Using implicit user feedback to balance energy consumption and user comfort of proximity-controlled computer screens

Garcia, P.A. Jaramillo; Gonzalez, Luis I. Lopera; Amft, Oliver

doi:10.1007/s12652-014-0222-2

Cited by 13 publications

(2 citation statements)

References 19 publications

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“…Ambient monitoring methods could detect the presence in front of a desktop screen, e.g., camera-based face detection or ultrasound-based proximity detection (Jaramillo-Garcia et al, 2014). However, screen-based light intensity must be assigned to an individual user to implement alerts, where wearable systems are advantageous.…”

Section: Practical Applicationsmentioning

confidence: 99%

Computer Screen Use Detection Using Smart Eyeglasses

2017

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Screen use can influence the circadian phase and cause eye strain. Smart eyeglasses with an integrated color light sensor can detect screen use. We present a screen use detection approach based on a light sensor embedded into the bridge of smart eyeglasses. By calculating the light intensity at the user's eyes for different screens and content types, we found only computer screens to have a significant impact on the circadian phase. Our screen use detection is based on ratios between color channels and used a linear support vector machine to detect screen use. We validated our detection approach in three studies. A test bench was built to detect screen use under different ambient light sources and intensities in a controlled environment. In a lab study, we evaluated recognition performance for different ambient light intensities. By using participant-independent models, we achieved an ROC AUC above 0.9 for ambient light intensities below 200 lx. In a study of typical ADLs, screen use was detected with an average ROC AUC of 0.83 assuming screen use for 30% of the time.

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Section: Practical Applicationsmentioning

confidence: 99%

Computer Screen Use Detection Using Smart Eyeglasses

2017

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“…First and foremost, we need to understand what method or "housing" is feasible for extended wear while maintaining proper sensor placement. Second, to distinguish mere presence of a screen from looking at a screen, light measurements need to be taken as close as possible to the subject's eyes (28,29). Head-placed wearables have been proposed to robustly detect light received at eye level.…”

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confidence: 99%

Feasibility and Acceptability of Wearable Sensor Placement for Measuring Screen Time of Children

et al. 2022

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Electronic screens (e.g., TV, computers, tablets, smartphones) are now ubiquitous in the lives of children. Research suggests that electronic screen use may offer both benefits (e.g., early learning, social contact and support) and risks (e.g., low physical activity/fitness, poor sleep quality, obesity) to the overall well-being of children (1-10). However, the ability to assess the relationship of screen exposure to behavioral and health outcomes is plagued by multiple measurement issues (11,12). To date, studies of electronic screen use in children have relied largely on parental or caregiver self-report, which is susceptible to high levels of error and is limited in its ability to capture the short bouts of screen use that are characteristic of newer media (e.g., smartphones) (13-21). Some groups have developed smartphone apps or TV/computer allowance devices that measure screen usage more objectively (22-24), but these systems often require instrumenting every screen in a person's environment, thus severely limiting real-world application and generalizability. A more precise, scalable, and cost-effective measure of screen use is needed to understand and evaluate screen time effects on child behaviors and health outcomes.

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