DrinkWatch

Flutura, Simon; Seiderer, Andreas; Aslan, Ilhan; Dang, Chi-Tai; Schwarz, Raphael; Schiller, Dominik; André, Elisabeth

doi:10.1145/3194658.3194666

Cited by 22 publications

(4 citation statements)

References 33 publications

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“…Additionally, interaction technology has to be used with caution in cars to not distract the driver. However, we believe that sensing technologies for automatically recognizing stress, and classifying emotions and activities (e.g., [10,12,20]) could improve a traffic companions impact on wellbeing. Also addressing other Positive Computing factors could help to increase wellbeing.…”

Section: Discussionmentioning

confidence: 99%

Traeddy

Martin

Geiger

Götz

et al. 2018

Proceedings of the Workshop on Human-Habitat for Health (H3): Human-Habitat Multimodal Interaction for Promoting Health and Wel

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Commuting by car can be stressful, especially unexpected traffic jams may result in feelings of loss of control and social disconnectedness. In this paper, we present Traeddy, a teddy bear augmented with embedded technology, which serves as a wellbeing companion for car commuters in case of traffic jams. Traeddy is capable to help, for example by notifying relevant contacts about traffic jams and potential delays. We describe in detail the design process, including 20 contextual inquiries and report the evaluation of Traeddy through an online survey with 102 participants and a field study evaluating Traeddy with three commuters and two contacts in the field. The results of the field study indicate that Traeddy has a positive impact on the relationship between the commuter and the notified contact. Furthermore, the majority of the online participants anticipated Traeddy to be useful and to support their wellbeing in traffic jams.

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Section: Discussionmentioning

confidence: 99%

Traeddy

Martin

Geiger

Götz

et al. 2018

Proceedings of the Workshop on Human-Habitat for Health (H3): Human-Habitat Multimodal Interaction for Promoting Health and Wel

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“…Recent developments in computing and design propagate designs for well-being (e.g., [7,15,37]), mindfulness (e.g., [3,47]), (soma)esthetic appreciation [21], mental and physical health (e.g., [9,13,35]). To this end, researcher build on foundations and advances in affective computing and user experience research and take inspirations from emerging (interdisciplinary) fields, such as positive computing [10], somaesthetics [38], and mindfulness based stress reduction (MBSR) [23].…”

Section: Somaesthetics In Hci Positive Computingmentioning

confidence: 99%

Towards Somaesthetic Smarthome Designs

Dang

Aslan

Lingenfelser

et al. 2019

Proceedings of the 9th International Conference on the Internet of Things

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Have you ever experienced a mirror that not only "sees" how you see yourself, but tells you how others perceive you? May such a smartmirror help not only adapt/improve oneself externally (look) but also internally (mood, emotions)? What are the expectations, fears (privacy)? The acceptance of such a smart thing requires approaches that go beyond technology-driven designs. In this paper, we report on the construction of an affective emotion-sensitive smartmirror as a smart piece of furniture for smarthomes and explorative studies with that system which address somaesthetics and aims to help inhabitants in self-reflection. The paper presents and discusses the prototype implementation and the results of a study in which the ideas, concerns, and experiences for/with the self-reflection capabilities of the affective smartmirror were explored by participants using the prototype as a technology probe.

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“…Wearable devices may facilitate real-time monitoring and tracking of fluid intake. Many wearable technologies have been used to monitor fluid intake, such as accelerometers, inertial sensors, smartwatches, cellphones, acoustic sensors, and electromyography sensors [9][10][11][12][13][14]. These items are widely available on the commercial market and have helped to identify drinking activities like drinking from different containers (cups, bottles, straws, and glass) and continuous or discrete volumes of fluid [6,15].…”

Section: Introductionmentioning

confidence: 99%

A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation

Ismail,

Niazi,

Haavik

et al. 2023

Sensors

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Dehydration is a common problem among older adults. It can seriously affect their health and wellbeing and sometimes leads to death, given the diminution of thirst sensation as we age. It is, therefore, essential to keep older adults properly hydrated by monitoring their fluid intake and estimating how much they drink. This paper aims to investigate the effect of surface electromyography (sEMG) features on the detection of drinking events and estimation of the amount of water swallowed per sip. Eleven individuals took part in the study, with data collected over two days. We investigated the best combination of a pool of twenty-six time and frequency domain sEMG features using five classifiers and seven regressors. Results revealed an average F-score over two days of 77.5±1.35% in distinguishing the drinking events from non-drinking events using three global features and 85.5±1.00% using three subject-specific features. The average volume estimation RMSE was 6.83±0.14 mL using one single global feature and 6.34±0.12 mL using a single subject-specific feature. These promising results validate and encourage the potential use of sEMG as an essential factor for monitoring and estimating the amount of fluid intake.

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DrinkWatch

Cited by 22 publications

References 33 publications

Traeddy

Traeddy

Towards Somaesthetic Smarthome Designs

A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation

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