## 1 Introduction  
Recent advances in wireless sensor network and ambient intelligence technologies have resulted in a rapid emergence of smart environments. Among these, the Smart Home (SH) has gained an increasing attention for its potential in providing enhanced quality of life within the home area. One of the motivations for SH research is the worldwide increase of the aging population, since the elderly have specific health issues that must be considered. This cohort is the focus, even though much of our work is equally applicable for other people who face similar difficulties. If, based on SH, it is possible to detect and interpret what this population do in their homes, we could think about enhancing their quality of life, prolonging independent living and reducing caregivers' necessary time and healthcare costs in general, without losing the safety that a continuous and unobtrusive monitoring provides. A proper detection and understanding of activities will enable an adequate handling of the inhabitants' habits promoting the development of more suitable solutions to foster the aforementioned quality of life. Activities of interest within an SH may include Activities of Daily Life (ADLs) relating to self-care and domestic tasks that are routinely performed by the inhabitant, and ambulatory activities relating to either specific motions or postures of the person. The capacity of the person to perform these activities without assistance can be considered as a reference for the estimation of the independent living level of the elderly [1]. The ADLs include self-care activities, such as sleeping, bathing, dressing, eating, taking medicine, and domestic tasks, such as preparing meals, housekeeping, or watering plants. Ambulatory activities, including sitting, standing and transitional activities (e.g., stand-to-sit), are very useful for detecting the physical activity level, promoting health (e.g., doing exercise) and detecting hazardous situations such as falls. There are different types of these activities, but the common ground to all of them is that they should be recognizable as such by a non-technician. The ability to correctly and automatically identify human activities, has the potential to lend itself to a wide range of application, such as detection of health emergencies [2], recommendation services for correct performance of activity steps [3], professional advice on routine lifestyle [4], anomaly detection [5] or help in treatment prescription [6]. Nowadays there are several devices and platforms available that can provide a good amount of quantitative data related to the activities performed by the SH inhabitants. However, the transitions between multiple activities are usually ignored due to their short duration [7]. These transitions would affect the final performance of activity recognition system since they would be misclassified as the available classes, thus, the reliable translation from the quantitative data to the specific activity being carried out remains a challenging task. In this paper, we focus on recognition of three types of activities (static activity, dynamic activity, and transitional activity) and further improvement of activity recognition performance, which aim to provide more reliable activity recognition system in SH. There are two main issues preventing the further improvement of activity recognition performance. Firstly, due to the diverse time intervals of three types of activities, the optimal value of the time interval for data segmentation is difficult to define. Too small a time interval may divide one activity into two adjacent windows, which may result in a lack of sufficient information to an accurate activity classification. Too wide a time interval may gather two or more activities into one segment and introduce extra noise.