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AutoADL: Automatic Detection of Activities of Daily Living and Resident Identification

Title
AutoADL: Automatic Detection of Activities of Daily Living and Resident Identification
Translated Title
AutoADL: 거주자의 식별 및 Activities of Daily Living 자동 탐지
Authors
Jin, Ju Heon
DGIST Authors
Jin, Ju Heon; Park, Tae Joon; Son, Sang Hyuk; Lee, Jong Hun
Advisor(s)
Park, Tae Joon; Son, Sang Hyuk
Co-Advisor(s)
Lee, Jong Hun
Issue Date
2015
Available Date
2015-01-12
Degree Date
2015. 2
Type
Thesis
Keywords
Indoor LocalizationUltrasonicAutomatic systemIdentification실내측위초음파자동화 시스템식별
Abstract
Localization is a basic technique which used by positioning and navigation services in our daily lives. These services typically utilize GPS in outdoor environments. However, they cannot be used in indoor environments because GPS signals are hardly received in indoor environments. By contrast, indoor localization does not utilize GPS as well as has some problems arising from a small space with many obstacles as well as issues of security and privacy. Nevertheless indoor localization is a very important technique for smart homes that can be applied to many indoor services. Therefore, many indoor localization studies have been conducted using sensors such as RF signals, RFID, ultrasonic sensors, smartphones. However, these studies only provide position information or need holding devices to user. There is a problem to be used in indoor localization. So, we propose our system, called AutoADL, which gives position and identification information without using dedicated devices. Existing study [25] has the same advantages as AutoADL. But, this study has some problems such as a limited number of people that can be tracked, and a need for labor intensive installation process. In contrast, AutoADL automatically calculates the number of people it can track and the characteristics of targets using K-means clustering [32], Bayesian Information Criterion (BIC) scoring [33], and error rate checking. In addition, it provides many people’s position and identification information with high accuracy using Multi-Hypothesis Tracking (MHT) algorithm. We simulate AutoADL in several environments such as changing the number of residents, home environments, weight-values, and resident’s height. In the result, when sensor distribution is smaller than 4cm and the difference of resident height is bigger than 5cm, tracking accuracy became higher than 90%. ⓒ 2015 DGIST
Table Of Contents
I. Introduction 1 -- II. Related Work 3 -- 2.1 Sensors 3 -- 2.2 Techniques 6 -- III. System Overview 8 -- IV. EstimatedResident’sInformation 10 -- 4.1 The Number of Residents 10 -- 4.2 Resident’s Height 12 -- V. Calculating Weight-Values 14 -- 5.1 The Height of Resident 17 -- 5.2 Direction 18 -- 5.3 Location 19 -- VI. Evaluation 20 -- 6.1 Accuracy about the sensor error 20 -- 6.2 Accuracy about the difference of height 21 -- 6.3 Accuracy about weight-values 22 -- 6.4 Accuracy about the number of residents 23 -- 6.5 Accuracy at the different environments 24 -- VII. Discussion 25 -- VIII. Conclusion & Future work 26 -- References 27
URI
http://dgist.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001915732
http://hdl.handle.net/20.500.11750/1400
DOI
10.22677/thesis.1915732
Degree
Master
Department
Information and Communication Engineering
University
DGIST
Files:
Collection:
Information and Communication EngineeringThesesMaster


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