제 12 회 정보통신응용기술워크숍 Future IT Revolution

1. 제12회 정보통신응용기술워크숍 Future IT Revolution 동서신의학 u-라이프케어 연구센터 프로액티브 라이프케어 기술 2007년 4월 11일 경희대학교 동서의료공학과 김 태 성

2. u-라이프케어 비전 질 높은 삶 건강한 삶 u-라이프케어 즐거운 삶 행복한 삶

3. Aging in Place

4. How Can Technologies Help ? Smart Homes/Smart Care Question: Can smart home systems anticipate the needs of residents or patients to improve the quality of life?

5. Proactive Lifecare Proactive Lifecare: innovations in sensors (i.e., motes), software, and wireless technologies that allow vital information about human health to be tracked remotely. Ross et al., 2004, IEEE Spectrum

6. Proactive System • Proactive system must anticipate a user’s needs in prior to the user’s requests. • Activity tracking for status monitoring • Task sampling for status monitoring • Health maintenance • Cognitive support for Alzheimer patients • Enhanced communication with the outside • Access to information and stimulation

7. Initiated in 2002. Lead by Eric Dishman, Senior Research Scientist Intel’s goal: make people more proactive about their own health and wellness needs, long before they have an exhausting, expensive medical catastrophe that drives them to an emergency room Make technology more proactive by anticipating people’s health-related needs and take whatever action that is appropriate on their behalf Proactive Computing, a concept developed by David Tennehouse, Intel’s Vice President of Research Intel’s Proactive Health Effort

8. Biological and Behavioral Sensors Gather real-world data Monitoring and localization Motion, contact, magnetic, RFID, motes, etc. Sensor Networks Reliable, secure, wireless network of sensors Plug and play Context tracking Statistical Inference Engines Transform that data into medically meaningful, contextual information Determine the types or patterns of activities Compare life-long database and current measures Behavior Tracking and Recognition Activity tracking and recognition Home Networks and Display Smart appliances: digital home technologies from PCs to TVs to cell phones Connect everything (TV, clock, telephone, PC, PDA, etc.) Reminder: display technology Proactive Health Technologies

9. uLCRC 연구내용 Major Research Areas at u-LCRC Bio MEMS 구조체 기술 SoC 기반 스마트 센서모듈 기술 u-라이프케어 프로액티브 컴퓨팅 기술 건강 지원 스마트 오브젝트 기술 u-라이프케어 플랫폼 기술

10. Bio-sensors Bio-Sensor & SOC • Analytical devices which use biological interactions to provide either qualitative or quantitative measurements • Bio-receptor is a chemical/biological molecular recognition element (ex. Antibody, enzymes, DNA, cells, tissue, or whole organ) • Transducer converts the recognition event into a electrical signal (ex. Electrodes, pH electrode, thermistor, photon counter, piezoelectric device)

11. Bio-SOC Bio-Sensor & SOC

12. Camera EEG EKG BP SpO2 GPS Mp3 PDA/ Gateway Motion Sensor Body Sensor Network (BSN) Bio-Sensor & SOC

13. Application-driven Challenges Data fusion (aggregate and filter) Erroneous sensor readings Missing values Feature selection Source recovery Distributed inference Activity recognition Support of multiple data rates Security and privacy at low energy cost Localization Networking Challenges Challenges for Sensor Nodes Low-complexity / low-power designs Smart sensors Integration of BSN Challenges in BSN Bio-Sensor & SOC Source Recovery using ICA Sensed Signal Mixture Recovered Sources

14. Human Activity Human Activity

15. video skill names Human Activity Tracking & Recognition Human Activity TRACKING RECOGNITION Spatial Segmentation Temporal Segmentation joint angles Particle Filtering ANN, Hidden Markov Models (HMM)

16. Activity Tracking Human Activity • Particle Filters for Motion Tracking

17. video Activity Naming Video Activity Models A,B,C Activity Recognition Human Activity Learning Motion Vector sequence Segmented Contours Feature Extraction Segmentation Learning activity model A activity model B activity model C ANN/HMM Recognition Learned ANN/HMM

18. Activity Recognition (Ex.) Human Activity • Active Contour-based Segmentation TrainingData Set Sitting Standing Testing

19. CBIR Proactive Computing for Lifecare Proactive Lifecare Active Proactive Lifecare A Priori Knowledge - Models, Patterns - 치매 패턴, 징후 Bio Sensor Daily DB • Proactive Lifecare • Warning • Diagnostics • Prognostics Daily Activity DB Analysis and Processing for Dangerous Activity, Diseases Passive Proactive Lifecare

20. Content-based Information Retrieval Proactive Lifecare • Spatiotemporally Unstructured Data Mining • Content-based Information Retrieval (CBIR) • Daily DB로부터 content-based information retrieval 기술 • Digital Still Images, Video, Audio • ECG, EEG, EMG, Medical Images • Independent Component Analysis-based Content-based Information Retrieval

21. Constrained ICA-based CBIR Proactive Lifecare • No use of metadata where text labeling is used • We consider the database as mixed data and the query image acts as a reference for the cICA algorithm. • Constrained Independent Component Analysis (cICA) is used for extracting information from database.

22. 동양의학 Proactive Lifecare Proactive Lifecare 한의지식 온톨로지 및 추론 엔진 한의학의 정량적 데이터 1. 특정 Context에서의 정량적 데이터와 한의학 정성적 지식의 융합을 위한 알고리즘 연구 수집가능한, 해석가능한 데이터 (설진 데이터) 한의학적 정성적 지식 모델 2. 온톨로지 모델 설계 - 세부과제 및 전체과제의 요구분석을 위한 연구 선행 - Task 온톨로지 구축을 위한 연구 3. 온톨로지에 대한 질의 방법 및 API 개발 연구

23. RFID 센서 행위 추적 맥파 설진 스마트침대 수면정보 당뇨 MEMS SoC 센서모듈 프로액티브 컴퓨팅/추론 행위/생체 DB 동서신의학 라이프케어 시스템 Proactive Lifecare 홈 네트워크

24. East-West Neo Medical uLCRC Proactive Lifecare 동서신의학 기술에 기반한 u-라이프케어 연구센터 수면 환경 운동 … 영양 생체 센서 데이터 센 서 데 이 터 행위 추적 및 인식 동양의학 KB 서양의학 KB 추론엔진 행위 센서 데이터 u-라이프케어 서비스 정보 생체 DB 행위 DB 개인 이력 DB 환경 센서 데이터 서양의학 기반 생체정보 동양의학 기반 생체정보 데이터베이스(DB) 및 지식베이스(KB) 설진 맥파 … 혈액 정보 … 동서신의학 u-라이프케어 Proactive Computing