# Data-Driven Knowledge-Based System for Self-Measuring Activities of Daily Living in IoT-Based Test
###### Abstract  
This paper proposes a data-driven knowledge-based system with which aged people can measure the degree of activities of daily living (ADL) by themselves. The proposed system, called E-coach for ADL Test (EAT), provides participants with self-measurement procedures, using e-coaching, which is a guidance mechanism to lead the participants from an initial stage to a target goal. The EAT traces the behavior of the participants to gather ADL data that tell how well they perform the given e-coaching. Driven by the Internet of Things data, the knowledge-based inference of the EAT carries out the e-coaching mechanism that figures out what state the self-measurement procedures stay on and what guidance is necessary for the next state. The EAT ensures that all the procedures for ADL measurement are executed automatically without any help from medical professionals. The experiment described in this paper demonstrates that the EAT distinguishes between dementia patients and normal people. The measurement report assists medical doctors in the diagnosis of certain medical conditions that these people may have. Keywords:data-driven knowledge-based inference; ADL self-measurement; Internet of Things; E-coaching +
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We collaborated with medical professionals working for a general hospital, to find a solution to supplement the ADL data obtained from the interviews and expensive medical examination. They concluded that it was necessary to measure the ADL of aged people even in a hospital and designed a study to measure the degree of ADL in the hospital environment. The study did not utilize digital automation equipment. In the study, a medical professional verbally presented a task to the participants and then monitored whether the participants performed the given task correctly according to specific criteria. While simulating the experiment in the hospital, we recognized that the participants might feel burdened with the fact that they were the subject of observation. The medical professional might affect the participant's behavior unconsciously, as well. Thus, an ADL measurement study requires an unmanned system that the participants can use to self-measure the degree of ADL without being disturbed. The system, called E-coach for ADL Test (EAT), proposed in this paper enables the participants to self-measure the degree of ADL, without the involvement of medical professionals. The e-coaching is tailored coaching to help shift an individual's behavior. The EAT provides the participants with e-coaching in the measurement procedures. While the participants perform the given e-coaching, the EAT gathers all the Internet of Things (IoT) data related to the degree of the participants' ADL. Driven by the IoT data, the knowledge-based inference of the EAT executes the e-coaching mechanism that figures out what state the self-measurement procedures stay on and what guidance is necessary for the next state. It generates a report that describes the degree of the participants' ADL at the end of the procedures. Using the EAT, the participants easily measure the degree of their ADL without any help from medical professionals, and the generated report on the ADL assists doctors in the diagnosis of certain medical conditions that these people may have. The EAT can execute e-coaching for dementia care even in a smart home, as the EAT has a general-purpose architecture for e-coaching. The inference module of the EAT detects the abnormal behaviors of patients with dementia by comparing the patients' current state with the expected state of the e-coaching plan and provides e-coaching for improving the abnormal behaviors. If the e-coaching is repeatedly provided to the dementia patients for a specific period of time, their ADL can be improved. The EAT also dynamically changes the e-coaching tactics as the patients' health conditions change. In other words, the EAT acts as a digital drug.