## 4 Content Analysis and Data Mining
### Group 1: IoT and Users  
The first group of papers includes [PERSON] et al. [63], [PERSON] and [PERSON] [64], [PERSON] et al. [65], [PERSON] et al. [66], and [PERSON] [67], in which their core discussion is around IoT and users since they have received high references. As shown previously, these papers have utilized IoT for decision purposes for daily activities and users' satisfaction (refer to Figure 7). It shows that the highest reference number is 391 for 'using' with a weighted presentation of 1.05. This word is used in different stems, such as use, usefulness, and uses. For example, \"... useful for growing a full decision... \", \"... used to detect changes in pitch... \", and \"... used for system selection... \". The usefulness is an important discussion in the innovation literature for all new technologies and concepts [68; 69]. Another high referenced word is 'information', which is used in this group and refers to 'user information satisfaction', 'information technology/system', 'information and communication', and 'accurate and reliable information'. This helps to point out important concerns in the smart home, such as the reliability of information and measuring user satisfaction. Figure 7 also shows other highly used words in this group. As an example of collecting 'information' and 'usability' of system, [PERSON] and [PERSON] [64] developed a personalized intelligent music selection system for psychological health based on heart rate variability and machine learning. Usability, need, and usefulness of a system have been ignored to be examined and validated in some contexts, but it is recommended as one of the key factors of successful technology adoption and implementation in the technology/information system literature [70; 71; 72; 73]. This system intends to utilize an intelligent mechanism for automatic music selection for the users according to their emotions and moods. The proposed system includes: (1) A smartphone, (2) A wearable device, such as smartwatch for tracking heart rate, (3) Time-domain method to determine the emotion according to the heart rate value (HRV), and (4) Machine learning algorithm for selecting music. The music types are determined by valence and emotion degree through classification and regression tree (CART)  
Figure 6: A number of references for each keyword within each group. Note: the left vertical axis shows the number of references for Groups 1 and 2, and the secondary vertical axis on the right shows the numbers for Group 3.  
analysis. To evaluate the proposed system performance, a survey was used: An experiment consisting of 30 graduate students aged 19-36 (14 females and 16 males) was conducted to determine if the user's working function is enhanced by this system. The purpose was to retain the user's HRV at a moderate level. For HRV analysis, the time-domain method was used, and a standard deviation of normal RR intervals (SDNN) was selected as the parameter to determine emotion. The system usability scale (SUS) questionnaire, service quality survey, and the \"National Aeronautics and Space Administration--Task Load Index\" (NASA-TLX) questionnaire (after each test to assess the users' mental workload) were carried out. A'satisfaction' assessment and a performance questionnaire were designed to measure 'user's satisfaction' and performance by using this system. However, tasks for the future are suggested to develop wearable units, so that the proposed system can be expanded to be used in other places like factories and healthcare centers. In addition, [PERSON] [67] proposed a door-lock system of smart homes able to work based on behavior tracking, and this has been the main contribution of this research. This system makes use of various kinds of constraint'satisfaction' problems (CSPs) and forward checking algorithm and works based on the following steps: (1) Every time the door is opened and closed by a resident, a sensor mounted in the lock system makes and gathers log data.