Guidance and support for healthy food preparation in an augmented kitchen

Wagner, J. J.; Geleijnse, Gijs; Halteren, Aart van

doi:10.1145/1961634.1961644

Cited by 9 publications

(5 citation statements)

References 1 publication

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“…• Produce: growing foods, as well as manipulating them to create more complex combinations, such as dishes or meals (e.g. [64,69]). • Track: identification and measurement of food and food practices (e.g.…”

Section: A Conceptual Model Of Hfi Researchmentioning

confidence: 99%

Making Sense of Human-Food Interaction

Bertran

Jhaveri

Lutz

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Activity in Human-Food Interaction (HFI) research is skyrocketing across a broad range of disciplinary interests and concerns. The dynamic and heterogeneous nature of this emerging field presents a challenge to scholars wishing to critically engage with prior work, identify gaps and ensure impact. It also challenges the formation of community. We present a Systematic Mapping Study of HFI research and an online data visualisation tool developed to respond to these issues. The tool allows researchers to engage in new ways with the HFI literature, propose modifications and additions to the review, and thereby actively engage in communitymaking. Our contribution is threefold: (1) we characterize the state of HFI, reporting trends, challenges and opportunities; (2) we provide a taxonomy and tool for diffractive reading of the literature; and (3) we offer our approach for adaptation by research fields facing similar challenges, positing value of the tool and approach beyond HFI. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models. * Altarriba Bertran and Wilde are co-first authors.

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Section: A Conceptual Model Of Hfi Researchmentioning

confidence: 99%

Making Sense of Human-Food Interaction

Bertran

Jhaveri

Lutz

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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“…A wide range of research has been undertaken with the common goal of developing systems to improve levels of IJPCC 10,3 independence (Pollack et al, 2003). They range from systems with the ability of issuing reminders for taking medicine (Lundell, 2005) to providing help in the preparation of food (Wagner et al, 2011). These systems, following learning of user behaviours, can deliver prompts and alert caregivers and health care providers.…”

Section: Assistive Smart Homesmentioning

confidence: 99%

A duration-based online reminder system

Chaurasia¹,

McClean²,

Nugent³

et al. 2014

International Journal of Pervasive Computing and Communications

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Purpose – This paper aims to discuss an online sensor-based support system which is believed to be useful for persons with a cognitive impairment, such as those with Alzheimer’s disease, suffering from deficiencies in cognitive skills which reduce their independence. Such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (iADLs). Design/methodology/approach – The system proposed processes data from a network of sensors that have the capability of sensing user interactions and ongoing iADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the iADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability, taking into consideration the iADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the iADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity; thus, updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct. Findings – The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the iADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single-sensor activation in comparison to an alternative approach which did not consider activity durations. Thus, it is concluded that incorporating progressive duration information into partially observed sensor sequences of iADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease. Originality/value – Activity duration information can be a potential feature in measuring the performance of a user and distinguishing different activities. The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the activity is also increased. The use of duration information in online prediction of activities can also be associated to monitoring the deterioration in cognitive abilities and in making a decision about the level of assistance required. Such improvements have significance in building more accurate reminder systems that precisely predict activities and assist its users, thus, improving the overall support provided for living independently.

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“…These sensors can be built into mobile devices or even utensils such as a coffee cup to augment them with awareness of their environment and situation as context [12]. By means of sensors in various kitchen utensils, the activities in the kitchen can be tracked and the user's cooking competence can be estimated [31]. This cooking competence can be used as background knowledge to recommend the user a new recipe.…”

Section: Related Workmentioning

confidence: 99%

Context-aware recommendations through context and activity recognition in a mobile environment

Pessemier

Dooms

Martens

2013

Multimed Tools Appl

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The mobile Internet introduces new opportunities to gain insight in the user's environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user's current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.

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Guidance and support for healthy food preparation in an augmented kitchen

Cited by 9 publications

References 1 publication

Making Sense of Human-Food Interaction

Making Sense of Human-Food Interaction

A duration-based online reminder system

Context-aware recommendations through context and activity recognition in a mobile environment

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