## 1 Introduction  
Falling is a common problem among older people living in the community and can have serious consequences for their lives and the society [1]. As reported by the World Health Organization (WHO), the population of elderly people (aged 60 or over) will increase from 900 million in 2015 to about 20 billion by 2050, accounting for 22% of the world's 2050 population [2]. Fall prediction is a multifaceted problem involving complex interactions among physiological, behavioral, and environmental factors [3]. Clinical fall-risk assessments typically include questionnaires and functional assessment of the posture, gait, cognition, and other risk factors with respect to falling [3]. These clinical assessments provide an overview of the fall-risk snapshots but are usually subjective, and those who use threshold assessment scores for performing the classification are considered to be decreasing rather than increasing [4]. Therefore, previous relevant studies have used the timed up and go (TUG) time score as a clinical evaluation item to overcome the subjective interpretation of falls [5]. The objective of clinical screenings is to find the outliers. The most common method used for clinically determining the thresholds is the clustering criteria. The TUG test performs a simple, rapid, and applicable clinical assessment of the balance and mobility of older people. However, the environmental assessment tools use clinical assessment scales or test data for analyzing the fall risk. The TUG assessment has been used to assess the fall risk in previous cohort studies of different versions and approaches using wearable sensors [5; 6; 7; 8; 9; 10; 11], predominantly by testing in controlled laboratory environments [9] or focusing on inpatients [11] and healthy subjects [5; 11; 12; 13]. In addition, although a few studies have focused on community-dwelling older people, there is no consensus about the scores for this group, most of which can be attributed to subjects belonging to different groups. For example, [PERSON] et al. [14] discriminated a community within 13.5 s. [PERSON] et al. [15] used 15 s to determine whether there is an optimal cutoff point for the risk of falling and whether the older people living in a family have a risk of falling. Subjects of different groups have different cutoff points. However, in the older-people community, there is no way to clearly divide this point. In case of the same problem, most research intends to select specific subjects for discussion; however, it is considerably difficult to select subjects applicable to the health of the real environment or the subhealth. Under first-line clinical diagnosis, clinical-test evaluation allows a comprehensive quantitative comparison of the performances of different tasks; however, most fall-risk assessments require the supervision of a medical professional [16]. Therefore, these tests are usually not applicable to large groups, such as community-dwelling older people, under first-line clinical diagnosis because of medical manpower constraints. Screenings for older people surviving a fall must avoid cumbersome processes, minimize the time required for case screening, and reduce dropout. In community service, the risk of falling and the use of artificial intelligence machine learning techniques must be discussed as part of the Internet of Things (IoT) to support medical services and improve the medical quality. \"Connected health\" brings the concept of intelligent health, which is an application based on the IoT. The effectiveness of sensor-based care models in clinical outcomes and cost savings was mentioned in a literature review on connected medicine; however, an understanding of how to use the value of the data can be derived from the patients by monitoring them at home and in the community, so obtaining information and then combining it with other biopsychosocial data to obtain information about whether patients need intervention, knowing that corresponding care information is less discussed [17].