AI Article Synopsis

  • This study explored a digital game called Lookware™, designed to help children with autism spectrum disorder (ASD) improve their emotion recognition skills using behavior analysis and eye tracking.
  • Children aged 4-14 were split into two groups: one played Lookware™ while the other played a control game.
  • Results showed that those who played Lookware™ had significantly better emotion recognition skills after the program, and both children and staff found the game to be feasible and acceptable.

Article Abstract

This study examined the feasibility, acceptability, and efficacy of a video game-based digital therapeutic combining applied behavior analysis techniques and gaze-contingent eye tracking to target emotion recognition in youth with autism spectrum disorder (ASD). Children aged 4-14 years with ASD were randomized to complete Lookware™ (n = 25) or a control video game (n = 29). Results from a 2 × 2 mixed ANOVA revealed that children in the intervention condition demonstrated significant improvements in emotion recognition from pre- to post-intervention compared to children in the control condition, F(1,52) = 17.48, p < 0.001. Children and staff perceived high feasibility and acceptability of Lookware™. Study results demonstrated the feasibility, acceptability, and preliminary efficacy of Lookware™.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10803-021-05101-wDOI Listing

Publication Analysis

Top Keywords

emotion recognition
12
digital therapeutic
8
applied behavior
8
behavior analysis
8
gaze-contingent eye
8
eye tracking
8
autism spectrum
8
spectrum disorder
8
report novel
4
novel digital
4

Similar Publications

Background: Mental health disorders are one of the leading causes of illness globally. The importance of psychosocial skills acquired in early childhood, such as executive functions, inhibitory control, emotional regulation, and social problem-solving, in preventing mental disorders has been reported. Furthermore, mental health care delivery is evolving, and mobile technology is becoming the medium for assessment and intervention.

View Article and Find Full Text PDF

Emotion Recognition Using PPG Signals of Smartwatch on Purpose of Threat Detection.

Sensors (Basel)

December 2024

School of AI Convergence, Sungshin Women's University, 34 da-gil 2, Bomun-ro, Seongbuk-gu, Seoul 02844, Republic of Korea.

This paper proposes a machine learning approach to detect threats using short-term PPG (photoplethysmogram) signals from a commercial smartwatch. In supervised learning, having accurately annotated training data is essential. However, a key challenge in the threat detection problem is the uncertainty regarding how accurately data labeled as 'threat' reflect actual threat responses since participants may react differently to the same experiments.

View Article and Find Full Text PDF

Background And Objective: Cardiovascular disease (CVD), one of the chronic non-communicable diseases (NCDs), is defined as a cardiac and vascular disorder that includes coronary heart disease, heart failure, peripheral arterial disease, cerebrovascular disease (stroke), congenital heart disease, rheumatic heart disease, and elevated blood pressure (hypertension). Having CVD increases the mortality rate. Emotional stress, an indirect indicator associated with CVD, can often manifest through facial expressions.

View Article and Find Full Text PDF

The extraction and analysis of pitch underpin speech and music recognition, sound segregation, and other auditory tasks. Perceptually, pitch can be represented as a helix composed of two factors: height monotonically aligns with frequency, while chroma cyclically repeats at doubled frequencies. Although the early perceptual and neurophysiological mechanisms for extracting pitch from acoustic signals have been extensively investigated, the equally essential subsequent stages that bridge to high-level auditory cognition remain less well understood.

View Article and Find Full Text PDF

The relationships between facial expression and color affect human cognition functions such as perception and memory. However, whether these relationships influence selective attention and brain activity contributed to selective attention remains unclear. For example, reddish angry faces increase emotion intensity, but it is unclear whether brain activity and selective attention are similarly enhanced.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!