This paper first sketches a living lab infrastructure installed in an alternative housing unit built to host 10 people with traumatic brain injury. It then presents the first research project in progress within this living lab. This interdisciplinary project aims at designing, implementing, deploying, and assessing a personalized assistive technology (PAT). Based on the needs and expectations expressed by the residents, their caregivers and their families, a cooking assistant appeared as one of the best suited PAT to foster residents autonomy and social participation. The resulting PAT will rely on pervasive computing and ambient intelligence. It will then be personalized according to each participant's capacities and specific cognitive impairments. The impact of the assistant on autonomy and quality of life will then be measured. The overall organizational impact of such assistive technology will be also documented and evaluated.

Western societies are facing demographic challenges due the rapid aging of their population. In this context, economic and social issues are emerging, such as an increasing number of elderly in need of home cares and a shortage of caregivers. Smart home technology has imposed itself as a potential avenue of solution to these important issues. Its goal is to provide adapted assistance to a semi-autonomous resident in the form of hints, suggestions, reminders, and to take preventive actions, for instance turning off the oven, in the case of an emergency. The main scientific challenge related to this kind of assistance concerns the problem of recognizing, in real time, of the on-going activities of the resident in order to provide punctual guidance for the completion of everyday tasks. In the literature, the majority of the proposed solutions for activity recognition exploit a complex and expensive network of intrusive sensors (i.e. infrared, radio-identification, electromagnetic, pressure, cameras, etc.). A recent and innovative way of performing activity recognition is based on the monitoring of electrical household appliances by analyzing the electrical signals solely at the main panel. This approach is less intrusive and required only one sensor. In this paper, we present new advancements in that field, which take the form of an efficient method for recognizing electrical appliances within smart home based on the analysis of the features of the load signatures (active and reactive power, FFT) and on the use of the C4.5 algorithm to extract decision trees. This method has been implemented and tested in real smart home infrastructure showing that it is economical, simple and efficient.

We apply artificial intelligence techniques to perform data analysis and activity recognition in smart homes. Sensors embedded in smart home provide primary data for reasoning about observations. The final goal is to provide appropriate assistance for residents to complete their Daily living Activities. Here, we introduce a qualitative approach that considers spatiotemporal specifications of activities in the Activity Recognition Agent to do knowledge representation and reasoning about the observations. We consider different existing uncertainties within sensors observations and Observed Agent’s activities. In the introduced approach, the more details about environment context would cause the less activity recognition process complexity and more precise functionality. To represent the knowledge, we apply the fuzzy logic to represent the world state by the fuzzified received values from sensors. The knowledge would be represented in the fuzzy context frame. To reduce the amount of collected data, meaningful changes in sensors generated values are considered to do Activity Recognition. Applying possibility distributions for event occurrence orders and sequences within different scenarios of activities realization, we are able to generate hypotheses about future possible occur-able events. The possible occur-able events and fuzzy digit parameters of their possible happening moments are represented in matrix format. The hypotheses about possible future observable contexts are generated considering spatial, temporal and other environmental parameters and then they would be ranked. Our final goal is to better explain the observations. If no possible explanation about observation be found, it would be recognized as abnormal behavior. In the case that no expected event be observed, we can reason that maybe event has occurred but not triggered and so next available events in previously learned scenarios would be expected. The system patience for number of possible missed events depends to trade-off between the degrees of resident's forgetfulness and probability of events trigger by applied sensors.

Providing cognitive assistance in smart homes is a field of research that receives a lot of attention lately. In order to give adequate assistance at the opportune moment, we need to recognize the observed behavior when the patient carries out some activities in a smart home. To address this challenging issue, we present a formal activity recognition framework based on possibility theory. We present initial results from an implementation of this possibilistic recognition approach in a smart home laboratory.