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  • 學位論文

嵌入式系統於光生物刺激與醫療器材及輔具應用

The applications of embedded system in photobiostimulation, medical and assistive devices

指導教授 : 方煒
本文將於2024/10/21開放下載。若您希望在開放下載時收到通知,可將文章加入收藏

摘要


本篇論文主要是利用嵌入式系統,進行低功率光生物刺激與生理訊號量測的相關性研究與應用,並開發醫療器材及導盲輔具。開發系統主要包括微處理器、光驅動電路、生理訊號量測模組。其中以低功率LED或laser做為光生物刺激源,探討低能量光刺激對人體的生理影響。生理訊號量測主要有腦電信號 (EEG)、心電信號 (ECG)及光體積變化信號 (PPG)。根據擷取的生理信號分析腦波、心律變異及自律神經狀態。設計的系統可將數位化的資料透過RS232通訊傳至外部電腦,做進一步儲存、分析與顯示。 實驗結果發現 LED (850 nm)刺激會引起大腦枕葉、頂葉和顳葉區域的alpha (8-13 Hz)活動發生顯著變化。Theta (4-7 Hz) 功率在大腦的後部區域也有顯著增加。當光刺激停止後,影響效果至少持續15分鐘。比較低功率LED (850 nm, 30 mW/pcs, pulse frequency 10 Hz)與低功率laser (830 nm, 7 mW/pcs, pulse frequency 10 Hz)刺激的研究,LED與laser對alpha (8-13 Hz)活動具有相似的影響。 在醫療器材開發方面,首先完成ECG量測和低功率LED驅動的裝置,以LED (850 nm) 光照射受測者內關穴,並使用ECG信號偵測心律變異 (HRV)。比較LED照射前後HRV數值,顯示自律神經 (ANS)狀態有影響。接著開發PPG信號量測和低功率LED刺激的裝置,比較PPG信號及市售的ECG的量測結果,發現兩者的HRV指標的一致性程度高。顯示健康的受測者安靜坐在椅子上,PPG信號可取代ECG信號來量測心律。另外實驗結果顯示以LED (850 nm) 光照射左手掌心,會影響自律神經系統 (ANS),交感神經系統 (SNS)的狀態有提升,後續仍需做更多測試驗證。 此外,結合雷射光學與攝影機,開發一個具有偵測距離和障礙物寬度的導盲輔具,並將結果以語音方式通知使用者。實驗結果顯示,在40和120 cm範圍內,偵測距離的標準差小於5%,偵測障礙物寬度的標準差小於4.5%。設計系統具有架構簡單、非手持及低成本的優點。

並列摘要


This dissertation introduces the research and application on an embedded system with low-power photo biostimulation and physiological signal measurement, as well as the development of medical devices and assistive device. The developed system includes a microprocessor, a photo drive circuit, and a physiological signal measured module. Among this, the low-power LED and laser are used as the photo biostimulation sources, to discuss their physiological effects on people. Physiological signal measurements include electroencephalography (EEG), electrocardiography (ECG) and photoplethysmography (PPG). The brain waves, heart rate variability, and autonomic nervous state can be analyzed from the physiological signals. This system can transmit the digitized data to the external computer through RS232 communication for further storage, analysis and display. The test results show that LED (850 nm) stimulation can evoke the significant variety in alpha (8-13 Hz) activity in the occipital, parietal and temporal lobes of the brain. Theta (4-7 Hz) power increases significantly in the posterior region. After the stimulation ceases, the latent effect can last for at least 15 minutes. Compared with the study of low-power laser stimulation (830 nm, 7 mW/pcs, pulse frequency 10 Hz), low-power LED (850 nm, 30 mW/pcs, pulse frequency 10 Hz) has a similar effect on alpha (8-13 Hz) activity. In terms of medical device, firstly, an embedded system with ECG measurement and low-power LED drive module was developed. To stimulate Neiguan point (PC6) with LED (850 nm) light and to measure the heart rate variability (HRV). HRV metrics were compared before and after LED irradiation, the status of the autonomic nervous system (ANS) was affected. Next, PPG-based recording and low-power LED stimulation system was designed. It is found that HRV metrics between PPG and ECG shows a high agreement. It shows that healthy subjects sit quietly, and PPG signal can used to measure the HRV instead of ECG signal. The test showed that ANS activity was affected by LED (850 nm) stimulation at the palm, and the sympathetic nervous system (SNS) was enhanced, but more test and verification are needed in the future. In addition, a blind guidance prototype with camera and laser for distance and obstacle width detection was proposed, and the user can be notified by the voice of the system. According to the experimental results, the standard deviation of the distance measurement is less than 5%, and the standard deviation of the obstacle width measurement is less than 4.5% within the range of 40 and 120 cm. The designed system has the advantages that including simple configuration, non-handheld, and low cost.

並列關鍵字

embedded system low-power photobiostimulation HRV ANS EEG ECG PPG

參考文獻


1. Aasthik Upadhyay, Abhimanyu S. Dhapola. Embedded Systems and its Application in Medical Field in Emerging Trends in Computer Science and Information Systems(NCETCSIS-2015), March 2015.
2. W. O. Tatum, A. M. Husain, S. R. Benbadis, and P. W. Kaplan. 2007. Handbook of EEG INTERPRETATION.
3. C. W. Mundt, K. N. Montgomery, U. E. Udoh et al. 2005. A Multiparameter Wearable Physiologic Monitoring System for Space and Terrestrial Applications. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE. VOL. 9, NO. 3.
4. M. Teplan. 2002. FUNDAMENTALS OF EEG MEASUREMENT. MEASUREMENT SCIENCE REVIEW. Volume 2, Section 2.
5. C. Baker, K. Armijo, S. Belka, M. Benhabib, V. Bhargava, N. Burkhart, A. Der Minassians, G. Dervisoglu, L. Gutnik, M. Haick, C. Ho, M. Koplow, J. Mangold, S. Robinson, M. Rosa, M. Schwartz, C. Sims, H. Stoffregen, A. Waterbury, E. Leland, T. Pering, and P. Wright. 2007. Wireless sensor networks for home health care. 21st International Conference on Advanced Information Networking and Applications Workshops. Vol. 2, pp. 832–837.

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