Don't Bother Me. I'm Socializing!

Park, Chunjong; Lim, Jaechang; Kim, Juho; Lee, Sung-Ju; Lee, Dongman

doi:10.1145/2998181.2998189

Cited by 59 publications

(8 citation statements)

References 38 publications

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“…One major factor should be taken into consideration when designing proactive speakers: 1) timing and 2) context. Proactive services, such as push notifcations, often have an interruptive element and delivering notifcations at opportune moments can minimize that interruption [14]. Compared with smartphones, smart speakers are more interruptive: voice is more intrusive as it can hardly be ignored (unlike a silent alarm or vibration in the user's pocket).…”

Section: Introductionmentioning

confidence: 99%

Developing the Proactive Speaker Prototype Based on Google Home

Wei

Dingler

Kostakos

2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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Smart speakers and conversational interfaces increasingly make it into consumer's homes. Listening to users' commands assigns them a rather passive role. Proactive speakers, on the other hand, have the potential to empower a broad range of applications such as context-aware health interventions and self-tracking systems. To achieve proactivity, however, requires the speaker to become context-aware and be able to detect opportune moments to initiate interactions. In this work, we present a proactive speaker prototype based on Google Home to investigate interaction contexts. We, additionally, built a voice-based Experience Sampling Method (ESM) application to study contextual factors that are correlated to opportune moments for device-triggered interactions. Preliminary results from a three-week feld study with 7 participants indicate the proposed prototype is a robust way to achieve proactivity and implement voice-based ESM. We present use cases for future research and applications for proactive smart speakers in the context of digital health. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI).

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Section: Introductionmentioning

confidence: 99%

Developing the Proactive Speaker Prototype Based on Google Home

Wei

Dingler

Kostakos

2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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“…We designed three distinct in-the-moment label collection rules: entry-moment, leaving-moment, and during usage. Previous studies in smartphone notification management [28,32,69] showed the effectiveness of breakpoint-based notification delivery. Inspired by these works, we align optimal notification instances with task-switching moments, corresponding to accessing (i.e., entry-moment) and leaving a monitored app (i.e., leaving-moment prompts).…”

Section: Machine Learning For Smartphone Overuse Predictionmentioning

confidence: 99%

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention

Orzikulova,

Xiao,

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explainable JITAI system that leverages machine learning to identify optimal intervention timings, introduces interventions with transparent AI explanations, and collects user feedback to establish a human-AI loop and adapt the intervention model over time. We conducted an 8-week field experiment (N=71) to evaluate the effectiveness of both the adaptation and explanation aspects of Time2Stop. Our results indicate that our adaptive models significantly outperform the baseline methods on intervention accuracy (>32.8% relatively) and receptivity (>8.0%). In addition, incorporating explanations further enhances the effectiveness by 53.8% and 11.4% on accuracy and receptivity, respectively. Moreover, Time2Stop significantly reduces overuse, decreasing app visit frequency by 7.0∼8.9%. Our subjective data also echoed these quantitative measures. Participants preferred the adaptive interventions and rated the system highly on intervention

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“…Digital environments complicate the discussion of I/identity because of the creation of and connection to virtual personae and spaces. Prensky's (2001Prensky's ( , 2006 identification of digital natives and digital immigrants calls attention to the emotional chasm and power structure inherent in the difference between being reared in a digital world and entering it as an adult. As digital immigrant Norton-Meir (2005) notes, "We must realize that our students think and process information differently than we do" (p. 430), and rethinking identity and literacy education involves the discussion of student and educator practices and I/identity development in, or stemming from, digital environments.…”

Section: I/identity and Digital Environmentsmentioning

confidence: 99%