[Abstract] Virtual Rehabilitation Program Using Kinect and Leap Motion Sensor Enhances Upper Limb Function in Stroke Patients

Abstract:

Virtual reality is being increasingly used in the field of rehabilitation. We tested the benefit of a non-immersive virtual rehabilitation program using the Kinect and Leap Motion sensor in the upper limb rehabilitation in subacute and chronic stroke patients. A total of 30 stroke survivors were randomly assigned in two groups in a 1:1 ratio. The active group benefited from 10 sessions of 30 minutes of virtual rehabilitation program added to the standard rehabilitation therapy. The control group received 10 sessions of 1.5 hours/day of standard rehabilitation therapy during their 14 days hospitalization. Upper limb function was assessed using the Action Research Arm Test (ARAT) and Upper Extremity Fugl-Meyer Assessment (UE-FM) at the beginning and at the end of the 14 days of study. The ARAT and UE-FM improvements were significantly higher in the active group compared to the control group. ARAT improvement was 8.13±5.74 points in the active group versus 2(0.25-2) points in the control group, p=0.0003. The UE-FM improvement was 6.86±3.11 points in the intervention group versus 2.2±1.14 points in the control group, p<0.0001. Virtual rehabilitation therapy was equally effective in the subacute and chronic stroke patients. In the subacute stroke patients, the ARAT improvement was 7.37±6.27 points versus 9±5.41 points in chronic stroke patients (p=0.6). UE-FM improvement in subacute stroke patients was 7.25±3.99 points versus 6.42±1.9 points in chronic patients (p=0.62). Conclusions: Non-immersive virtual reality program improved motor recovery in stroke patients. Both subacute and chronic patients benefited from the virtual therapy.

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[Abstract] Design of a hand rehabilitation gaming platform using IoT technologies – IEEE Conference Publication

Abstract

Nowadays, elements of the game can be met more often in regular processes. Education, business, marketing and many other spheres are being included with gaming elements, since games, according to conducted studies, positively affect people and make them happier. Also, games reduce stress and help to be focused on specific tasks. Today’s technologies such as virtual reality tools provide huge opportunities for developers to create projects that can be used as a key element that improves the efficiency and results of certain processes.This article presents a gaming platform for hand rehabilitation, which includes the use of a Leap Motion controller in conjunction with an Arduino-based robotic arm. The main idea of gamification of hand rehabilitation is to help improve the accuracy of gestures, coordination, and also restore the functionality of the hands using the capabilities of Leap Motion and Arduino.

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[Abstract + References] Enhancing mirror therapy via scaling and shared control: a novel open-source virtual reality platform for stroke rehabilitation

Abstract

Mirror therapy is increasingly used in stroke rehabilitation to improve functional movements of the affected limb. However, the extent of mirroring in conventional mirror therapy is typically fixed (1:1) and cannot be tailored based on the patient’s impairment level. Further, the movements of the affected limb are not actively incorporated in the therapeutic process.

To address these issues, we developed an immersive VR system using HTC Vive and Leap Motion, which communicates with our free and open-source software environment programmed using SteamVR and the Unity 3D gaming engine. The mirror therapy VR environment was incorporated with two novel features: (1) scalable mirroring and (2) shared control. In the scalable mirroring, mirror movements were programmed to be scalable between 0 and 1, where 0 represents no movements, 0.5 represents 50% mirroring, and 1 represents 100% mirroring. In shared control, the contribution of the mirroring limb to the movements was programmed to be scalable between 0 to 1, where 0 represents 100% contribution from the mirroring limb (i.e., no mirroring), 0.5 represents 50% of movements from the mirrored limb and 50% of movements from the mirroring limb, and 1 represents full mirroring (i.e., no shared movements).

Validation experiments showed that these features worked appropriately. The proposed VR-based mirror therapy is the first fully developed system that is freely available to the rehabilitation science community. The scalable and shared control features can diversify mirror therapy and potentially augment the outcomes of rehabilitation, although this needs to be verified through future experiments.

References

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[Abstract] Application of Gamification Tool in Hand Rehabilitation Process

Abstract

Video games are constantly evolving and today they are one of the main types of entertainment. As we live in the digital age, the implementation of IT solutions in other areas of activity remains relevant. Today, almost all processes use IT technologies: calling a taxi, ordering food, education, shopping, and so on. The use of IT technologies in the field of medicine is not uncommon. But it’s not often that you see video games being used in this area. Video games and their development are an integral part of the IT sphere. The technologies that exist now allow us to create our own game products, which can then be implemented in other processes. This research is aimed at studying the term of “gamification”, its impact on rehabilitation processes, and the study of gamification tools and game products used in the field of medicine. In this research we propose a gamified solution for hand rehabilitation process which include 3D game that work in conjunction with the VR tool called Leap Motion. Since video games attract people with reward systems, goals and many other factors, why not to use it as a key element in process of rehabilitation? In some cases, it is more effective method rather than traditional therapy.

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[ARTICLE] Research on Key Technologies of Hand Function Rehabilitation Training Evaluation System Based on Leap Motion – Full Text

Abstract

This paper proposes an immersive training system for patients with hand dysfunction who can perform rehabilitation training independently. The system uses Leap Motion binocular vision sensors to collect human hand information, and uses the improved PCA (Principal Component Analysis) to perform data fusion on the real-time data collected by the sensor to obtain more hands with fewer principal components, and improve the stability and accuracy of the data. Immediately, the use of improved SVM (Support Vector Machine) and KNN (K-Nearest Neighbor Algorithm) for gesture recognition and classification is proposed to enable patients to perform rehabilitation training more effectively. Finally, the effective evaluation results of the rehabilitation effect of patients by the idea of AHP (Analytic Hierarchy Process) are taken as necessary reference factors for doctors to follow up treatment. Various experimental results show that the system has achieved the expected results and has a good application prospect.

1. Introduction

According to the data in the report of stroke prevention and treatment in China in 2019, the number of stroke patients over the age of 40 reached 12.42 million. At present, the number of patients in China continues to grow at a rate of 12% per year, bringing heavy burden to patients’ families and this society. There are currently 17 million new stroke patients worldwide each year (equivalent to the total population of Beijing), 6.5 million deaths from strokes each year, and 26 million surviving stroke patients worldwide (equivalent to that of a European country’s total population). Everyone has a one-sixth chance to be related to stroke. Stroke is an acute cerebrovascular disease with high morbidity, high mortality and high disability. It is the leading cause of disability in Chinese adults. Stroke finger weakness or poor movement affects the patient’s normal rehabilitation progress. Stroke rehabilitation has become a major problem for stroke patients [1], so how to use modern human-computer interaction technology to play a certain key to the rehabilitation of patients, compared with large and expensive machinery and equipment, is more important and simpler, and at the same time, it can be afforded by most patients. Under this background, the key technology of hand function rehabilitation evaluation system based on new somatosensory equipment is proposed in Research.

In recent years, with the maturity of the computer level all over the world, human-computer interaction based on virtual reality technology has also become the focus of research. In-depth research on the existing new body-sensing device Leap Motion is also a problem that many scientists are keen on. In 2019, Z. W. Zhu [2] used Kinect to introduce the Bhattacharyya distance into the Bayesian Perceptual Hidden Markov Model to develop a depth image-based gesture recognition system, and verified the superiority of the system, but overall, Kinect gesture recognition is far less accurate and reliable than Leap Motion. In 2019, P. Sun [3] and others used a combination of principal component analysis and support vector machine to classify and recognize static gesture pictures. The results show that the algorithm has certain application value. The disadvantage is that the system only uses gesture images. Simple identification and classification have been performed. The accuracy needs to be improved, and no specific application scenario is mentioned. In 2017, C. X. Tang [4] used the effective combination of multiple sensors to reduce the decline in gesture recognition rate due to occlusion and other factors, thereby effectively improving the recognition rate. However, for the joint effect of multiple Leap Motion, there is still no more convincing experimental proof. In 2016, Z. H. Liu [5] of Donghua University and others used Leap Motion and PC to build a low-cost stroke upper limb rehabilitation and evaluation system. Patients completed training tasks and achieved a certain degree of rehabilitation under the guidance of virtual games. The rehabilitation evaluation system only uses the example of Leap Motion’s official website, and does not reflect the detailed evaluation scores in real-time rehabilitation with real patients, which is highly subjective. In 2015, J. T. Hu [6] improved the static and dynamic gesture recognition algorithms of Leap Motion, and also applied them to some simple daily activities. However, the types of gestures that can be recognized are too simple, and the accuracy is still slightly insufficient.

To sum up, there is currently no system for efficient rehabilitation training for patients with hand dysfunction and the training results are recorded and fed back to the doctor in real time. This paper proposes the key technology research of hand function rehabilitation training system based on Leap Motion. The real-time rehabilitation training information collected by Leap Motion is used to effectively identify and classify the optimized PCA and SVM. This not only avoids the problem of low recognition rate caused by Leap Motion, but also overcomes the problem of losing gesture information caused by simply using an algorithm, and accurately improves the recognition rate of gestures. Then, the effect of rehabilitation training on patients is evaluated with the idea of AHP, so that doctors can grasp the rehabilitation information of patients at any time. In order to achieve more effective rehabilitation of patients’ hand function training.

2. Design and Implementation of Hand Rehabilitation Training System

Leap Motion is a new type of somatosensory device [7], which adopts the principle of infrared binocular vision and uses infrared LED and cameras to complete the recognition and tracking of human hand movements in a way different from other motion control technologies. The two built-in cameras can capture the information in the shape of an inverted pyramid between 25 – 600 ms above, as shown in Figure 1. Leap Motion uses triangulation to locate the position information of the hand in three dimensions. The basic unit of its collection is frame, with an average capture accuracy of 0.7 mm. At the same time, it records and tracks hand movement data at a rate of 200 frames per second. Each frame of data contains the position information of the key parts of the hand, including palm movement speed, palm normal vector, finger orientation and so on. This accuracy is much higher than Microsoft’s Kinect, and has higher acquisition efficiency and accuracy.

Leap Motion transmits the captured static gesture position, vector information, and dynamic gesture movement information to the computer for subsequent processing and gesture extraction and recognition through the USB interface [8]. The specific gesture recognition process is shown in Figure 2.

Figure 1. Leap Motion mapping range map.

Figure 2. Leap Motion gesture recognition flowchart.

Among them, Leap Motion’s most important steps in gesture recognition are gesture segmentation, gesture analysis and tracking, and gesture recognition. The role of gesture segmentation is to separate the required gestures from the surrounding environmental factors, so as to better realize the recognition of gestures; the role of gesture analysis and tracking is to obtain the feature information and motion characteristics of the gestures, thereby ensuring the subsequent algorithms Robustness; the role of gesture recognition is to accurately classify various types of gestures, and it is also the most critical step to make the type of gesture required to play a better role in applications based on gesture recognition.[…]

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[ARTICLE] Finger State Progress Model for Virtual Fine Motor Stroke Rehabilitation – Full Text PDF

ABSTRACT

Manual observation in measuring and assessing stroke
patient progress in fine motor rehabilitation will lead to
inconsistencies especially when the patient is evaluated
by different therapists or attends different
rehabilitation facilities. In addition, it also increases
therapist workload if they need to supervise many
patients at the same time. Thus, a model was proposed to
capture finger data from motion sensor device using
Time-Based Simplified Denavit-Heartenberg (TS-DH)
and the Finger State progress (FSP) model. Actual
finger movement was compared with patterns of finger
state for real-time evaluation of finger movement
progress. The model will assist therapists in real-time or
post-exercise evaluation of patient progress and
analysis can be done during stroke rehabilitation
exercise. As a conclusion, the model can be used
efficiently in virtual stroke rehabilitation as real-time
indicator or as a long term analysis to compare prior
progress.[…]

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[Abstract] An Automatic Rehabilitation Assessment System for Hand Function Based on Leap Motion and Ensemble Learning

Abstract

For stroke patients, hand function assessment is an important part of the hand rehabilitation process. The hand function assessment, however, requires the patient to complete a series of actions under the guidance of the therapist who then scores the patient’s performance. This type of assessment is both time-consuming and highly subjective. Therefore, in order to achieve a fast, objective and accurate assessment, this paper adopts a non-contact infrared imaging device, Leap Motion, to measure the patient’s motion information and then uses these motion information to infer the hand’s rehabilitation level. This paper improves the traditional way of hand function assessment from the following aspects. Only three coherent movements (finger opposition, lift wrist and stretch fingers) are required to complete the assessment, which makes the assessment time shorter and the assessment process easier. At the same time, an assessment algorithm based on the Ensemble Learning is proposed and integrated into the automatic hand function assessment system. In addition, the virtual reality game has been implemented in the assessment system to ensure a satisfactory interaction with patients, which makes the assessment process more interesting and convenient. Using this system, 50 stroke patients underwent clinical trials with the Brunnstrom and Fugl-Meyer assessment scales. The matching rate between the automatic assessment result and the manual Brunnstrom assessment result is 92%, while the matching rate with the Fugl-Meyer assessment result is 82%. Furthermore, Wilcoxon Signed-Rank test and Kappa test are also used to validate the consistency between the automatic assessment results and the manual assessment results. These experiments illustrate that this automatic assessment system is fast, comfortable and reliable.

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[Abstract] Application of AR and VR in Hand Rehabilitation: A Systematic Review

Highlights

•The human hand accounts for a third of all work-related accidents.

•Feedback, challenge and increased difficulty are motivators of patients’ adherence.

•Leap Motion Controller and haptic gloves can be integrated into the home setting.

•AR/VR technologies can be used as a complement to conventional therapies.

•Patients can benefit from the use of AR or VR interventions for hand rehabilitation.

Abstract

Background

The human hand is the part of the body most frequently injured in work related accidents, accounting for a third of all accidents at work and often involving surgery and long periods of rehabilitation. Several applications of Augmented Reality (AR) and Virtual Reality (VR) have been used to improve the rehabilitation process. However, there is no sound evidence about the effectiveness of such applications nor the main drivers of therapeutic success.

Objectives

The objective of this study was to review the efficacy of AR and VR interventions for hand rehabilitation.

Methods

A systematic search of publications was conducted in October 2019 in IEEE Xplore, Web of Science, Cochrane library, and PubMed databases. Search terms were: (1) video game or videogame, (2) hand, (3) rehabilitation or therapy and (4) VR or AR. Articles were included if (1) were written in English, (2) were about VR or AR applications, (3) were for hand rehabilitation, (4) the intervention had tests on at least ten patients with injuries or diseases which affected hand function and (5) the intervention had baseline or intergroup comparisons (AR or VR intervention group versus conventional physical therapy group). PRISMA protocol guidelines were followed to filter and assess the articles.

Results

From the eight selected works, six showed improvements in the intervention group, and two no statistical differences between groups. We were able to identify motivators of patients’ adherence, namely real-time feedback to the patients, challenge, and increased individualized difficulty. Automated tracking, easy integration in the home setting and the recording of accurate metrics may increase the scalability and facilitate healthcare professionals’ assessments.

Conclusions

This systematic review provided advantages and drivers for the success of AR/VR application for hand rehabilitation. The available evidence suggests that patients can benefit from the use of AR or VR interventions for hand rehabilitation.

Graphical abstract

Source: https://www.sciencedirect.com/science/article/abs/pii/S1532046420302136

[Abstract] A game changer: the use of digital technologies in the management of upper limb rehabilitation – BOOK

Abstract

Hemiparesis is a symptom of residual weakness in half of the body, including the upper extremity, which affects the majority of post stroke survivors. Upper limb function is essential for daily life and reduction in movements can lead to tremendous decline in quality of life and independence. Current treatments, such as physiotherapy, aim to improve motor functions, however due to increasing NHS pressure, growing recognition on mental health, and close scrutiny on disease spending there is an urgent need for new approaches to be developed rapidly and sufficient resources devoted to stroke disease. Fortunately, a range of digital technologies has led to revived rehabilitation techniques in captivating and stimulating environments. To gain further insight, a meta-analysis literature search was carried out using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) method. Articles were categorized and pooled into the following groups; pro/anti/neutral for the use of digital technology. Additionally, most literature is rationalised by quantitative and qualitative findings. Findings displayed, the majority of the inclusive literature is supportive of the use of digital technologies in the rehabilitation of upper extremity following stroke. Overall, the review highlights a wide understanding and promise directed into introducing devices into a clinical setting. Analysis of all four categories; (1) Digital Technology, (2) Virtual Reality, (3) Robotics and (4) Leap Motion displayed varying qualities both—pro and negative across each device. Prevailing developments on use of these technologies highlights an evolutionary and revolutionary step into utilizing digital technologies for rehabilitation purposes due to the vast functional gains and engagement levels experienced by patients. The influx of more commercialised and accessible devices could alter stroke recovery further with initial recommendations for combination therapy utilizing conventional and digital resources.

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[Abstract + References] A Feasibility Study on Wrist Rehabilitation Using the Leap Motion – Conference paper

Abstract

Wrist and hand rehabilitation are common as people suffer injuries during work and exercise. Typically, the rehabilitation involves the patient and the therapist, which is both time consuming and cost burdening. It is desirable to use advanced telemedicine technologies such that the patient is able to enjoy the freedom of performing the required exercise at their own time and pace, while the healthcare system can operate more efficiently. The Leap Motion Controller (LMC), an inexpensive motion detection device, seems to be a good candidate for remote wrist rehabilitation. In this paper, the functionality and capability of the LMC are examined. Experiments are carried out with a total of twelve people performing twelve different movements. From the experimental results, the feasibility of using the LMC as a rehabilitation device is discussed.

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