## 1 Introduction  
Western societies are increasingly challenged by the phenomenon of global population aging [1]. This demographic shift poses a significant threat to the sustainability of healthcare services [2], highlighting the urgent need for innovative solutions. One such solution is the concept of aging in place [3], which advocates for enabling individuals to reside in their own homes with a degree of independence for longer periods, rather than relocating to specialized care facilities. An effective strategy for implementing aging in place involves the development of smart homes tailored to the specific profiles and needs of their residents [4]. For instance, people with mild cognitive deficit, early Alzheimer's disease, or intellectual impairment could benefit from such technology. Smart homes are residential spaces equipped with a range of sensing technologies [5], which can include vision-based sensors, ultrasonic sensors, or simpler ubiquitous sensors like infrared motion detectors, light sensors, electromagnetic contacts, and smart power analyzers. This integration of technology aims to support aging individuals in successfully and securely completing their Activities of Daily Living (ADLs) [6]. Furthermore, the incorporation of intelligent agents into smart homes, alongside sensor networks, transforms these environments into comprehensive cognitive orthoses. Collaborating with occupational therapists, nurses, physicians, and other professionals, these cognitive orthoses work synergistically to enhance the quality of life of aging individuals [7]. By providing real-time monitoring, assistance, and intervention when needed, smart homes equipped with intelligent agents offer a promising avenue for promoting independence and well-being among the elderly population. Many researchers have studied the challenges related to this ambitious goal from several points of view of computing and engineering. One of these challenges is the ability, for an artificial intelligence, to be able to comprehend the context and the ongoing ADLs in real time [8]. This topic, known as activity recognition, is the subject of the predilection of many teams of research around the world [9]. Several approaches have been proposed, but they can broadly be divided into two main families: knowledge-driven [10] or data-driven [11]. The first family of approaches [12; 10; 13; 14; 15] was originally explored with the development of expert systems and models based on mathematical formalisms (first-order logic, lattice theory, etc.). These models are generally considered to be simpler to implement and to evolve. Nevertheless, to be functional, they require a library containing the models for each ADL. While these approaches are less popular in research nowadays, they are often still chosen for real-world deployment where machine learning models are challenging to train due to high variability in the conditions, in the activities, and the difficulty to obtain ground truth [16]. Nevertheless, these knowledge-driven approaches have long suffered from generalization problems and from complex update/maintenance. To palliate to the weaknesses of these approaches, most researchers are now focusing on using machine learning for ADLs recognition [17]. The idea is to attain adaptable systems which would be independent from a human-constructed library [18]. Most of those are supervised, which means they need a labeled training dataset to work properly [19]. On the contrary, unsupervised methods do not require a labeled dataset for the learning phase [20]. These models are scarce in the literature and can only recognize coarsely grained ADLs [21]. A good tradeoff is the recent semi-supervised learning models [22]. Nevertheless, whatever approach researchers rely upon, their approach is limited by the quality of the data [16]. Very often, smart homes still rely on very simple sensors due to the cost and the complexity of processing the data [23].