Development of a Diagnosis and Evaluation System for Hemiplegic Patients Post-Stroke Based on Motion Recognition Tracking and Analysis of Wrist Joint Kinematics

Abstract

An inexperienced therapist lacks the analysis of a patient’s movement. In addition, the patient does not receive objective feedback from the therapist due to the visual subjective judgment. The aim is to provide a guide for in-depth rehabilitation therapy in virtual space by continuously tracking the user’s wrist joint during Leap Motion Controller (LMC) activities and present the basic data to confirm steady therapy results in real-time. The conventional Box and Block Test (BBT) is commonly used in upper extremity rehabilitation therapy. It was modeled in proportion to the actual size and Auto Desk Inventor was used to perform the 3D modeling work. The created 3D object was then implemented in C # through Unity5.6.2p4 based on LMC. After obtaining a wrist joint motion value, the motion was analyzed by 3D graph. Healthy subjects (23 males and 25 females, n = 48) were enrolled in this study. There was no statistically significant counting difference between conventional BBT and system BBT. This indicates the possibility of effective diagnosis and evaluation of hemiplegic patients post-stroke. We can keep track of wrist joints, check real-time continuous feedback in the implemented virtual space, and provide the basic data for an LMC-based quantitative rehabilitation therapy guide.

Leap Motion Controller Video Game-Based Therapy for Upper Extremity Motor Recovery in Patients with Central Nervous System Diseases. A Systematic Review with Meta-Analysis

“…Our results can complement the previous findings, support a guarantee to use LMC to improve GMD in subjects with cerebral palsy and other non-acute CNSDs and suggest the use of LMC with one of the best non-immersive VR devices, without manual contact, to be used in the neurorehabilitation of UE in patients with CNSDs. Unlike other VR devices, such as the Kinect ® , which are more geared towards reproducing global movements while standing to maintain balance [ 39 ], LMC is a highly accurate and validated device to primarily rehabilitate the damaged UE [ 41 , 42 ].…”

“…The LMC generates a virtual representation of the UE on the computer screen and indicates to the patient what task should be performed [ 35 ]. Compared to other motion capture systems, such as Kinect ® (Microsoft Corp., Redmond, WA, USA), which is the most widely used body recognition device in balance and gait analysis [ 39 ], LMC shows several advantages, including its low cost [ 34 ], its small size, its ease of use and installation [ 40 ], and the wide variety of engineering applications that can be used, such as physical rehabilitation and assessment [ 41 , 42 ] and medical education [ 43 ]. Several studies have assessed the accuracy of manual motion tracking using LMC [ 44 , 45 ].…”

Comparison of the Performance of the Leap Motion ControllerTM with a Standard Marker-Based Motion Capture System

“…It is used in a myriad of different scenarios, for example human-robot interaction [ 1 , 2 , 3 ], surgery training [ 4 ] and gesture recognition [ 5 , 6 ]. The LMC has also found application in aiding hand rehabilitation; for example, it has been used as a development and evaluation system for post-stroke patients [ 7 ] and used for video-game-based therapy in the upper extremities [ 8 , 9 , 10 ]. In another study [ 11 ], the LMC was used to investigate hand tremor, a common movement disorder, often found in patients with dystonia, Parkinson’s disease, and cerebellar ataxia.…”

Diferenciação de pacientes cirróticos com e sem encefalopatia hepática a partir da análise de padrões motores finos: Um estudo piloto com o Leap Motion Controller

“…Embora haja uma diferença de visão entre os autores quanto ao quesito de aplicabilidade, a depender das condições em que se insere, ambos os trabalhos chegaram a valores de precisão sempre inferiores a 1 cm. As variações com tendência de C > B > A em 87,5% dos casos apresentados nesse estudo corroboram outros estudos que empregaram o sensor e obtiveram resultados promissores (Guzsvinecz, Szucs & Sik-Lanyi, 2019;Kim, Park & Lee, 2020;Colombini et al, 2021).…”

“…Outro estudo, desta vez de Vysocký et al (2020), também realizou uma investigação dos atributos do sensor, destacando suas especificidades técnicas, benefícios e dando indicações de aplicação para a obtenção de dados confiáveis na Research, Society and Development, v. 10, n. 7, e48310716749, 2021 (CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi.org/10.33448/rsd-v10i7.16749 caixa de interação do dispositivo. Nota-se a considerável parcela de trabalhos que utilizaram o LMC em pacientes com patologias do SNC, principalmente os acometidos por AVC, a exemplo de trabalhos recentes como de Wang et al (2017), Cortés-Perez et al (2021, Kim, Park & Lee (2020).…”