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RSA분석과 자율신경기능을 평가하는 호흡주기 설정에 관한 연구
이상명,이성준,안재목,김점근,Lee, Sang-Myung,Lee, Sung-Jun,Ahn, Jae-Mok,Kim, Jeom-Keun 대한의용생체공학회 2007 의공학회지 Vol.28 No.4
Heart rate variability(HRV) is the clinical consequence of various influences of the autonomic nervous system(ANS) on heart beat. HRV can estimate the potential physiologic rhythm from the interval between consecutive beats(RR interval or HRV data), but cardiovascular system governed by ANS is in relation to respiration and autonomic regulation. It is known as RSA representing respiration-related HR rhythmic oscillation. Because the mechanism linking the variability of HR to respiration is complex, it has so far been unknown well. In this paper, we tried to evaluate 5-min RR interval segments under control of respiration in order to find out a proper respiration rate that can estimate the ANS function. 10 healthy volunteers were included to evaluate 5-min HRV data under 4 different respiration-controlled environments; 0.03Hz, 0.1Hz, 0.2Hz, and 0.4Hz respiration. HRV data were analyzed both in the frequency and the time domain, with cross-correlation coefficient(cross-coeff.) for HRV and respiration signal. The results showed maximum cross-coeff. of 0.84 at 0.1 Hz and minimum that of 0.16 at 0.4Hz respiration. Cross-coeff was decreased at a faster rate from 0.1Hz respiration. All mean SDNN, RMSSD, and pNN50 of time domain measures were 108.7ms, 71.85ms, and 28.47%, respectively, and LF, HF, and TP of frequency domain measures were $12,722ms^2,\;658.8ms^2$, and $7,836.64ms^2$ at 0.1Hz respiration, respectively. In conclusion, 0.1Hz respiration was observed to be very meaningful from time domain and frequency domain analysis in relation to respiration and autonomic regulation of the heart.
사례 연구: 녹거노인 일상 활동 모니터링 시스템의 실제 주택에서의 장기간 실험
이선우(Seon-Woo Lee),옥대윤(Dae-Yoon Ok),정필환(Philhwan Jung),김점근(Jeom-Keun Kim) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.8
This paper describes analysis of long-term experiments on a monitoring system to assess the daily activities of the elderly who live alone. The developed system is composed of an in-house sensing system and a server system. The in-house sensing system installed in their own houses is a typical wireless sensor network system including three kinds of wireless sensors. The server system has a database server and an assessment server. We have installed the system into an elderly house, collected data during over two years continuously, then analyze the data. From the analysis, we could measure the energy consumption profile of three kinds of sensor nodes. The experiment shows all kinds of nodes can operate over one year with two AA-size alkaline batteries. Using a measure of reliability of the monitoring system called ‘deadzone’, the system has showed the failure operation for 842 hours (4.66 %) during over 18,000 hours total operation period.