Design of a Kitchen-Monitoring and Decision-Making System to Support AAL Applications

Abstract

Numerous researchers are working on Ambient Assisted Living systems to enable more comfortable and safer living for senior people in their homes. Due to modern lifestyles and an aging population, this has become a very important issue. According to the available literature, it is obvious that the kitchen is one of the most important and most dangerous rooms in the home. However, there is still evident lack of monitoring systems suitable for specific kitchen activities. In this paper, we propose a monitoring system capable of identifying activities related to the cooking process, and a decision-making system capable of identifying some unwanted and possibly critical conditions. The proposed systems are designed to satisfy the requirements of the modern Ambient Assisted Living systems dedicated to older adults. The proposed monitoring system consists of ultrasound, temperature, and humidity sensors. The acquired results from these sensors are the inputs for the decision-making system, which generate warnings or alarms intended for the senior users and/or formal or informal caregivers. This system is designed to improve home safety related to kitchen activities, as well as to provide information about the lifestyle and daily activities of senior users. Experimental validation of the proposed system confirms its functionality and accurate design approach.

A Contextual Inquiry of People with Vision Impairments in Cooking

“…Beyond customizing contextual information of objects, we also uncovered the importance of managing the space (Section 4.1.3) and knowing the internal state of objects (Section 4.1.5), which often require them to leverage touch to scan through the space (Section 5.1.1). Prior research has explored approaches to augment the kitchen space, such as by instrumenting a kitchen with cameras, microphones, and motion tracking [49,64]. These approaches should also work for people with vision impairments, and may provide even more benefit as they may help address accessibility challenges in the kitchen.…”

Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

“…Only in six instances did researchers endeavor to pinpoint abnormal kitchen behavior. For example, Žari'c et al [33] linked erroneous behavior to the use of a hotplate during cooking, focusing solely on activities during the cooking stage. While they modeled situations potentially endangering individuals during cooking, such as a plate being turned on without objects on it, they did not cover cases such as the completion of a cooking task, initiation, or organization (see Table 1).…”

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

“…In [ 35 ], Žarić et al presented a system to monitor the cooking process in home kitchens and to identify critical conditions related to elderly people. The proposed system utilized humidity, ultrasound, and temperature sensors as input to a system that is capable of generating an alert or a warning in case of a dangerous situation.…”