“KogniChef”: A Cognitive Cooking Assistant

Neumann, Alexander; Elbrechter, Christof; Pfeiffer-Leßmann, Nadine; Kõiva, Risto; Carlmeyer, Birte; Rüther, Stefan; Schade, Michael; Ückermann, André; Wachsmuth, Sven; Ritter, Helge

doi:10.1007/s13218-017-0488-6

Cited by 24 publications

(9 citation statements)

References 13 publications

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“…is to convert a non-smart home into a smart home by cheap and easy-integrable IoT devices to be affordable by everybody. Furthermore, several sophisticated research prototypes have already been implemented in the KogniHome : an intelligent cooking assistant [ 14 ], a connected chair [ 15 ], an intelligent door/wardrobe [ 16 ] and a smart mirror [ 16 ].…”

Section: Research Platformmentioning

confidence: 99%

A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment

2022

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This paper presents a framework for learning event sequences for anomaly detection in a smart home environment. It addresses environment conditions, device grouping, system performance and explainability of anomalies. Our method models user behavior as sequences of events, triggered by interaction of the home residents with the Internet of Things (IoT) devices. Based on a given set of recorded event sequences, the system can learn the habitual behavior of the residents. An anomaly is described as deviation from that normal behavior, previously learned by the system. One key feature of our framework is the explainability of detected anomalies, which is implemented through a simple rule analysis.

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Section: Research Platformmentioning

confidence: 99%

A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment

2022

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“…Moreover, the devices used might include those which deviate from standard ones found in the kitchen. Examples include: a robotic arm 4 , oven cameras [26], tracking-recording cameras [27]- [29], automated ingredient scales [27], autonomous stoves [27], [28], accelerometers and RFID readers attached to kitchen objects [29]. The investigation of how these workflows are automatically formed pertains to the field of automated planning [30], which is out of the scope of this paper.…”

Section: B An Abstract Iot Cooking Workflow Should Respectmentioning

confidence: 99%

IoT Cooking Workflows for End-Users: A Comparison Between Behaviour Trees and the DX-MAN Model

Ventirozos

Batista-Navarro

Clinch

et al. 2021

2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

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A kitchen underpinned by the Internet of Things (IoT) requires the management of complex procedural processes. This is due to the fact that when supporting an end-user in the preparation of even only one dish, various devices may need to coordinate with each other. Additionally, it is challengingyet desirable-to enable an end-user to program their kitchen devices according to their preferred behaviour and to allow them to visualise and track their cooking workflows. In this paper, we compared two semantic representations, namely, Behaviour Trees and the DX-MAN model. We analysed these representations based on their suitability for a range of endusers (i.e., novice to experienced). The methodology required the analysis of smart kitchen user requirements, from which we inferred that the main architectural requirements for IoT cooking workflows are variability and compositionality. Guided by the user requirements, we examined various scenarios and analysed workflow complexity and feasibility for each representation. On the one hand, we found that execution complexity tends to be higher on Behaviour Trees. However, due to their fallback node, they provide more transparency on how to recover from unprecedented circumstances. On the other hand, parameter complexity tends to be somewhat higher for the DX-MAN model. Nevertheless, the DX-MAN model can be favourable due to its compositionality aspect and the ease of visualisation it can offer.

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“…The applicability of both types of approaches inside a real automatic supervision system has become a hot topic in recent years. Thus, data-based systems have been proposed, for instance, for assisting persons with dementia during handwashing (Hoey et al, 2010), for kitchen activities supervision (Kruger et al, 2014;Yordanova et al, 2017;Rohrbach et al, 2012;Neumann et al, 2017) and for supervision in a whole smart environment using sensor data (Twomey et al, 2016;Intille et al, 2006;Kröse et al, 2008;Crandall & Cook, 2011). Additionally, knowledge-based supervision systems try to take full advantage of other knowledge resources in contexts where accessing training data is complicated.…”

Section: Related Workmentioning

confidence: 99%