# Fine-Grained Mapping Between Daily Activity Features in Smart Homes
## 2 Related Work
### DAR Model Establishment  
For DAR model establishment, only a single smart home is involved, and the same data distribution is utilized. Three key issues are typically addressed during the establishment of a DAR model. The primary challenge in DAR model establishment is how to effectively segment the sensor event stream. Over a given period, numerous daily activity instances are performed, resulting in a continuous stream of sensor events. To recognize the category of each activity instance, the activated sensor event stream must be segmented accurately. Early approaches focused on segmentation algorithms using fixed windows, defined by either a fixed number of sensor events or a fixed duration [15; 16]. While these methods are straightforward to implement, they often yield poor recognition performance. This is because the number of activated sensor events and their durations vary significantly between activity instances. Consequently, sensor events from multiple activities may be grouped into the same window, or events from a single activity may be split across multiple windows. To address these limitations, segmentation algorithms based on whole daily activity instances have been developed. [PERSON] et al. observed that sensor events activated by the same activity are spatially and temporally close. They proposed a relevancy degree formula to measure the relationship between adjacent sensor events, segmenting the stream when the relevancy degree exceeds a specified threshold [17]. Similarly, [PERSON] et al. extracted additional features from sensor events and introduced a change-point detection algorithm for time-series data [18]. Although segmentation algorithms based on entire activity instances improve recognition performance, they often require specialized algorithms and are challenging to apply in real-time DAR scenarios [19]. The second challenge in DAR model establishment is how to extract meaningful features from the sensor event stream to represent daily activities. There are two primary approaches for feature extraction. The first approach involves manually defining the feature space. For example, the equipped sensors themselves are often used as the feature space. Additionally, frequently mined items or sequences from the sensor event stream can serve as the feature space [20; 21]. Once the feature space is defined, methods such as activated frequency, density [22], TF-IDF [23], and mutual information [24] are commonly employed to calculate feature values. The second approach generates the feature space automatically. For instance, [PERSON] et al. used 2D Convolutional Neural Networks (CNNs) to extract features by representing the sensor event stream as images, with sensor events mapped to pixels or grayscale values. This approach is inspired by applications where CNNs are used to extract features from images [25]. Additionally, recognizing the similarity between sensor event streams and natural language sequences, [PERSON] et al. and [PERSON] et al. proposed feature extraction models based on Seq2 Seq and Word Embedding techniques, respectively [26; 27]. Compared to the first approach, the second approach allows for the extraction of more complete and discriminative features, potentially improving the accuracy of daily activity recognition. The final challenge in DAR model establishment is how to develop an effective model for recognizing daily activities. Approaches to developing DAR models can be categorized into two main types: data-driven approaches and knowledge-driven approaches. Data-driven approaches typically treat DAR as a classification task [28; 29; 30; 31; 32; 33]. As such, a variety of classification models are used, such as Naive Bayes (NB) [28], Hidden Markov Models (HMM) [34], Support Vector Machines (SVM) [35], Conditional Random Fields (CRF) [36], Random Forests (RF) [37], Ensemble Learning [38; 39], Formal Concept Analysis (FCA) [40], and Long Short-Term Memory (LSTM) networks [41], along with their variants.