Archive for category Paretic Hand

[Abstract] An Analysis of Wrist and Forearm Range of Motion using the Dartfish Motion Analysis System

Abstract

Introduction

Wrist range of motion (ROM) is considered the universal measurement of success for both surgical and non-surgical treatments. A goniometer can be challenging for an individual; to use by themselves, whereas the Dartfish app can analyze and provide immediate feedback to monitor and evaluate patients’ kinematic changes during recovery.

Purpose

of Study: To establish the validity and reliability of the Dartfish app measuring ROM in order for it to be used in clinical applications.

Methods

Twelve healthy participants, ages 18 to 25, with no previous history of wrist injuries were recruited for this study. The ROM measurements collected were flexion/extension, radial/ulnar deviation, and supination/pronation for both goniometer and Dartfish measurements.Goniometer measurements were performed using a plastic universal two-arm goniometer. Dartfish measurements were performed by two observers on an iPad Pro for three trials. Statistical analyses such as t-tests, and the Pearson correlation coefficient, as well as reliability analyses, such as intraclass correlation coefficient (ICC), and Bland-Altman plots were performed.

Results

There was no significant difference between the goniometer and Dartfish ROM measurements except for the ulnar deviation measurement. The concurrent validity showed nearly; perfect correlations between examiners using Dartfish with r-values that ranged from 0.904 to 0.997, and between ADK and the goniometer showed medium, large, and very large correlations since the values ranged from 0.418 to 0.829. The ICC for test-retest reliability had excellent agreement which ranged from 0.993 to 0.999 and the ICC values for inter-observer reliability had good and excellent agreement which ranged from 0.893 to 0.997.

Conclusion

Overall, the results demonstrated that the Dartfish app was a reliable and valid method to measure wrist and forearm ROM. A patient would be able to easily record their own ROM measurements videos and track their progress during their recovery without the need to visit their physician.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0894113020301575?dgcid=rss_sd_all

, , , , , , , , , ,

Leave a comment

[ARTICLE] A reward–punishment feedback control strategy based on energy information for wrist rehabilitation – Full Text

Abstract

Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination (R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.

Introduction

Stroke has become one of the major diseases that threaten people’s physical and mental health in the world.1 Loss of control of upper limbs is a common impairment underlying disability after stroke for patients, which seriously affects their daily activities.2 Traditional physical therapy is labor intensive and requires great energy of therapists.3 With the development of robotics, the emergence of rehabilitation robots provides a new way for rehabilitation.4 Rehabilitation robots are able to assist patients to complete training tasks without therapists. In addition, rehabilitation robots are capable of estimating patients’ rehabilitation status accurately through a variety of sensors, which helps therapists to develop a follow-up treatment plan for patients.

Control of rehabilitation robots, however, remains an open-ended research area. Control strategies, which target subjects ranging from the mildly impaired and severely impaired, are the most extensively investigated controller paradigm in the rehabilitation robotics community and have been proved to be the most promising techniques for promoting recovery after stroke.5,6 There is strong evidence that high engagement in rehabilitation training induces neural plasticity.7 Therefore, great attention is paid on investigating how to use robot control strategies to promote patients’ active engagement in robotic therapy.

Assist-as-needed (AAN) control strategy is one of the most popular research topics in the field of rehabilitation robots control strategies and is considered promising to promote patients’ engagement. As the name suggested, AAN control strategy emphasizes that robots only supply as much effort as a patient needs to accomplish training tasks by estimating his/her performance in real time.8 Impedance control first proposed by Hogon was applied in AAN control strategy primitively.9 Representatively, Krebs et al. proposed an AAN controller based on impedance control with MIT-Manus,10,11 which can update impedance parameters according to patients’ performance. In this case of robotic therapy, the robot provides assistance based on specific impedance parameters when the subject is not able to track the desired trajectory and does not provide assistance when the subject is able to track or exceed the desired trajectory so as to allow the subject to move voluntarily. This kind of mechanism encourages subjects to get rid of the limitations of the desired trajectory, which can be regarded as a reward and make them more active. But some subjects showed signs of slack behavior that they rely too much on the robot’s assistance to complete the task without any punishments.12 In other words, giving only rewards without punishments will cause subjects’ slackness in rehabilitation training. Therefore, it is necessary to develop control strategies exhibiting the reward–punishment feedback.

Wolbrecht et al. proposed an adaptive controller including a forgetting term to create the reward–punishment feedback mechanism.13 The adaptive law is made up of an error-based adaptive law and a forgetting law. The standard adaptive law dominates when there is a major tracking error so as to assist the subject to complete the task, while the forgetting law dominates when there is a minor tracking error so as to decay the assistance force to promote the subject’s active engagement, which forms a mechanism that gives a reward feedback to subjects by exhibiting a minor tracking error when they are highly engaged and gives a punishment feedback by exhibiting a major tracking error when they are slack. But the adaptive controller is model-based, it does not perform well when it is applied to wrist or finger rehabilitation because minor modeling deviations affect the wrist or finger much more than the upper limb. The tracking error will not change significantly regardless of the degree of the subject’s engagement.

Another improvement to the AAN control strategy was proposed by Pehilivan et al., who introduced a minimum AAN control strategy, which relied on Kalman filter to estimate subjects’ capability.14,15 According to the estimated results, the controller updates the derivative feedback gain to modify the bounds of allowable error on the desired trajectory, which also reflects the idea of reward–punishment feedback. Subsequently, Kalman filter was replaced by nonlinear disturbance observer, and the electromyography (EMG) sensors were used to estimate the subjects’ engagement.16

To sum up, in order to promote the engagement of subjects, the common feature of the above control strategies is that they can create a reward–punishment feedback mechanism according to the subjects’ current engagement or performance. To the best of our knowledge, previous researchers have not specifically identified this mechanism. More control strategies for rehabilitation robots support this point of view.1725

In this article, we proposed a reward–punishment feedback control strategy to promote subjects’ engagement for wrist rehabilitation. Firstly, we utilize the energy contributed by the subject to estimate his/her engagement. The energy can be obtained by calculating the integral of the torque contributed by the subject against the position. Secondly, an adaptive controller including a reward–punishment term was proposed. Unlike the adaptive controller above,13 the included term is not constant. Instead, it updates based on the estimated results so that the controller can give reward or punishment feedback to subjects by reflecting different tracking error, which is suitable for wrist rehabilitation. Finally, the control strategy was demonstrated through experiments on healthy subjects without cardiovascular and cerebrovascular diseases operating a wrist rehabilitation robot. The contributions of this work include the development of an engagement estimated approach without any extra sensors, which greatly reduces development costs. This work also proposed an improved adaptive controller including a reward–punishment term for wrist rehabilitation, which has great potential to promote subjects’ engagement.

This article is organized as follows. The second section presents an engagement estimated approach based on energy information and a human robot coupled system modeling. The third section proposes an adaptive controller including a reward–punishment term and details the Lyapunov stability analysis. The fourth section introduces the specific implementation methods of three experiments. The fifth section presents and analyzes experimental results. Eventually, the discussion and conclusion are presented in the sixth section.

Engagement estimated based on energy information

We have developed a wrist rehabilitation robot, a three degree-of-freedom (DOF) device, as shown in Figure 1(a). The device is capable of independently actuating all three DOFs of subject’s forearm and wrist. Relatively, the device has three joints: flexion/extension joint, radial/ulnar deviation joint, and pronation/supination joint can all be controlled. Each joint of the device employs both a brushless DC motor with a conveyor belt to drive. Therefore, the control methods of the three joints are similar, and this article only describes the control strategy of the flexion/extension joint.


                        figure
Figure 1. The mechanical structure of the wrist rehabilitation robot. (a) The directional view of the human robot coupled system. (b) The side view of the wrist rehabilitation robot.

[…]

Continue

, , , ,

Leave a comment

[ARTICLE] Non-Immersive Virtual Reality for Post-Stroke Upper Extremity Rehabilitation: A Small Cohort Randomized Trial – Full Text

Abstract

Immersive and non-immersive virtual reality (NIVR) technology can supplement and improve standard physiotherapy and neurorehabilitation in post-stroke patients. We aimed to use MIRA software to investigate the efficiency of specific NIVR therapy as a standalone intervention, versus standardized physiotherapy for upper extremity rehabilitation in patients post-stroke. Fifty-five inpatients were randomized to control groups (applying standard physiotherapy and dexterity exercises) and experimental groups (applying NIVR and dexterity exercises). The two groups were subdivided into subacute (<six months post-stroke) and chronic (>six months to four years post-stroke survival patients). The following standardized tests were applied at baseline and after two weeks post-therapy: Fugl–Meyer Assessment for Upper Extremity (FMUE), the Modified Rankin Scale (MRS), Functional Independence Measure (FIM), Active Range of Motion (AROM), Manual Muscle Testing (MMT), Modified Ashworth Scale (MAS), and Functional Reach Test (FRT). The Kruskal–Wallis test was used to determine if there were significant differences between the groups, followed with pairwise comparisons. The Wilcoxon Signed-Rank test was used to determine the significance of pre to post-therapy changes. The Wilcoxon Signed-Rank test showed significant differences in all four groups regarding MMT, FMUE, and FIM assessments pre- and post-therapy, while for AROM, only experimental groups registered significant differences. Independent Kruskal–Wallis results showed that the subacute experimental group outcomes were statistically significant regarding the assessments, especially in comparison with the control groups. The results suggest that NIVR rehabilitation is efficient to be administered to post-stroke patients, and the study design can be used for a further trial, in the perspective that NIVR therapy can be more efficient than standard physiotherapy within the first six months post-stroke.

1. Introduction

Stroke Alliance for Europe states that “every 20 s, someone in Europe has a stroke”, while in the United States, “someone has a stroke every 40 s” a leading cause of significant long-term disabilities [1,2]. According to a European Union (EU) report, Romania has the lowest annual healthcare expenditure per capita (€1029 in 2015, compared to the EU average of €2884). The highest risk factors of a stroke are smoking and alcohol drinking, with males accounting for more than 50% of those impacted. Additionally, the level of education influences both lifestyle and life expectancy, with the Romanian life expectancy being among the lowest in the EU (75.3 years in Romania versus 80.9 years in the EU, in 2015). Moreover, there were 61,552 stroke cases in Romania in 2015 and forecasts state that this number will increase by 24% until 2035 [3,4].Worldwide, the population faces high incidence rates of stroke and post-stroke sequelae with an increased need for neurorehabilitation services. In Europe, it is estimated that the number of annual stroke events will increase from 613,148 registered in 2015 to 819,771 in 2035, an increase of 34%. Considering that post-stroke survival rates have improved; estimations predict that the number of people living with strokes in Europe will grow from 3,718,785 in 2015 to 4,631,050 in 2035 [1].Stroke complications can be long-lasting; thus, at 15-years post-stroke, two-thirds of survivors live with a disability, nearly two of five suffer from depression, and more than a quarter have cognitive impairment [5]. Post-stroke disability significantly contributes to the increasing use of long-term medical care resources, thus highlighting that efficient rehabilitation can cut costs in the healthcare system [6] whereas telerehabilitation is still in the early phase of utilization in developing countries.Furthermore, international guidelines for stroke rehabilitation include physiotherapy techniques and methods for the recovery of the swallowing function and the urinary and bowel continence. These techniques and methods are also recommended for the improvement/prevention of shoulder pain, joint misalignments, and limb deviations caused by post-stroke spasticity, also used for secondary prevention of falling, as well as for enhancing the ability to perform self-care and daily living activities. Recovery from post-stroke impairments is facilitated, on the one hand, by increasing the motor function and, on the other hand, by improving the functionality of the limbs and body as a whole functional unit. In order to retrieve functional capacity, the existing guidelines recommend the use of intensive, repetitive training, improvement of functional mobility, use of orthoses, performing specific activities of daily living (ADLs) practiced repeatedly, progressive and bilateral training of the upper limb, the use of virtual reality and assisted robotic therapy, and the use of strength training exercises [7,8,9].The use of virtual reality technology as an adjunct or substitute for traditional physiotherapy has been studied and proved to be effective in improving patients’ functional rehabilitation. However, as regards strokes, some systematic reviews suggest that virtual reality (VR) has not brought more benefits to patients compared to standard physiotherapy alone, while other research advocates for specific VR training as a therapy with a better outcome compared to conventional physiotherapy in the rehabilitation of stroke survivors [10,11,12,13,14].Research on neuroplasticity and learning or relearning abilities shows that there are several principles of motor learning, including multisensory stimulation, explicit feedback, knowledge of results, and motor imagery. These principles, notably explicit feedback and multisensory stimulation, are found in the VR technology used for neuromotor rehabilitation. Accordingly, VR therapy becomes an alternative to classical physiotherapy, as it develops neuroplasticity. So, novel enriched environments are preferred in the context of current rehabilitation methods since guidelines do not provide an accurate record of evidence inferred from the specialized literature about motor skill learning. This evidence is essential in identifying practical methods and applications that could shape future approaches to neuromotor relearning. Furthermore, in animal research, it has been shown that aerobic exercise and environmental enrichment have pleiotropic actions that influence the occurrence of molecular changes associated with stroke and subsequent spontaneous recovery. These aspects may argue in favor of the efficient use of VR in motor and functional recovery after a stroke, by stimulating neuroplasticity [15,16].Over the past ten years, research and literature reviews regarding the use of VR in post-stroke recovery have been homogeneous. Many approaches have focused on the use of VR as adjunct therapy alongside standard physiotherapy, and in some studies, non-dedicated VR technologies have been used, for medical purposes, in the motor rehabilitation of post-stroke patients [17,18]. Previous research on NIVR and immersive VR-based activities suggests that these interventions improve upper extremity rehabilitation after a stroke by providing motivating environments, stimulating extrinsic feedback, or simulating gameplay to facilitate recovery. Besides non-immersive VR therapy use in post-stroke patient’s rehabilitation, immersive VR therapy is used but requires more space and is more expensive, compared to NVIR. Robotic therapy is gaining more ground in neuro-motor rehabilitation, but the costs are very high, and in the case of exoskeletons, complex technology requires a long period of time for physiotherapists to acquire skills in the use of equipment. Currently, research has shown that VR positively influences the recovery of the upper extremity in post-stroke patients, as an adjunct therapy, by using dedicated and non-dedicated technologies [19,20]. The VR action on upper extremity post-stroke rehabilitation, using dedicated NVIR technology as a standalone therapy has not yet been determined at a staged level according to the post-stroke phases. The present study aims to investigate the efficiency of a dedicated NIVR system used in the rehabilitation of patients with subacute and chronic stroke, on upper extremity functionality and motor function. The research was done through specific VR training that incorporates real-time 3D motion capture and built-in visual feedback which provide functional exercises designed to train and regain the neuromotor functions of the upper extremity.Our main goal was to evaluate the efficiency of the proposed protocol, by using staged, specific, and customized NIVR therapy on three levels of difficulty and by using specific exergames according to patient’s capacity, and adjusted by the level of difficulty, compared to standard physiotherapy. Besides, we were looking for differences in post-stroke clinical and functional status in the use of VR that improve or negatively influence the functional outcomes of the upper extremity when exposed to VR-based therapy compared to standard physiotherapy. […]

Continue —-> https://www.mdpi.com/2076-3425/10/9/655/htm

, , , , , , , , , ,

Leave a comment

[VIDEO] Repetitive Transcranial Magnetic Stimulation (rTMS) and Hand Therapy – YouTube

Repetitive transcranial magnetic stimulation (rTMS), combined with hand therapy, is currently a research-based treatment. It is intended to help people with stroke achieve greater recovery of hand function. The hand weakness following a stroke stems from brain neurons destroyed by the stroke, as well as from surviving neurons. The surviving neurons become dormant (not active) through inactivity and suppression from the non-stroke hemisphere. In some patients, rTMS can help dormant neurons reactivate and thereby improve voluntary hand function.

More information: https://www.mhealth.org/care/treatmen…

, , , , , , ,

Leave a comment

[Abstract] Association between postural patterns of spastic upper extremity and functional independence after TBI and stroke

Abstract

BACKGROUND:Spastic hypertonia of the upper limb after stroke or traumatic brain injuries (TBI) is a prevalent clinical sign causing abnormal postures and movement patterns due to hyperexcitability of the upper motor neurons and rheological alterations in the affected muscles. These alterations limit the use of the upper limb, restricting its functional activities and affecting the individual’s quality of life and social participation.

OBJECTIVE:To determine the association between spastic patterns of the upper limb, wrist, fingers and thumb, and independence in everyday activities after a stroke or TBI.

METHODS:The design is a cross-sectional descriptive and correlational study. The sample consisted of 206 individuals who complied with the eligibility criteria and signed an informed consent. Clinical evaluation was carried out, including determination of the postural pattern of the upper extremity according to Hefter’s taxonomy and postural pattern classification of the wrist, fingers and thumb. Functional independence was evaluated using the Functional Independence Measure (FIM) and the Barthel Index (BI).

RESULTS:Univariate between-subject ANOVAs were used to examine associations of the four pattern classifications with the two independence measures, FIM and BI. Results indicate that Pattern I of Hefter’s upper limb taxonomy is associated with lesser functional independence according to FIM and BI mean scores.

CONCLUSIONS:The postural pattern of the upper limb after TBI or stroke is related to the patient’s functional independence. Specifically, Pattern I tends to co-occur with low independence.

Source: https://content.iospress.com/articles/neurorehabilitation/nre203042

, , , , , , , , , , , , ,

Leave a comment

[Abstract] Wearable vibrotactile stimulation for upper extremity rehabilitation in chronic stroke: clinical feasibility trial using the VTS Glove

Abstract

Objective: Evaluate the feasibility and potential impacts on hand function using a wearable stimulation device (the VTS Glove) which provides mechanical, vibratory input to the affected limb of chronic stroke survivors.

Methods: A double-blind, randomized, controlled feasibility study including sixteen chronic stroke survivors (mean age: 54; 1-13 years post-stroke) with diminished movement and tactile perception in their affected hand. Participants were given a wearable device to take home and asked to wear it for three hours daily over eight weeks. The device intervention was either (1) the VTS Glove, which provided vibrotactile stimulation to the hand, or (2) an identical glove with vibration disabled. Participants were equally randomly assigned to each condition. Hand and arm function were measured weekly at home and in local physical therapy clinics.

Results: Participants using the VTS Glove showed significantly improved Semmes-Weinstein monofilament exam, reduction in Modified Ashworth measures in the fingers, and some increased voluntary finger flexion, elbow and shoulder range of motion.

Conclusions: Vibrotactile stimulation applied to the disabled limb may impact tactile perception, tone and spasticity, and voluntary range of motion. Wearable devices allow extended application and study of stimulation methods outside of a clinical setting.

Source: https://www.researchgate.net/publication/343096166_Wearable_vibrotactile_stimulation_for_upper_extremity_rehabilitation_in_chronic_stroke_clinical_feasibility_trial_using_the_VTS_Glove

, , , , , , , ,

Leave a comment

[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

, , , , , , , , , , , , ,

Leave a comment

[Abstract] Functional implications of impaired bimanual force coordination in chronic stroke

Highlights

• We examined the role of bimanual force coordination in bimanual dexterity after stroke.

• Stroke group showed impaired dexterity in a bimanual task with a shared goal.

• Stroke group had poor bimanual coordination of forces during dynamic force modulation.

• Reduced bimanual force coordination predicted impaired dexterity in a bimanual task.

Abstract

Background

The ability to coordinate forces with both hands is crucial for manipulating objects in bimanual tasks. The purpose of this study was to determine the influence of bimanual force coordination on collaborative hand use for dexterous tasks in chronic stroke survivors.

Methods

Fourteen stroke survivors (63.03 ± 15.33 years) and 14 healthy controls (68.85 ± 8.16) performed two bimanual tasks: 1) Pegboard assembly task, and 2) dynamic force tracking task using bilateral index fingers. The Pegboard assembly task required collaborative use of both hands to construct a structure with pins, collars, and washers. We quantified bimanual dexterity with Pegboard assembly score as the total number of pins, collars, and washers assembled in one minute. The force tracking task involved controlled force increment and decrement while tracking a trapezoid trajectory. The task goal was to match the target force with the total force, i.e., sum of forces produced by both hands as accurately as possible. We quantified bimanual force coordination by computing time-series cross-correlation coefficient, time-lag, amplitude of coherence in 0 – 0.5 Hz, and 0.5 – 1 Hz for force increment and decrement phases.

Results

In the Pegboard assembly task, the stroke group assembled fewer items relative to the control group (p = 0.004). In the bimanual force tracking task, the stroke group showed reduced cross-correlation coefficient (p = 0.01), increased time-lag (p = 0.00), and reduced amplitude of coherence in 0 – 0.5 Hz (p = 0.03) and in 0.5 – 1 Hz (p = 0.00). Multiple regression analysis in the stroke group revealed that performance on Pegboard assembly task was explained by cross-correlation coefficient and coherence in 0.5 – 1 Hz during force increment (R2 = 0.52, p = 0.00).

Conclusions

Individuals with stroke show impaired bimanual dexterity and diminished bimanual force coordination. Importantly, stroke-related deterioration in bimanual force coordination is associated with poor performance on dexterous bimanual tasks that require collaboration between hands. Re-training bimanual force coordination in stroke survivors could facilitate a higher degree of participation in daily activities through improved bimanual dexterity.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0304394020306571

, , ,

Leave a comment

[ARTICLE] Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control – Full Text

Abstract

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.

Introduction

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 [1]. 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 [2][3]. 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 [4][5]. 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 [6][7].

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 [8][9].

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 [10][11].

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 [12].

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 [13].

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 [14].

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 [15].

The network topology diagrams of the mobile communication network and the wireless sensor network are shown in Figure 1 and Figure 2, respectively.

FIGURE 1. - Mobile communication network topology.

FIGURE 1.

Mobile communication network topology.

View All

FIGURE 2. - Wireless sensor network topology.

FIGURE 2.

Wireless sensor network topology.

View All

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 [16].

Continue —–> https://ieeexplore.ieee.org/abstract/document/9159631

, , , , , , , , , , , , ,

Leave a comment

[Abstract + References] Move-IT: A Virtual Reality Game for Upper Limb Stroke Rehabilitation Patients – Conference paper

Abstract

Stroke rehabilitation plays an important role in recovering the lifestyle of stroke survivors. Although existing research proved the effectiveness and engagement of Non-immersive Virtual Reality (VR) based rehabilitation systems, however, limited research is available on the applicability of fully immersive VR-based rehabilitation systems. In this paper, we present the development and evaluation of “Move-IT” game designed for domestic upper limb stroke patients. The game incorporates the use of Oculus Rift Head Mounted Display (HMD) and the Leap Motion hand tracker. A user study of five upper limb stroke patients was performed to evaluate the application. The results showed that the participants were pleased with the system, enjoyed the game and found it was exciting and easy to play. Moreover, all the participants agreed that the game was very motivating to perform rehabilitation exercises.

References

  1. 1.“What is stroke?” Stroke.org, 16 July 2014. http://www.stroke.org/understand-stroke/what-stroke. Accessed 20 May 2020
  2. 2.Burke, J.: Games For Upper-limb Stroke Rehabilitation (Seminar). University of Ulster, Northern Ireland, 29 March 2010Google Scholar
  3. 3.A stroke occurs when the brain is damaged due to lack of blood supply. http://www.brainandnerves.com/uk/blood-vessels-of-the-brain/stroke/. Accessed 20 May 2020
  4. 4.Khujah, A.: Stroke Rehabilitation, 17 February 2012. http://archive.aawsat.com/details.asp?section=15&article=664001&issueno=12134#.WHQSn1N97X5. Accessed 20 May 2020
  5. 5.WHO|The world health report 2002 – Reducing Risks, Promoting Healthy Life, WHO. http://www.who.int/whr/2002/en/. Accessed 20 May 2020
  6. 6.Alhazani, 100 stroke cases accure in SA daily, 13 September 2013. http://www.alarabiya.net/ar/saudi-today/2013/09/17/السعودية-تسجل-100-اصابة-بالسكتة-الدماغية-يوميا.html. Accessed 20 May 2020
  7. 7.Alsinani, F.: 6000 of stroke cases accure in Kingdom of Saudi Arabia yearly, Riyad newspaper, 14 Apr 2005. http://www.alriyadh.com/56594. Accessed 23 May 2020
  8. 8.Gunasekera, W., Bendall, J.: Rehabilitation of neurologically injured patients. In: Moore, A.J., Newell, D.W. (eds.) Neurosurgery. Springer Specialist Surgery Series, pp. 407–421. Springer, London (2005).  https://doi.org/10.1007/1-84628-051-6_23
  9. 9.Burke, J.W., McNeill, M., Charles, D., Morrow, P., Crosbie, J., McDonough, S.: Serious games for upper limb rehabilitation following stroke. In: Proceedings of the 2009 Conference in Games and Virtual Worlds for Serious Applications, Washington, DC, USA, 2009, pp. 103–110 (2009)Google Scholar
  10. 10.Rego, P.A., Moreira, P., Reis, L.: Serious games for rehabilitation: a survey and a classification towards a taxonomy. In: 5th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2010)Google Scholar
  11. 11.Laver, K.E., George, S., Thomas, S., Deutsch, J.E., Crotty, M.: Virtual reality for stroke rehabilitation. Cochrane Database Syst. Rev. no. 9, p. CD008349, September 2011Google Scholar
  12. 12.AlMousa, M., Al-Khalifa, H.S., AlSobayel, H.: Requirements elicitation and prototyping of a fully immersive virtual reality gaming system for upper limb stroke rehabilitation in Saudi Arabia. Mobile Information Systems (2017). https://www.hindawi.com/journals/misy/2017/7507940/. Accessed 23 May 2020
  13. 13.Grimm, F., Gharabaghi, A.: Closed-loop neuroprosthesis for reach-to-grasp assistance: combining adaptive multi-channel neuromuscular stimulation with a multi-joint arm exoskeleton. Front. Neurosci. 10, 284 (2016)Google Scholar
  14. 14.Dörner, R., Göbel, S., Effelsberg, W., Wiemeyer, J. (eds.): Serious Games, Foundations, Concepts and Practice. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-40612-1CrossRefGoogle Scholar
  15. 15.Yagv, B.: Overview of virtual reality technologies. In: Presented at the Interactive Multimedia Conference, University of Southampton, United Kingdom (2013)Google Scholar

Source: https://link.springer.com/chapter/10.1007/978-3-030-58796-3_23

, , , , , , ,

Leave a comment

%d bloggers like this: