Narratives and sensor driven cognitive behavior training game platform

Park, Kyung-Eun; Kihl, Taesuk; Park, Seungie; Kim, Minjae; Chang, Juno

doi:10.1109/sera.2016.7516137

Cited by 12 publications

(2 citation statements)

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“…These sensors rely on real time data to guide a visual PC interface. Park et al developed a game leveraging the data from electroencephalograms (EEGs) to guide game elements (45).…”

Section: Discussionmentioning

confidence: 99%

The Use of Mobile Games in the Management of Patients With Attention Deficit Hyperactive Disorder: A Scoping Review

et al. 2022

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Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder associated with significant morbidity. Current treatment approaches consist of a mixture of pharmacological and psychological approaches. The emergence of digital technology, and mobile gaming applications, represents a promising novel method in potentially augmenting existing interventions for ADHD. In this review, we will map out the use of mobile gaming applications in the management of ADHD and evaluate the effectiveness of these technologies and any areas for future research. Four electronic databases were searched for relevant articles. All articles were screened for abstract and full text by two independent reviewers, and data extracted onto a common data extraction sheet. The data was narratively synthesized and reported in line with the PRISMA-ScR guidelines. A total of 19 studies were included. Studies mostly evaluated the effectiveness of games on male children with ADHD. Most games were focused on the treatment of ADHD, while a minority were focused on the diagnosis and monitoring of ADHD. Some of the common gaming mechanisms employed in games included having participants responding to cures, remembering details, and making associations between different entities. The studies generally showed an improvement in performance of children as they played the games, but evidence for the effectiveness of these modalities remains scarce and mixed. While it is exciting that there is such a wide variety of games available currently in the diagnosis, treatment, and monitoring of ADHD, many of the games lack clinical evidence to prove their effectiveness. Furthermore, most studies contain several limitations including small sample size, limited ages of participants, lack of control group, and lack of comprehensive outcomes. To promote the application of these games to clinical practice, robust clinical trials, collaboration between stakeholders and using a comprehensive set of outcome measurements is essential.

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“…These sensors rely on real time data to guide a visual PC interface. Park et al developed a game leveraging the data from electroencephalograms (EEGs) to guide game elements (45).…”

Section: Discussionmentioning

confidence: 99%

The Use of Mobile Games in the Management of Patients With Attention Deficit Hyperactive Disorder: A Scoping Review

et al. 2022

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“…The behaviors of adults have fixed patterns and take place in regular locations. In contrast, the behaviors of school-age children carry some distinctive features [1][2][3][4]. The daily activities of school-age children often take place in open spaces like schools and streets.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Analysis of Behavior Features of School-Age Children Based on Video Image Processing

Zhen¹

2020

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School-age children have vastly different behavior features from adults. Most of the relevant studies are theoretical summaries of behavior features of these children, failing to detect the behaviors or recognize the behavior features in an accurate manner. To solve the problem, this paper puts forward a novel method to recognize the behavior features of school-age children through video image processing. Firstly, the authors designed a method to extract static behavior features of school-age children from surveillance video images. Next, the behavior features of school-age children were extracted by optical flow method. On this basis, a dual-network flow neural network (DNFNN) was designed, in which the time flow network processes the dense optical flow of multiple continuous frames of the surveillance video, while the spatial flow network treats the region of interest (ROI) in the static frame from the video. After that, the workflow of the DNFNN was introduced in details. Experimental results fully demonstrate the effectiveness of the proposed network. The research findings provide a reference for the application of video image processing to behavior recognition in other fields.

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