Application of Non-Immersive Virtual Reality in Cerebral Palsy Children: A Systematic Review

IF 3 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Agrawal Luckykumar Dwarkadas, Viswanath Talasila, Sheeraz Kirmani, Rama Krishna Challa, K. G. Srinivasa
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Abstract

Cerebral palsy (CP) is a movement disorder caused by brain damage. Virtual reality (VR) can improve motor function and daily life activities in CP patients. This systematic review examines the use of non-immersive VR in treating CP children. The objective is to evaluate the effectiveness of non-immersive VR in rehabilitating CP children as a standalone intervention or in combination with traditional therapy. The review follows the PRISMA guidelines and includes a comprehensive search of five bibliographic databases. Two reviewers independently assess the search results, evaluate full-text publications, and extract relevant data. The outcomes were described by the International Classification of Functioning, Disability, and Health for Children and Youth (ICF-CY) framework. A total of 20 English-language studies published between January 2013 and January 2023 are included in the review based on predefined inclusion and exclusion criteria. The findings demonstrate that non-immersive VR, when used in conjunction with traditional therapy, yields positive effects on body structure and function (hand function, grip strength, and upper extremity function), activity (motor function, activities of daily life [ADL], balance), and participation (caretakers' assessment, usability, motivation, and user satisfaction) in CP children. Moreover, non-immersive VR alone is found to be more efficient than traditional therapy in improving manual dexterity in CP children. The non-immersive VR can be effective in rehabilitating CP children, and the review concludes by recommending future research with larger sample sizes and randomized trials to investigate further the potential benefits of non-immersive VR in this population.

非沉浸式虚拟现实技术在脑瘫儿童中的应用:系统回顾
脑性瘫痪(CP)是一种由脑损伤引起的运动障碍。虚拟现实(VR)可以改善 CP 患者的运动功能和日常生活活动。本系统综述研究了非沉浸式 VR 在治疗 CP 儿童中的应用。目的是评估非沉浸式 VR 作为一种独立的干预措施或与传统疗法相结合对 CP 儿童康复的有效性。综述遵循 PRISMA 指南,包括对五个文献数据库的全面检索。两名审稿人独立评估搜索结果、评价全文出版物并提取相关数据。研究结果采用《国际儿童和青少年功能、残疾和健康分类》(ICF-CY)框架进行描述。根据预定义的纳入和排除标准,本综述共纳入了 2013 年 1 月至 2023 年 1 月间发表的 20 篇英文研究。研究结果表明,非沉浸式 VR 与传统疗法结合使用时,可对 CP 儿童的身体结构和功能(手部功能、握力和上肢功能)、活动(运动功能、日常生活活动 [ADL]、平衡)和参与(护理人员评估、可用性、动机和用户满意度)产生积极影响。此外,与传统疗法相比,非沉浸式虚拟现实疗法在改善 CP 儿童的手部灵活性方面更为有效。非沉浸式虚拟现实技术可有效帮助 CP 儿童康复,综述最后建议今后开展样本量更大的研究和随机试验,以进一步调查非沉浸式虚拟现实技术在这一人群中的潜在益处。
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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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