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This work presents a real-time system for tracking multiple object in the context of meal preparation when using the Cognitive Orthosis for CoOKing (COOK). This system is called SafeCOOK. It aims to provide more capabilities to detect some dangerous situations that the current system does not consider. For example, it can locate a utensil or other kitchen object that has been left on the cooking surface of the stove while a meal is being prepared. This system uses a hybrid method based on YOLO and KCF to detect, track and drop cooking utensils as they enter and leave the cooking area, and is capable of monitoring an entire cooktop in real-time with a single camera. The software has been implemented on an embedded platform in the smart stove and has been added to it. The system produces good segmentation and tracking results at a frame rate of 1 to 4 frames per second, as demonstrated in extensive experiments using video sequences under different conditions.

Deep learning models have significantly contributed to recognizing older adults’ daily activities for telemonitoring and assistance. However, recognizing human activities in real-world smart homes over the long term presents substantial challenges. Obtaining the ground truth is time-consuming and costly, yet it is crucial for training and improving deep learning models. Inspired by the impressive performance of self-supervised learning models, this paper utilizes a model based on the SimCLR framework and a self-attention mechanism for downstream human activity recognition. The model leverages the limited and intermittent labeled activities collected by the Label Older Adults’ Daily Activities (LOADA) application, which was deployed and used to acquire activity labels in the real-world, uncontrolled smart homes of three young people and two older adults for over one month. The experimental results demonstrate significant performance in activity recognition, employing semi-supervised learning with limited labels, and transfer learning scenarios where representations learned from one smart home are transferred to another. This research could inspire other human activity recognition community researchers to overcome labeling challenges for monitoring older adults in real-world scenarios.

Assistive technologies for cognition (ATC) can help alleviate some of the impacts of executive dysfunction and support independence. This article presents a scoping review to highlight the research gaps in this area. Search of scientific and gray literature was conducted in clinical and computer science databases, resulting in a selection of 27 papers. Traumatic brain injury and dementia were the disorders for which the most supports were available. Planning and carrying out tasks were the most supported executive function operations. Food preparation was the daily activity for which the most supports were developed. Diverse non-context-aware technologies were used to deliver primarily audio and visual prompts and cues. The performance of most of the technologies was tested among the target population to evaluate acceptability and effectiveness. This review showed that: (1) The goal formulation executive function operation needs to be the focus of more research; (2) the clinical context needs to be described in more be detail; (3) ATC development could benefit from the use of a wider range of user-centered methods, such as observational or ideation methods; (4) more evaluation of user outcomes is needed, such as impact on independence; and (5) a greater diversity of activities of daily living should be supported. Recommendations are presented.

To enable ageing in place, innovative and integrative technologies such as smart living environments may be part of the solution. Despite extensive published literature reviews on this topic, the effectiveness of smart living environments in supporting ageing in place, and in particular involving unobtrusive technologies, remains unclear. The main objective of our umbrella review was to synthesize evidence on this topic.

This work presents a real-time system for tracking multiple object in the context of meal preparation when using the Cognitive Orthosis for CoOKing (COOK). This system is called SafeCOOK. It aims to provide more capabilities to detect some dangerous situations that the current system does not consider. For example, it can locate a utensil or other kitchen object that has been left on the cooking surface of the stove while a meal is being prepared. This system uses a hybrid method based on YOLO and KCF to detect, track and drop cooking utensils as they enter and leave the cooking area, and is capable of monitoring an entire cooktop in real-time with a single camera. The software has been implemented on an embedded platform in the smart stove and has been added to it. The system produces good segmentation and tracking results at a frame rate of 1 to 4 frames per second, as demonstrated in extensive experiments using video sequences under different conditions.

Remote monitoring uses smart home features to promote aging in place by preventing emergencies and increasing the quality of life of older adults. However, traditional reports, data, and graphs produced by remote monitoring technologies are not well suited to older adults’ needs. Thus, the complexity for older adults to use and interpret reports can lead to usability and adoption issues. The goals of this study were 1) to incorporate ludic-based design principles into an application that provides older adults with an alternative way to interact with information about their Activities of Daily Living (ADL), and 2) involve older adults in creating new ludic interfaces that address usability and reduce adoption issues. This ambient assistive technology offers older adults the opportunity, through its interface, to promote curiosity and exploration, the pursuit of non-external goals, and openness about the user’s routine and lifestyle. By using an iterative, Human-Centered, co-design approach in 4 workshops with older adults (N = 7), we combine older adults’ needs with ludic elements to propose a new user experience.

iNnovative Easy Assistance System (NEARS) is a Canadian transdisciplinary research project that aims to create a platform and standards that will make the development of Ambient Assisted Living (AAL) solutions technically feasible and clinically viable. It built and operationalized a hardware and software infrastructure for smart environments called NEARS-Hub. The key function of the NEARS-Hub is to deliver data generated by The Internet of Things (IoT) devices near the edge. The processing of the collected data in homes and its storage in the event of break-in services are carried out locally at the level of the edge node. The goal is to provide high-quality services and a quick response time. Therefore, edge nodes must be capable of flexibility, interoperability, and scalability to adapt their services in case of unforeseen situations. A common modeling approach is to create services without separating responsibilities between system layers. However, very few solutions allow the administration of services and the provisioning of resources in a flexible way and as close as possible to the equipment, at the edge of the network, or, ultimately, at the places where the data has been generated. This article proposes NEARS-Hub, a lightweight edge computing platform for AAL solutions, which revolves around three main notions: interoperability, flexibility, and scalability. The design of the current version of the NEARS-Hub is based on knowledge from several home experiments. The proposed model is validated by comparing the performances of the NEARS-Hub with a version based on a classic AAL solution.

This work presents a real-time system for tracking multiple object in the context of meal preparation when using the Cognitive Orthosis for CoOKing (COOK). This system is called SafeCOOK. It aims to provide more capabilities to detect some dangerous situations that the current system does not consider. For example, it can locate a utensil or other kitchen object that has been left on the cooking surface of the stove while a meal is being prepared. This system uses a hybrid method based on YOLO and KCF to detect, track and drop cooking utensils as they enter and leave the cooking area, and is capable of monitoring an entire cooktop in real-time with a single camera. The software has been implemented on an embedded platform in the smart stove and has been added to it. The system produces good segmentation and tracking results at a frame rate of 1 to 4 frames per second, as demonstrated in extensive experiments using video sequences under different conditions.

Ambient Assisted Living (AAL) environments encompass technical systems and the Internet of Things (IoT) tools to support seniors in their daily routines. They aim to enable seniors to live independently and safely for as long as possible when faced declining physical or cognitive capacities. This work presents the design, development and deployment of an AAL system in the context of smart cities. The proposed architecture is based on microservices and software components. We examined the requirements and specifications of AAL systems in smart homes, in efforts to describe and evaluate how they would be transposable in the case of smart cities. The system has been tested and evaluated in the laboratory; it has been deployed in real life settings within city and is still in use by five elderly people.