548-557, 2018. Show Context CrossRef Google Scholar
11. M. Suarez-Escobar and E. Rendon-Velez, “An overview of robotic/mechanical devices for post-stroke thumb rehabilitation”, Disability and Rehabilitation: Assistive Technology, vol. 13, no. 7, pp. 683-703, 2018. Show Context CrossRef Google Scholar
12. M. Li, Z. Liang, B. He, C.-G. Zhao, W. Yao, G. Xu, et al., “Attention-controlled assistive wrist rehabilitation using a low-cost eeg sensor”, IEEE Sensors Journal, vol. 19, no. 15, pp. 6497-6507, 2019. Show Context View Article Full Text: PDF (4401KB) Google Scholar
13. D. Huggins-Daines, M. Kumar, A. Chan, A. W. Black, M. Ravishankar and A. I. Rudnicky, “Pocketsphinx: A free real-time continuous speech recognition system for hand-held devices”, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 1, pp. I-I, 2006. Show Context View Article Full Text: PDF (89KB) Google Scholar
14. J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You only look once: Unified real-time object detection”, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016. Show Context View Article Full Text: PDF (1742KB) Google Scholar
[Abstract] Motiv’Handed, a New Gamified Approach for Home-Based Hand Rehabilitation for Post-stroke Hemiparetic Patients – Conference paper
This document summarizes a master thesis project trying to bring a new solution to hemiplegia rehabilitation, one of the numerous consequences of strokes. A hemiplegic patients observe paralysis on one side of their body, and as so, loses autonomy and their quality of life decreases. In this study, we decided to only focus on the hand rehabilitation aspect. However, there is a clear tendency in stroke patients to stop training regularly when returning home from the hospital and the first part of their rehabilitation is over. They often experience demotivation, having the feeling that they will never get back to a fully autonomous person ever again and tend to put their training aside, especially when they do not see clear and visible results anymore. This is also due to the supervised training becoming sparser. All of this results in patients stagnating or even worse, regressing. Thus, we decided to offer a motivating solution for hand rehabilitation at home through gamification.
- 1.Stroke Paralysis. Portea. https://www.portea.com/physiotherapy/stroke-paralysis#section_1. Accessed 15 June 2020
- 2.Recovering from Hand Weakness after Stroke. Saebo. https://www.saebo.com/stroke-hand-weakness-recovery/. Accessed 15 June 2020
- 3.UHMA, a new solution for post-stroke home-based hand rehabilitation for patient with hemiparese, Duval–Dachary Sarah, Master thesis (2019)Google Scholar
- 4.Motiv’Handed, a new home-based hand rehabilitation device for post-stroke hemiparetic patients, Chevalier–Lancioni Jean-Philippe, Master Thesis (2020)Google Scholar
- 5.WIM, Jenny Holmsten website. https://www.jennyholmsten.com/wim. Accessed 15 June 2020
- 6.Carneiro, F., Tavares, R., Rodrigues, J., Abreu, P., Restivo, M.: A gamified approach for hand rehabilitation device. Int. J. OnlineGoogle Scholar
- 7.Engineering (iJOE), January 2018. Virtual reality for therapeutic purposes in stroke: A systematic review. S. Viñas-Diza, M. Sobrido-Prieto. s.l. : Elsevier España, S.L.U (2015)Google Scholar
[ARTICLE] Virtual reality-based treatment for regaining upper extremity function induces cortex grey matter changes in persons with acquired brain injury – Full Text
Individuals with acquired brain injuries (ABI) are in need of neurorehabilitation and neurorepair. Virtual anatomical interactivity (VAI) presents a digital game-like format in which ABI survivors with upper limb paresis use an unaffected limb to control a standard input device and a commonplace computer mouse to control virtual limb movements and tasks in a virtual world.
In a prospective cohort study, 35 ambulatory survivors of ABI (25/71% stroke, 10/29% traumatic brain injury) were enrolled. The subjects were divided into three groups: group A received VAI therapy only, group B received VAI and physical/occupational therapy (P/OT), and group C received P/OT only. Motor skills were evaluated by muscle strength (hand key pinch strength, grasp, and three-jaw chuck pinch) and active range of motion (AROM) of the shoulder, elbow, and wrist. Changes were analyzed by ANOVA, ANCOVA, and one-tailed Pearson correlation analysis. MRI data was acquired for group A, and volumetric changes in grey matter were analyzed using voxel-based morphometry (VBM) and correlated with quantified motor skills.
AROM of the shoulder, elbow, and wrist improved in all three groups. VBM revealed grey matter increases in five brain areas: the tail of the hippocampus, the left caudate, the rostral cingulate zone, the depth of the central sulcus, and the visual cortex. A positive correlation between the grey matter volumes in three cortical regions (motor and premotor and supplementary motor areas) and motor test results (power and AROM) was detected.
Our findings suggest that the VAI rehabilitation program significantly improved motor function and skills in the affected upper extremities of subjects with acquired brain injuries. Significant increases in grey matter volume in the motor and premotor regions of affected hemisphere and correlations of motor skills and volume in nonaffected brain regions were present, suggesting marked changes in structural brain plasticity.
Neurological disorders, including acquired brain injuries (ABIs) are important causes of disability and death worldwide [1, 2]. Although age-standardized mortality rates for ischemic and hemorrhagic strokes have decreased in the past two decades, the absolute number of stroke survivors is increasing, with most of the burden in low- and middle-income countries . Another major issue is that trends toward increasing stroke incidence at younger ages has been observed . Moreover, this type of ABI is the leading cause of long-term disability in the United States, with an estimated incidence of 795,000 strokes yearly .
In more than 80% of stroke survivors, impairments are seen in at least one of the upper limbs. Six months after a stroke, 38% of patients recover some dexterity in the paretic arm, though only 12% recover substantial function even in spite of having received physical/occupational therapy (P/OT) . Only a few survivors are able to regain some useful function of the upper limb. Failing to achieve useful function has highly negative impacts on the performance of daily living activities [6, 7]. Regaining control and improving upper limb motor function after ABIs are therefore crucial goals of motor system rehabilitation. In left-sided limb impairment, neglect syndrome can contribute to a worsened clinical state, making the alleviation of symptoms even more difficult to achieve. Mirror therapy has been reported as a promising approach to improve neglect symptoms [8, 9].
MRI has been used to track changes in brain connectivity related to rehabilitation , and several studies of healthy individuals playing off-the-shelf video games have demonstrated changes in the human brain resulting from interactions in a virtual world (VW) [11, 12]. Furthermore, playing video games results in brain changes associated with regaining improved, purposeful physical movements [13, 14]. The socio-cultural relevance of virtual reality (VR) and VW applications lies, more generally, in the fact that these technologies offer interactive environments to users. These interactive environments are actually present in the users’ experiences while less so in the world they share as biological creatures . The way in which we engage with VWs allows for rehabilitation exercises and activities that feel similar to their actual physical world counterparts . In the past two decades, researchers have demonstrated the potential for the interactive experiences of VWs to provide engaging, motivating, less physically demanding, and effective environments for ABI rehabilitation [9, 16,17,18].
One of the suitable rehabilitation methods seems to be exercises and tasks in VW called virtual anatomical interactivity (VAI) . This method provides sensory stimulation / afferent feedback and allows the independent control of an anatomically realistic virtual upper extremity capable of simulating human movements with a true range of motion. ABI survivors are able to relearn purposeful physical movements and regain movement in their disabled upper extremities . Contrary to conventional therapy, which exercises impaired upper limbs to improve limb movement, the general VAI hypothesis is that brain exercises alone (or combined with traditional therapy) may positively influence neuroplastic functions. In the VW, subjects can move their virtual impaired limbs using their healthy hands, meaning simulated physical movements are survivor-authored. Virtual visuomotor feedback may help regain functional connectivity between the brain and the impaired limb, therefore also regaining voluntary control of the limb.
The aim of the study was to test if the shoulder, elbow, and wrist movement; hand pinch strength; and grip strength of the paretic side improved through the use of VAI exclusively or combined with P/OT for upper extremities and how these approaches improved functional outcomes measured by the Action Reach Arm Test . The relationship between changes in abilities to control upper extremities and volumetric changes in cortex grey matter measured by VBM and using MRI was also explored.[…]
[Abstract] Experiences of augmented arm rehabilitation including supported self-management after stroke: a qualitative investigation
To explore the experiences of stroke survivors and their carers of augmented arm rehabilitation including supported self-management in terms of its acceptability, appropriateness and relevance.
A qualitative design, nested within a larger, multi-centre randomized controlled feasibility trial that compared augmented arm rehabilitation starting at three or nine weeks after stroke, with usual care. Semi-structured interviews were conducted with participants in both augmented arm rehabilitation groups. Normalization Process Theory was used to inform the topic guide and map the findings. Framework analysis was applied.
Interviews were conducted in stroke survivors’ homes, at Glasgow Caledonian University and in hospital.
17 stroke survivors and five carers were interviewed after completion of augmented arm rehabilitation.
Evidence-based augmented arm rehabilitation (27 additional hours over six weeks), including therapist-led sessions and supported self-management.
Three main themes were identified: (1) acceptability of the intervention (2) supported self-management and (3) coping with the intervention. All stroke survivors coped well with the intensity of the augmented arm rehabilitation programme. The majority of stroke survivors engaged in supported self-management and implemented activities into their daily routine. However, the findings suggest that some stroke survivors (male >70 years) had difficulties with self-management, needing a higher level of support.
Augmented arm rehabilitation commencing within nine weeks post stroke was reported to be well tolerated. The findings suggested that supported self-management seemed acceptable and appropriate to those who saw the relevance of the rehabilitation activities for their daily lives, and embedded them into their daily routines.
[ARTICLE] Evaluation of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after Stroke trial: descriptive analysis of intervention fidelity, goal selection and goal achievement – Full Text
To report the fidelity of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after stroke (RATULS) randomized controlled trial, the types of goals selected and the proportion of goals achieved.
Descriptive analysis of data on fidelity, goal selection and achievement from an intervention group within a randomized controlled trial.
Out-patient stroke rehabilitation within four UK NHS centres.
259 participants with moderate-severe upper limb activity limitation (Action Research Arm Test 0–39) between one week and five years post first stroke.
The enhanced upper limb therapy programme aimed to provide 36 one-hour sessions, including 45 minutes of face-to-face therapy focusing on personal goals, over 12 weeks.
7877/9324 (84%) sessions were attended; a median of 34 [IQR 29–36] per participant. A median of 127 [IQR 70–190] repetitions were achieved per participant per session attended. Based upon the Canadian Occupational Performance Measure, goal categories were: self-care 1449/2664 (54%); productivity 374/2664 (14%); leisure 180/2664 (7%) and ‘other’ 661/2664 (25%). For the 2051/2664 goals for which data were available, 1287 (51%) were achieved, ranging between 27% by participants more than 12 months post stroke with baseline Action Research Arm Test scores 0–7, and 88% by those less than three months after stroke with scores 8–19.
Intervention fidelity was high. Goals relating to self-care were most commonly selected. The proportion of goals achieved varied, depending on time post stroke and baseline arm activity limitation.
Up to 80% of stroke survivors have difficulties using their affected arm in daily activities,1 which often persist in the longer term, impacting on the ability to engage social roles and on autonomy.2 There is a need for further high quality evidence to support interventions to improve arm function after stroke.1,3,4 Repetitive functional task training has shown promise for improving arm function,3,5 and therefore further trials of this type of intervention are particularly important. The Robot-Assisted Training for the Upper Limb after Stroke (RATULS) randomized controlled trial, the largest of its kind to date (n = 770), was published recently.6 Participants were randomized to receive robot-assisted training, an enhanced upper limb therapy programme (where repetitive functional task practice focused on personal goals), or usual care.6 There was little evidence of a difference in the primary outcome of arm activity limitation (i.e. success in attaining pre-specified improvement in the Action Research Arm Test7,8 score at three months) between randomization groups. However, participants who were randomized to receive the enhanced upper limb therapy programme performed significantly better in a number of secondary outcomes when compared to those who received usual care. Clinically important benefits at the end of the three month intervention period were observed in measures of impairment (Fugl-Meyer Assessment Motor Score),8,9 activities of daily living and mobility (Stroke Impact Scale).10 Additionally, there were statistically significant improvements which were not considered clinically important, as the confidence intervals did not include values that are currently deemed to be Minimum Clinically Important Differences. These statistically significant improvements were in measures of arm function (Action Research Arm Test), hand function (Stroke Impact Scale),10 and activities of daily living (Barthel Activity of Daily Living Index)11 – with the latter continuing to 6 months follow-up. Participants randomized to receive the enhanced upper limb therapy programme also performed significantly better than those randomized to receive robot-assisted training in measures of activities of daily living at three months (Stroke Impact Scale10 and Barthel Index11) but these improvements also did not reach the threshold for being considered clinically important.6
It is important that the development and fidelity of interventions are fully reported to enable the results of a trial to be interpreted, and for the intervention to be replicable in routine clinical practice or future research. However, stroke rehabilitation trials often fall short in terms of reporting these aspects.12,13 The development and description of the enhanced upper limb therapy programme followed the Template for Intervention Description and Replication (TIDieR) framework,12 and the planned delivery of the intervention (TIDieR items 1–11) has been reported.14 The aim of this paper is to report the intervention fidelity (TIDieR item 12) and a descriptive analysis of the types of personal goals selected and the proportion achieved.[…]
[Abstract] Sensory interventions on motor function, activities of daily living, and spasticity of the upper limb in people with stroke: A randomized clinical trial
• Sensory function after stroke is a prognostic factor in the achievement of functional performance.
• Sensory stimulation can be helpful technique in the chronic phase of cerebrovascular accident.
• Motor function, ADL, and spasticity can be improved through sensory stimulation.
Stroke is the second cause of death around the world. Motor and sensory problems are common complications of the stroke. These defects in the upper limb cause reduced use of the affected limb and consequently a decrease in the quality of life.
Purpose of the Study
The purpose of this study was to examine the effect of exteroceptive and proprioceptive stimulations on motor function, spasticity of the upper limb, and activities of daily living in people who have had stroke.
Sixty people with chronic stroke selected by convenience sampling. Before the intervention, Modified Ashworth Scale, Fugl-Meyer assessment of Motor Recovery after Stroke, and Barthel Index were measured and then the intervention phase was started. Exteroceptive and proprioceptive sensory stimulations were performed for 6 weeks. Independent t-test was used to compare groups.
The intervention group made improvement in motor function (P = .0001, Cohen’s d = 2.14), activities of daily living of upper limb (P = .0001, Cohen’s d = 1.32), and spasticity (P = .002, Cohen’s d = −0.94).
Motor function and activities of daily living and spasticity of the upper limb can be improved through exteroceptive and proprioceptive stimulations. In this study, this type of intervention had the most impact on motor function compared with the rest.
Exteroceptive and proprioceptive stimulations in upper limb can be used in chronic phase of stroke. Improvement in motor function and activities of daily living and reducing spasticity are the results of these stimulations.
• Music-based interventions integrate most of the principles of motor training and multimodal stimulation.
• The use of music in rehabilitation can improve motor and cognitive functions of subacute and chronic stroke patients.
• Music-based interventions lead to better mood and quality of life in stroke patients than conventional approaches.
• Future studies should better address methodological aspects to improve the level of evidence of these interventions.
Music-based interventions have emerged as a promising tool in stroke motor rehabilitation as they integrate most of the principles of motor training and multimodal stimulation.
This paper aims to review the use of music in the rehabilitation of upper extremity motor function after stroke. First, we review the evidence supporting current music-based interventions including Music-supported Therapy, Music glove, group music therapy, Rhythm- and music-based intervention, and Musical sonification. Next, we describe the mechanisms that may be responsible for the effectiveness of these interventions, focusing on motor learning aspects, how multimodal stimulation may boost motor performance, and emotional and motivational aspects related to music.
Then, we discuss methodological concerns in music therapy research related to modifications of therapy protocols, evaluation of patients and study designs. Finally, we highlight clinical considerations for the implementation of music-based interventions in clinical settings
[ARTICLE] Automated functional electrical stimulation training system for upper-limb function recovery in poststroke patients – Full Text
• We developed an accelerometry system to detect the motion intention of poststroke patients for triggering FES.
• A visual game module was combined with this automated FES training system.
• This system can reduce variability in compound movements produced by poststroke patients and FES.
• An optimal threshold of triggering can defined for each patient for specific tasks.
This paper describes the design and test of an automated functional electrical stimulation (FES) system for poststroke rehabilitation training. The aim of automated FES is to synchronize electrically induced movements to assist residual movements of patients.
In the design of the FES system, an accelerometry module detected movement initiation and movement performed by post-stroke patients. The desired movement was displayed in visual game module. Synergy-based FES patterns were formulated using a normal pattern of muscle synergies from a healthy subject. Experiment 1 evaluated how different levels of trigger threshold or timing affected the variability of compound movements for forward reaching (FR) and lateral reaching (LR). Experiment 2 explored the effect of FES duration on compound movements.
Synchronizing FES-assisted movements with residual voluntary movements produced more consistent compound movements. Matching the duration of synergy-based FES to that of patients could assist slower movements of patients with reduced RMS errors.
Evidence indicated that synchronization and matching duration with residual voluntary movements of patients could improve the consistency of FES assisted movements. Automated FES training can reduce the burden of therapists to monitor the training process, which may encourage patients to complete the training.
Hemiplegia is a common sequela experienced by stroke survivors; it leads to dysfunction in the upper and lower limbs. Various rehabilitation strategies have been adopted to help patients recover limb motor functions [1,2]. The methods of rehabilitation training currently adopted in clinic for poststroke patients are generally high-intensity, repetitive task-oriented paradigms that are practiced daily with outcome feedback . Information on movement kinematics and muscle activation is often used to adjust the training strategy and to ensure that recovery progresses in the desired direction [3,4]. An inappropriate regimen in rehabilitation training may result in abnormal activation of muscles  and may lead to reduced effectiveness in motor functional recovery or even increased risk of muscle contracture and spasticity [5,6].
Functional electrical stimulation (FES) may potentially increase the effectiveness of rehabilitation training. It uses electrical stimulation to assist patients in producing physical movements  and to facilitate the training of patients’ voluntary muscle contraction . Several studies have reported that FES improves the plasticity of the cerebral cortex and can be easily performed by therapists because it does not require extensive manual operations , , , . Evidence suggests that FES is a useful modality for rehabilitation training with explainable neural mechanisms.
Progress has been made in FES applications to aid the recovery of motor functions in patients poststroke , and novel technologies have been integrated into FES paradigms, including gaming  and intelligence applications , , . However, even though many control strategies have been developed to generate electrical stimulation patterns, these control strategies have not been widely translated into routine clinical uses , , , ,  due to the controller is too complex, or needs to be adjusted according to the patient’s condition. Notably, a recent development in neuromotor control theory focusing on the modular organization of multiple muscle activations has led to the formulation of synergy-based FES strategies , , . This approach provides a feasible solution for multi-channel FES control using residual muscle activities from the patient [23,, , , ]; and it leverages the idea that normal movement kinematics can be generated out of muscle synergies .
We have evaluated the synergy-based FES training paradigm in a short-term clinical intervention study. A five day of intervention using synergy-based FES was carried out in poststroke patients. The outcome of the short-term intervention was measured by changes in Fugl-Meyer scores and movement kinematics. Results of evaluations prior to and post intervention showed improvements in both Fugl-Meyer scores and movement kinematics . In a subsequent analysis, synergy-based FES training demonstrated evidence in reorganizing neural circuits in the brain, which led to repairing of impaired muscle activation pattern towards the normal pattern .
In this study, we present a design and verification of an autotriggered FES system with a synergy-based stimulation strategy and used RMS errors to analyze the movement process of the patients for each trial by using acceleration. This automated FES training system is designed to continuously integrate with FES clinical protocol therapeutic intervention in stroke rehabilitation .
The automated FES training system with a gaming interface and accelerometer triggered generation of multiple channels of electrical stimulations to a group of targeted muscles. In this automated FES training system, we anticipated improved consistency of patient movements during rehabilitative training. If successful, the study will provide a training protocol that induces smaller RMS errors across movement trials.
2. Methods and materials
2.1. Design of the automated FES system
Fig. 1 presents a schematic of the components and experimental environment of the automated trigger FES system. The system was composed of a gaming device, an elbow cast including a radiofrequency identification (RFID) reader and an accelerometer, a multichannel FES system, and a computer. The software for the development of the training game (named Picking Apples) was created using Unity (version 2018.1.3f1, Unity Technologies Inc., CA, USA). For ease of operation, the RFID device and the Li-ion battery were mounted in the elbow cast. The RFID information and accelerometer data were transmitted wirelessly by Bluetooth (Fig. 1A).
[ARTICLE] Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis – Full Text
A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery.
The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke.
A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD = 0.42; 95% CI = 0.18–0.66; I2 = 48%; P < 0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD = 0.12; 95% CI = − 0.28 – 0.52; I2 = 0%; P = 0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P = 0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor imagery (P = 0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD = − 0.30; 95% CI = − 0.96 – 0.36; I2 = 0%; P = 0.370; fixed-effects model).
The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.
Motor deficit is the most common sequela after stroke, resulting in severe negative impacts on activities of daily living and social participation for patients . Spontaneous recovery usually occurs within the first 3 months after the onset of stroke; however, there exists a great deal of variability in recovery across patients, particularly patients with severe deficits, who tend to recover less and more slowly . With regard to the importance of motor training in facilitating motor recovery after stroke, various rehabilitation training protocols, such as task-specific training and constrained-induced motor training have been applied in regard to stroke [3, 4]. However, these protocols are limited in patients with severe motor function deficit, due to the voluntary participation of hemiparetic hands. On the other hand, brain-computer interface (BCI) technology does not involve the direct volitional control of hemiparetic hands in training; therefore, it may be promising for these patients.
The term “BCIs” refers to systems that capture the features of brain activity and translate them into computerized commands to control external devices, which can be communication devices , functional electrical stimulation (FES) , or exoskeleton robots , among others. To acquire brain activity signals, either invasive or non-invasive strategies can be used. Invasive BCIs can acquire spatiotemporal signals and have a great capacity to distinguish more dimensions of patients’ intent through implants in the brain cortex . However, non-invasive BCIs, using signals collected from electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), or functional magnetic resonance imaging (fMRI), may be more promising than the invasive strategy in reality, due to safety and ethical issues . Among them, the EEG signal-based BCI is the most commonly used system because of its relatively simple and inexpensive equipment requirements, as well as rich sources regarding its temporal resolution (e.g., visually evoked potential, P300, slow cortical potential) and frequency (e.g., power in given frequency bands) domains, the information can be extracted as the feature for controlling external devices . The EEG signal-based BCI captures the signal of the event-related and time-locked decrease or increase in the oscillatory power in given frequency bands; in other words, the event-related desynchronization (ERD) or event-related synchronization (ERS), respectively [11, 12]. At present, hybrid BCI systems that combine more than one signal can provide more efficient natural control of external devices .[…]