## 2 Related Work  
The scientific literature related to activity recognition in the context of smart homes for seniors is quite extensive [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. Generally speaking, the problem of ADL recognition in smart homes relates to a fundamental question in AI [34]: How to identify the actual ongoing activities, as well as their progress (steps), so that this identification can be used in a decision-making assistive process? Usually, a recognition system receives a series of observations (which are low-level inputs from sensors), interprets these inputs (extracting and understanding the information), and subsequently employs an algorithm to align the observed input sequence with one of the activity models (signatures) stored in a knowledge base [12]. The literature can be divided into three main categories of technologies, which are typically used for activity recognition: vision-based approaches, recognition using wearable sensors, and recognition exploiting ambient sensors. Camera-based approaches constitute one of the most prolific fields of research since the rise of deep learning models [39]. Recently, there have been notable advancements in employing deep learning models for the automatic extraction of features from input data, ranging from low-level to high-level features. This approach has shown significant improvements, particularly in the classification of large datasets, especially those based on vision. In light of this progress, the work of [40] introduces a Convolutional Neural Networks (CNNs) architecture as a deep learning model to recognize human actions in a smart home video dataset. Also, they compared the performance of their deep learning approach with previous works that utilized traditional machine learning methods on the same dataset. Their results indicate that the proposed deep learning model achieved an accuracy rate of 82.41% in classifying human activity. The limitation of this work is the low granularity of their recognition process. More recently, [PERSON] et al. [41] proposed a vision-based human activity recognition model also using deep learning algorithms. Their proposal relies on a hybrid model composed of Long Short-Term Memory (LSTM) combined with Convolutional Neural Networks (CNNs). LSTM is a specialized form of recurrent neural networks (RNNs), uniquely tailored to handle long-term data dependencies. Concurrently, Convolutional Neural Networks (CNNs) have established themselves as high-performing tools for image classification within the realm of deep learning algorithms. Recognizing the limitations of LSTM in classifying static images, they therefore proposed a hybrid CNN-LSTM model. This model leverages CNNs for initial feature extraction, followed by feeding these features to LSTM in a sequential manner via a time-distributed layer. They applied this hybrid model to classify six high-level activities (boxing, jogging, etc. )from two datasets, achieving accuracies of 96.24% and 93.39% on the KTH and Weizmann datasets, respectively. Additionally, they conducted two separate implementations of the CNN and LSTM models on these datasets, employing identical parameters to those used in the hybrid model, to assess their individual impacts on accuracy and loss. While the model shows good performance, its limitation relies on focusing only on high-level activities. Also, the chosen activity was pretty different, making them easy to distinguish. In a smart home, with, for instance, the case of cooking, activities are often similar (i.e., making tea or making coffee). Closer to our proposal, some works tried to address the specific issue of recognizing low-level actions (fine-grained recognition) in smart homes and hand gestures. A good example is the work of [PERSON] et al. [42], which proposes recognizing the hand parts in a depth hand silhouette using a camera-based system.