Deep gesture interaction for augmented anatomy learning

Karambakhsh, Ahmad; Kamel, Aouaidjia; Sheng, Bin; Li, Ping; Yang, Po; Feng, David Dagan

doi:10.1016/j.ijinfomgt.2018.03.004

Cited by 74 publications

(28 citation statements)

References 18 publications

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“…The research by Sodhro et al further shows that the combination of existing big data and computers to monitor data and provide medical services remotely has and will continue to have a positive and significant effect [40]. In addition, many other works of literature have proved the role of big data and algorithms in precision medicine [41,42].…”

Section: Big Data and Medical Innovationmentioning

confidence: 99%

Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation

Liu

Shu-yan

Ding

2020

IJERPH

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Medical innovation has consistently been an essential subject and a source of support for public health research. Furthermore, improving the level of medical research and development is of great concern in this field. This paper highlights the role of big data in public medical innovation. Based on a sample of China’s listed firms in the medical industry from 2013 to 2018, this paper explores the exogenous shock effect of China’s big data medical policy. Results show that the construction of the medical big data platform effectively promotes innovation investment and the innovation patent of medical firms. In addition, the heterogeneity of this promoting effect is reflected in firm size through the overcoming of different innovation bottlenecks. The research conclusions support the positive significance of the macro-led implementation of the medical big data platform, and suggest that the positive economic externalities generated by this policy are critical to public health.

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Section: Big Data and Medical Innovationmentioning

confidence: 99%

Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation

Liu

Shu-yan

Ding

2020

IJERPH

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“…The convolutional neural network is a model that is used to process object recognition. In 2012, CNN is becoming a model that really important to support object recognition [13]. CNN also works well to do adaptive multi-modal and shows it's a great power on image recognition [13].…”

Section: Cnn (Convolutional Neural Network)mentioning

confidence: 99%

BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using CNN and LSTM

Aljabar¹,

Suharjito²

2020

Adv. sci. technol. eng. syst. j.

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Sign language is one of the languages which are used to communicate with deaf people. By using it, they can communicate and understand each other. In Indonesia, there are two standards of sign language which are SIBI (Sistem Bahasa Isyarat) and BISINDO (Bahasa Isyarat Indonesia). Deep learning is a model that is used to apply to this topic. In this model, there are a lot of methods such as convolutional neural network, recurrent neural network, long-sort term memory, and each model has its characteristics. There are also some issues in deep learning by sign language recognition as the object such as data training, object position, pose, lighting, and the background of objects. This research will describe how to combine background subtraction and gaussian blur pre-processing, forwarding preprocessing background subtraction with CNN by using BISINDO, LSTM, and a combination between CNN and LSTM. In conclusion, this research shows that a combination between CNN and LSTM is the best model by explaining the accuracy and testing with sign language BISINDO as the object. The accuracy showed that for CNN 96%, LSTM 86%, and combination CNN and LSTM 96%, and the loss showed that for CNN 18%, LSTM 41%, and combination CNN and LSTM 17%.

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“…Ahmad Karambakhsha et al [22] combined trained neural network (CNN) with augmented reality in order to facilitate anatomy learning in medical field. Input source in this method were gestures which can be recognized by RGB-d camera followed by 3D path tracking.…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Literature Review of Research Methodologies Used for Evaluation of Augmented Reality Based Learning Applications

Sirshar¹,

Baig²,

Ali³

2019

Preprint

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With the advancement in AR technology, more education-based applications are being developed using Augmented Reality, which has revolutionized the learning experience. However, in order to determine the application’s impact on student’s motivation, performance and their communication with the lecturer, various studies are conducted. These studies use one of the three research methodologies for data analysis and evaluation. In this systematic review, we have analyzed various research methodologies for system evaluation of the AR learning applications and recorded the student response toward the system. Also, we checked which methodology is preferred by researchers and why. A total number of 25 studies were analyzed which were published during the year of 2015 and 2019. The results indicate that most popular research technique is mixed methodology as it combines both qualitative and quantitative techniques. The purpose of this review is to offer new insights to researchers and provide them with advice about evaluation of AR applications and which tool or technique is more effective.

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Deep gesture interaction for augmented anatomy learning

Abstract: The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription.

Cited by 74 publications

References 18 publications

Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation

Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation

BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using CNN and LSTM

A Systematic Literature Review of Research Methodologies Used for Evaluation of Augmented Reality Based Learning Applications

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