# Data-Driven Knowledge-Based System for Self-Measuring Activities of Daily Living in IoT-Based Test
## 2 Related Research  
Cognitive impairment due to Alzheimer's disease and other forms of dementia affect a patient's ability to maintain ADL [9]. This has severe implications on the patient's independence and quality of life [10]. Current scientific evidence indicates that evaluation by ADL measurement helps to identify problems that require treatment or care [11]. For this reason, various studies on ADL measurement have been conducted. Research related to gait ability suggests a variety of measures to score physical function and basic daily living possibilities of the elderly [12]. In order to determine the possibility of independent daily living for the elderly, instrumental ADL for measuring the ability to use tools, appliances, and equipment is also a major issue [13]. Currently, hospitals measure patients' ADL through complex interviews of elderly people, their relatives, and caregivers. The ADL measured through memory is prone to distortion and is less accurate. In order to accurately measure the patient's ADL, it is necessary to measure the ADL in daily life for a long time [14]. The use of IoT Sensor can be an alternative method for practical ADL measurement [15]. IoT technology enables long-term data collection by installing various sensors in the house [16]. [PERSON] et al. proposed a promising tool for the daily assessment of walking ability in a home with an easy-to-install distance sensor array [17]. The test bed of this paper collects basic walking ability and instrumental activities of daily living function through an IoT Sensor Network. In addition, we consider helping the patient's daily activity through the EAT system. The existing studies have attempted to monitor the health of people by using IoT or fog computing. A system called Technology Integrated Health Management was developed to use sensory devices at home to remotely monitor the health of people with dementia [18]. [PERSON] et al. proposed a structured approach for healthcare systems based on IoT [19]. They illustrated the approach for medical-use cases such as alcoholism, Alzheimer's disease (AD), and staff/patient safety-use cases. Healthcare systems based on fog computing improve the accuracy, data privacy, communication cost, and response time of the IoT systems. [PERSON] et al. continuously measured 4072 participants' gait speed at 75 designated places, using fog computing, and found that daily gait speed and in-laboratory gait speed were inconsistent [14]. Fog computing can be designed to control access permissions for non-intrusive sensor data and to protect personal data [15]. [PERSON] et al. proposed a smart-home-based ambient assisted-living framework [16]. The framework has a preprocessing fog layer to acquire real-time data from a heterogeneous sensing system. Machine learning and rule-based inference technique are used to recognize the changes in a person's health. The machine learning mainly uses the Bayesian method and statistical models, for example, the probabilistic latent semantic analysis (PLSA) model. The rule-based inference supports a deterministic estimation approach. As machine learning cases, [PERSON] et al. used an adaptive Bayesian inference system to predict walking activities and gait events with wearable sensors [20]. The system includes an adaptive action-perception method for the prediction. The experiments show that mean decision times of 240 and 40 ms are needed to achieve mean accuracies of 99.87% and 99.82% for recognition of walking activities and gait events, respectively. [PERSON] et al. proposed a Bayesian network decision model to diagnose dementia, AD, and mild cognitive impairment (MCI) [21]. The proposed decision model can be used to build clinical decision support systems to diagnose such diseases. For the Consortium to Establish a Registry for Alzheimer's Disease dataset, the best accuracies of the proposed system are obtained by the multilayer perceptron artificial neural network classifier (84% and 71% for dementia and AD, respectively). [PERSON] et al.