# Data-Driven Knowledge-Based System for Self-Measuring Activities of Daily Living in IoT-Based Test
## 6 Conclusions  
In this paper, we proposed an IoT data-driven knowledge-based system for self-measuring the degree of ADL in a real hospital. The system, called EAT, provides participants with the e-coaching on self-measuring ADL. To develop the EAT, we designed a closed-loop e-coaching process, software architecture, state machine of the e-coaching plan, and inference rule convention. The closed-loop e-coaching process could provide tailored feedback depending on whether or not the participants executed the given e-coaching action. The software architecture of the supervisor device was designed to support the closed-loop e-coaching process. We proposed the formal representation of the e-coaching plan by using the UML state machine. The inference rule convention of CLIPS was proposed to support the state-based programming. Inference rules based on the rule convention were implemented to execute the state machine of the e-coaching plan. We conducted the experiment with 17 participants, in a general hospital, by using the EAT. Although the experiment had the disadvantage of a small number of participants, it demonstrated a significant difference between the group of dementia participants and the other groups of normal and MCI participants. Because of the relatively small number of MCI participants, the difference between the normal and the MCI participants was not apparent. In this experiment, the EAT could classify participants with dementia and participants without dementia. Thus, the EAT can assist medical doctors in the diagnosis of certain medical conditions. Figure 16: Average response time. The EAT has a general-purpose mechanism and architecture for e-coaching. Diverse types of IoT devices can join the EAT. After defining an e-coaching plan for dementia care in a smart home, the EAT can detect the abnormal behaviors of people with dementia and provide the relevant e-coaching. If such e-coaching is repeatedly provided to people with dementia for a specific period of time, their ADL will improve. In a hospital environment, the patient can perform the examination on their own, without the help of medical staff, so the EAT can serve as a medical-staff assistant. The EAT allows patients to continue to receive care in a home environment, without frequent doctor visits. The patient's family and doctors can remotely monitor the patient's condition. Doctors can improve patient care strategies in a remote environment. Thus, the EAT provides personalized healthcare. The EAT can be universally applied to patients in need of care in a home environment, e.g., autism patients. The EAT can also be extended to personal nutrition management and assistive systems for athletes. It helps balance nutrition by monitoring an individuals' food intake and weight, and guides them through the necessary exercise. The EAT analyzes the training data of the athletes to motivate higher training performance, as well. In the future, we intend to continue to experiment with the EAT in a general hospital, to collect big data related to ADL. We plan to develop an automatic generator for the CLIPS inference rule generator on the basis of the state machine of the e-coaching plan. Finally, we will attempt to enhance the EAT to maintain the personal health state of aged people in real homes. The enhanced EAT will be a digital drug expected to help with the early diagnosis and symptom improvement of dementia. Conceptualization, Y.S., Y.J.P., and S.J.K. ; methodology, Y.S. ; software, Y.S. and Y.J.P. ; validation, Y.S. and Y.J.P. ; formal analysis, Y.S. ; investigation, Y.S. and Y.J.P. ; resource, Y.S. and Y.J.P. ; data curation, Y.J.P. ; writing--original draft, Y.S. and S.J.K. ; writing--review and editing, Y.S. and S.J.K. ; visualization, Y.S. ; supervision, S.J.K. ; project administration, S.J.K. All authors have read and agreed to the published version of the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1A6A1A03025109).