Designing a robot-assisted therapy for individuals with anxiety traits and states

Aziz, Azizi Ab; Ahmad, Faudziah; Yusof, Nooraini; Ahmad, Farzana Kabir; Yusof, Shahrul Azmi Mohd

doi:10.1109/isamsr.2015.7379778

Cited by 8 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the pilot study by Sung et al (2015) about robot-assisted therapy using socially assistive pet robot (PARO), institutionalized older adults showed significantly increased communication and interaction skills and activity participation after receiving 4-week robot-assisted therapy. Another robotic application is to provide intervention to enhance people's impaired cognitive function, such as memory (Paletta et al, 2018), attention (Lins et al, 2019) and concentration (Tleubayev et al, 2019), or reduce their negative psychophysiological feelings, such as stress (Aminuddin et al, 2016) and anxiety (Ab Aziz et al, 2015). Additionally, a few studies adopted the robots to facilitate learning and educational activities for people with cognitive disabilities, such as children with dyslexia (Andruseac et al, 2015) or Traumatic Brain Injury (TBI) (Barco Martelo and Fosch Villaronga, 2017).…”

Section: Applicationsmentioning

confidence: 99%

A Systematic Review of Robotic Rehabilitation for Cognitive Training

et al. 2021

View full text Add to dashboard Cite

A large and increasing number of people around the world experience cognitive disability. Rehabilitation robotics has provided promising training and assistance approaches to mitigate cognitive deficits. In this article, we carried out a systematic review on recent developments in robot-assisted cognitive training. We included 99 articles in this work and described their applications, enabling technologies, experiments, and products. We also conducted a meta analysis on the articles that evaluated robot-assisted cognitive training protocol with primary end users (i.e., people with cognitive disability). We identified major limitations in current robotics rehabilitation for cognitive training, including the small sample size, non-standard measurement of training and uncontrollable factors. There are still multifaceted challenges in this field, including ethical issues, user-centered (or stakeholder-centered) design, the reliability, trust, and cost-effectiveness, personalization of the robot-assisted cognitive training system. Future research shall also take into consideration human-robot collaboration and social cognition to facilitate a natural human-robot interaction.

show abstract

Section: Applicationsmentioning

confidence: 99%

A Systematic Review of Robotic Rehabilitation for Cognitive Training

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Diverse categorization of ED methods is presented by several authors (Atefeh and Khreich, 2015;Lavanya et al, 2014;Panagiotou et al, 2016;Zarrinkalam and Bagheri, 2017). The scholars of this paper have adapted the classifications proposed by previous researchers and categorize ED methods into supervised and unsupervised methods as it is shown in Many supervised ED methods have been developed and presented throughout the time in literature to achieve different objectives (Ab Aziz et al, 2016;Mustaffa et al, 2014;Yusof et al, 2015). Sakaki et al (2010) applied Support Vector Machine (SVM) classifier to detect earthquake and forecast earthquake's center in real-time from Twitter dataset.…”

Section: Event Detection Methodsmentioning

confidence: 99%

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

Self Cite

View full text Add to dashboard Cite

Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

show abstract

“…Using of social networks as a source to consume news and other information is a double-edged sword. On one side, people can reach news on social networks easily, more cheaply than the traditional news media, and they can diffuse their information faster [3], [4]. On the other edge, the online social networks provide a perfect environment for generating and disseminate of fake news.…”

Section: Introductionmentioning

confidence: 99%

Content-Social Based Features for Fake News Detection Model from Twitter

Hussein¹

2019

IJATCSE

View full text Add to dashboard Cite

The advancement of social networks has facilitated the sharing and spread of news among people over the world. With the growth of these networks and growth of the volume of the news shared daily, the phenomena of fake news become more stronger and widely spread. In this paper, content-social based features for fake news detection model from Twitter data has been proposed. This work aims to analyze content-based features of news content including of linguistic features, writing style features, semantic features and sentiment features along with social-context based features of news diffusion over the social network including user-based features and network-based features to detect fake news from Twitter news posts. With using of unsupervised graph-based clustering approach, no labelled data is required and this make the proposed model more practical to detect online fake news.

show abstract

Designing a robot-assisted therapy for individuals with anxiety traits and states

Cited by 8 publications

References 14 publications

A Systematic Review of Robotic Rehabilitation for Cognitive Training

A Systematic Review of Robotic Rehabilitation for Cognitive Training

A Survey on Event Detection Models for Text Data Streams

Content-Social Based Features for Fake News Detection Model from Twitter

Contact Info

Product

Resources

About