Multidimensional Earcon Interaction Design for The Blind: a Proposal and Evaluation

Bustoni, Isna Alfi; Hidayatulloh, Indra; Sn, Azhari; Augoestin, Nia Gella

doi:10.1109/isriti.2018.8864487

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Meanwhile, according to a study conducted by Survei Sosial Ekonomi Nasional (Susenas) in 2012, there were over six million people in Indonesia [3]. There are currently insufficient facilities to meet the requirements of people with disabilities [4,5]. One of them is the requirement for a companion to monitor their activities.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

et al. 2022

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There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation.

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Section: Introductionmentioning

confidence: 99%

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

et al. 2022

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“…It would be better if this game can be developed further. By providing new interactive features such as multimodal earcon interaction [9] might lead to the possibility that this game can be also available for blind or visually impaired users. So, they can also learn English through the English Challenge game.…”

Section: Discussionmentioning

confidence: 99%

Designing of english challenge mobile game application as the media of english language learning

Setyadharma¹,

Iryanti²,

Hidayatulloh

et al. 2020

JEATech

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English became the first foreign language that came to Indonesia in 1967. The human needs of English can already be seen through lots of official scientific works in English and its intense competitions globally. However, still, many people finds it difficult to learn English, for example, the difficulty in understanding tenses, differences between writing, reading and pronouncing. By relying on the latest features on smartphone, which on the mean time, smartphone is one of many gadgets that is the most practical to use in anywhere and anytime, plus, with gaming contents as the best multimedia entertainer application, learning English will be more interactive and easier to use. In this research, Construct 2 is used as The Developer App and Game Development Life Cycle (GDLC) method of Rido Ramadan and Yani Widyani’s version is used as The Development Method. The game system tested by using black box and by giving out Questionnaire for User Interface Satisfaction to 24 respondents, to test the system’s usability. It resulted 80,83% for the whole of the system, meaning that the whole system is quiet interesting in the respondents’s view. Then, the screen display gained 80,41%, this means that the screen display is very interesting and did not confuse the respondents. Technology and information game gained 75,41%, this means that the information contained in the game is quiet good, however, further development is needed. The introduction of the game’s system gained 71,63%, means that some of the system’s ability did not run effectively and needed further repairment and development. The last, usability and user interface gained 76,25%, this means that the game is quiet interactive to the respondents.

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“…Likewise, to assist the student with a disability like visual disability, the researcher can consider an audio-based chatbot. The audio-based chatbot can be a complete voice assistant or earcon-based for a particular menu [61].…”

Section: Gamification Feature Challengesmentioning

confidence: 99%

Gamification on Chatbot-Based Learning Media: a Review and Challenges

Hidayatulloh

Pambudi

Surjono

et al. 2021

ELINVO

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The mobile learning sector has exploded, implying that the e-Learning trend is shifting to mobile platforms. As a result, chatbots have become increasingly popular alternatives for online learning and examinations on mobile platforms. However, it did not provide enough motivation for the student. On the other hand, gamification in a typical e-Learning platform is a widely used technique for increasing students' learning motivation. Therefore, combining gamification with chatbot-based learning and examinations possibly offer benefits, including increase student learning motivation. This study explored the possibilities and future challenges of the development of gamification within chatbot-based learning media. We discussed four aspects: architecture's reliability, security and privacy issue, user’s acceptance and motivation, and gamification feature challenges.

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Multidimensional Earcon Interaction Design for The Blind: a Proposal and Evaluation

Cited by 6 publications

References 17 publications

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

Designing of english challenge mobile game application as the media of english language learning

Gamification on Chatbot-Based Learning Media: a Review and Challenges

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