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1.
Educ Technol Res Dev ; 70(1): 205-230, 2022.
Article in English | MEDLINE | ID: mdl-35035182

ABSTRACT

Parents recognize the potential benefits of technology for their young children but are wary of too much screen time and its potential deficits in terms of social engagement and physical activity. To address these concerns, related literature suggests technology usages with a blend of digital and physical learning experiences. Towards this end, we developed Kid Space, incorporating immersive computing experiences designed to engage children more actively in physical movement and social collaboration during play-based learning. The technology features an animated peer learner, Oscar, who aims to understand and respond to children's actions and utterances using extensive multimodal sensing and sensemaking technologies. To investigate student engagement during Kid Space learning experiences, an exploratory case study was designed using a formative research method with eight first-grade students. Multimodal data (audio and video) along with observational, interview, and questionnaire data were collected and analyzed. The results show that the students demonstrated high levels of engagement, less attention focused on the screen (projected wall), and more physical activity. In addition to these promising results, the study also enabled us to understand actionable insights to improve Kid Space for future deployments (e.g., the need for real-time personalization). We plan to incorporate the lessons learned from this preliminary study and deploy Kid Space with real-time personalization features for longer periods with more students.

2.
JMIR Mhealth Uhealth ; 8(12): e20625, 2020 12 18.
Article in English | MEDLINE | ID: mdl-33337336

ABSTRACT

BACKGROUND: Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. OBJECTIVE: The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. METHODS: The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. RESULTS: Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered "unhealthy" and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. CONCLUSIONS: The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.


Subject(s)
Ecological Momentary Assessment , Meals , Feeding Behavior , Humans , Surveys and Questionnaires
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