# Data-Driven Knowledge-Based System for Self-Measuring Activities of Daily Living in IoT-Based Test
## 2 Related Research
studied ADL recognition from sensor data using the PLSA topic model [22]. The PLSA was used to discover activity topics and activity-object patterns. The mean accuracy of the proposed algorithm is 72.5%. Rule-based inference is also used to care for personal health [23]. [PERSON] et al. tried to recognize ADL by using a rule-based forward chaining inference engine classifier [24]. They developed two ad hoc ADL classifiers, namely the rule-based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively. [PERSON] et al. developed a non-intrusive wireless sensor system with a rule-based forward chaining inference engine classifier [25]. The system acquiresenvironmental data essential for the classification of activities of daily living. By analyzing retrieved the data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different ADLs. The experiments show an overall sensitivity of 91.27%. [PERSON] et al. studied a clinical decision support system (CDSS), using a rule-based inference [26]. The CDSS was designed for the diagnosis of the various stages of the AD. Its classification accuracy is 93%. The \"C\" Language Integrated Production System (CLIPS) was developed to model human knowledge or expertise at NASA's Johnson Space Center, from 1985 to 1996. CLIPS is a rule-based inference engine useful for creating expert systems and other programs where a heuristic solution is easier to implement and maintain than an algorithmic solution [27]. Rules and objects form an integrated system, as rules can pattern-match on facts and objects. E-coaching is an emerging computing area in which intelligent systems are used to encourage progress toward specific health-related goals by providing tailored guidance [28]. A mobile application called Lucy infers and counsels people regarding eating behaviors [29]. [PERSON] et al. proposed an approach to monitor athletes' behavioral changes to predict a decline in motivation [30]. This approach encourages these individuals to keep training through more personalized routines and effective strategies. [PERSON] et al. studied a framework to provide e-coaching that takes into account the feelings of people with dementia [31]. E-coaching systems based on emotion appraisal are more effective and acceptable to people. Further, [PERSON] et al. reported that participants with moderate-level dementia could complete an average of 11% more handwashing steps independently and required 60% fewer interactions with a human caregiver when the e-coaching system was in use [32]. The existing studies on assistive technology for cognition have indicated that it can increase the efficiency of traditional rehabilitation practices by enhancing a person's ability to engage in therapeutic tasks independently and by broadening the range of contexts in which these tasks can be exercised [33]. A loosely coupled architecture of an e-coaching system was proposed by [PERSON] et al. [34]. The architecture recognizes the four major activities of the e-coaching processes: monitoring, decision-making, persuasion/awareness provision, and diagnosing/learning. [PERSON] and [PERSON] proposed the concept of personal health navigation (PHN) [35]. The purpose of PHN is to help an individual reach and maintain his/her desired health state. PHN starts with a user-specified health goal. From there, the system starts to collect measurements about the person's health that relate to his/her goal to estimate the current health states. To reach the desired goal optimally, the system breaks the steps from the current state and provides the next step to an actuation mechanism.