# Superpower Glass: Delivering Unobtrusive Real-time Social Cues in Wearable Systems

arXiv: https://arxiv.org/abs/2002.06581 · HTML: https://mlsanity.com/paper/2002.06581/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging](https://mlsanity.com/paper/2101.03478/) (2021)
- [A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism](https://mlsanity.com/paper/2303.03577/) (2023)
- [Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study](https://mlsanity.com/paper/2108.07917/) (2021)
- [Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels](https://mlsanity.com/paper/2101.03477/) (2021)
- [Training and Profiling a Pediatric Emotion Recognition Classifier on Mobile Devices](https://mlsanity.com/paper/2108.11754/) (2021)
- [Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images](https://mlsanity.com/paper/2201.11197/) (2022)
- [Computer Vision Estimation of Emotion Reaction Intensity in the Wild](https://mlsanity.com/paper/2303.10741/) (2023)
- [Self-Supervised Learning for Audio-Based Emotion Recognition](https://mlsanity.com/paper/2307.12343/) (2023)
- [An Exploration of Active Learning for Affective Digital Phenotyping](https://mlsanity.com/paper/2204.01915/) (2022)
- [Personalization of Stress Mobile Sensing using Self-Supervised Learning](https://mlsanity.com/paper/2308.02731/) (2023)
- [Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study](https://mlsanity.com/paper/2311.12812/) (2023)
- [Cripping AI: Reimagining AI Through Lived Disability Experiences](https://mlsanity.com/paper/2605.02080/) (2026)

## Abstract (arXiv)

We have developed a system for automatic facial expression recognition, which runs on Google Glass and delivers real-time social cues to the wearer. We evaluate the system as a behavioral aid for children with Autism Spectrum Disorder (ASD), who can greatly benefit from real-time non-invasive emotional cues and are more sensitive to sensory input than neurotypically developing children. In addition, we present a mobile application that enables users of the wearable aid to review their videos along with auto-curated emotional information on the video playback bar. This integrates our learning aid into the context of behavioral therapy. Expanding on our previous work describing in-lab trials, this paper presents our system and application-level design decisions in depth as well as the interface learnings gathered during the use of the system by multiple children with ASD in an at-home iterative trial.