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Title: A Scoping Review of Assistance and Therapy with Head-Mounted Displays for People Who Are Visually Impaired
Given the inherent visual affordances of Head-Mounted Displays (HMDs) used for Virtual and Augmented Reality (VR/AR), they have been actively used over many years as assistive and therapeutic devices for the people who are visually impaired. In this paper, we report on a scoping review of literature describing the use of HMDs in these areas. Our high-level objectives included detailed reviews and quantitative analyses of the literature, and the development of insights related to emerging trends and future research directions. Our review began with a pool of 1251 papers collected through a variety of mechanisms. Through a structured screening process, we identified 61 English research papers employing HMDs to enhance the visual sense of people with visual impairments for more detailed analyses. Our analyses reveal that there is an increasing amount of HMD-based research on visual assistance and therapy, and there are trends in the approaches associated with the research objectives. For example, AR is most often used for visual assistive purposes, whereas VR is used for therapeutic purposes. We report on eight existing survey papers, and present detailed analyses of the 61 research papers, looking at the mitigation objectives of the researchers (assistive versus therapeutic), the approaches used, the more » types of HMDs, the targeted visual conditions, and the inclusion of user studies. In addition to our detailed reviews and analyses of the various characteristics, we present observations related to apparent emerging trends and future research directions. « less
Authors:
; ; ; ; ; ;
Award ID(s):
1800961 1564065
Publication Date:
NSF-PAR ID:
10343483
Journal Name:
ACM Transactions on Accessible Computing
ISSN:
1936-7228
Sponsoring Org:
National Science Foundation
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It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition [3], image recognition [4] and text processing [5] are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do not have access to such data resources must rely on techniques in which existing models can be adapted to new datasets [6]. A preliminary version of this breast corpus release was tested in a pilot study using a baseline machine learning system, ResNet18 [7], that leverages several open-source Python tools. The pilot corpus was divided into three sets: train, development, and evaluation. Portions of these slides were manually annotated [1] using the nine labels in Table 1 [8] to identify five to ten examples of pathological features on each slide. 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Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432. [2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/. [3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015. [4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201. [5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021]. [6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/. [7] J. Picone, T. Farkas, I. Obeid, and Y. 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