臺灣博碩士論文加值系統

本研究旨在建立一套勞工疲勞生物感測器與職場有害物監測穿戴式裝置，
以同時監測勞工在職場內的疲勞狀況及偵測職場環境之有害物，並探討勞工的
生理指標（包括血氧、皮膚溫度、血壓、心率、皮膚導電度及熱量消耗）與環
境有害物數值（包括一氧化碳、二氧化碳、二氧化氮、揮發性有機物、懸浮微
粒及臭氧）之間的相關性。
本研究選擇金屬製造業工廠的88名勞工，使用本研究建置的穿戴式有害物
感測裝置與穿戴式疲勞生物感測裝置進行現場資料收集。以SD卡收集每日勞
工暴露狀況與生理指標數據，將收集到的數據透過SPSS進行統計分析，以探
討勞工疲勞指標與有害物指標之間的相關性。
研究結果顯示，PM2.5暴露與A、B廠暴露組的勞工顯著相關於心跳與血壓
上升，但對DBP影響不一致；B廠暴露組及對照組的血氧濃度呈負相關，暗示
PM2.5可能影響心血管健康。CO2暴露與心跳、血壓下降有顯著相關，可能與勞
工疲勞相關；然而，NO2濃度低，未顯示對心血管指標的顯著影響，可能受感
測器靈敏度影響。O3暴露對心跳、血壓與血氧有影響，而TVOC暴露與心跳上
升有顯著相關性。另外，PM2.5和TVOC高低濃度組差異明顯，但對生理指標影
響不顯著（P > 0.05）； CO2和CO高濃度組對皮膚導電度有顯著影響（P < 0.05）。
本研究因現場勞工有害物暴露濃度較低，及有害物感測元件選擇不理想，
僅能發現皮膚導電度、皮膚溫度和熱量消耗三種生理指標有相關性，未來的研
究方向建議使用更具有指標性的有害物感測元件及選擇其他暴露濃度較高類型
的工廠。此外，建議若要選用臭氧和二氧化氮感測元件，必須要找有臭氧發生
之工廠，臭氧通常在太陽光或強電弧下生成，而二氧化氮則更容易在具有內燃
機或高溫燃燒的地方生成。這些情況在室內作業場所中較少出現，因此這兩種
物質的監測相對困難。

This study aimed to develop a wearable device integrating a worker fatigue biosensor and workplace hazardous substance monitor to simultaneously monitor worker fatigue and detect workplace hazards. It explored the correlations between physiological indicators (including blood oxygen, skin temperature, blood pressure, heart rate, skin conductance, and calorie expenditure) and environmental pollutant levels (including carbon monoxide, carbon dioxide, nitrogen dioxide, volatile organic compounds, particulate matter, and ozone).
The study involved 88 workers from metal manufacturing factories, using the developed wearable hazardous substance sensor and fatigue biosensor for on-site data collection. Data on daily worker exposure conditions and physiological indicators were collected via SD cards and statistically analyzed using SPSS to investigate correlations between fatigue and hazard indicators.
Results showed significant associations between PM2.5 exposure and increased heart rate and blood pressure among workers from Factories A and B, with inconsistent effects on diastolic blood pressure (DBP). Negative correlations were observed between PM2.5 exposure and blood oxygen levels in Factory B workers and the control group, suggesting a potential impact on cardiovascular health. CO2 exposure correlated significantly with decreased heart rate and blood pressure, possibly linked to worker fatigue. However, low NO2 concentrations did not show significant effects on cardiovascular indicators, likely due to sensor sensitivity. O3 exposure affected heart rate, blood pressure, and blood oxygen, while TVOC exposure correlated significantly with increased heart rate. Moreover, although there were notable differences in PM2.5 and TVOC concentrations between high and low exposure groups, these did not significantly affect physiological indicators (P > 0.05). High concentrations of CO2 and CO significantly affected skin conductance (P < 0.05).
Due to lower hazardous substance exposure levels and suboptimal sensor selection, the study only found correlations with skin conductance, skin temperature, and calorie expenditure. Future research should utilize more indicative hazardous substance sensors and target factories with higher exposure levels. Additionally, monitoring ozone and nitrogen dioxide should focus on facilities where these substances are more likely to be generated, such as those exposed to sunlight or high electrical arcs. These conditions are less common in indoor workplaces, posing challenges for their detection.

目錄VI
圖目錄VII
表目錄IX
第一章 前言1
第二章 文獻探討2
(一)疲勞感測器相關之文獻2
(二)有害物感測器相關文獻7
(三)應用感測裝置於作業場所相關暴露調查研究8
(四)有害物感測元件之相關研究11
(五) 化學性危害物與疲勞之相關研究12
第三章 研究方法14
(一)研究架構14
(二)工廠製程介紹14
(三)感測器介紹20
(四)統計方法26
第四章 結果與討論28
(一)有害物監測結果28
(二)生理指標監測結果31
(三)有害物與生理指標之相關性35
(四)研究限制43
第五章 結論45
第六章 參考文獻47

圖目錄
圖 1研究架構圖14
圖 2 A廠1號區15
圖 3 A廠2號區15
圖 4 A廠3號區16
圖 5 A廠4號區16
圖 6 A廠5號區17
圖 7 A廠6號區17
圖 8 A廠辦公室18
圖 9 B廠1區18
圖 10 B廠2區19
圖 11 B廠3號區19
圖 12 B廠4號區20
圖 13 B廠辦公室20
圖 14 穿戴式有害物感測裝置正面(左)、反面(右)外觀照22
圖 15 穿戴式有害物感測裝置頂部(上)、底部(下)外觀照22
圖 16 穿戴式有害物感測裝置內部元件示意圖23
圖 17穿戴式疲勞生物感測裝置正面外觀照片25
圖 18穿戴式疲勞感生物測裝置背面外觀照片25
圖 19 每日平均PM2.5濃度之時間趨勢圖29
圖 20 每日平均CO2濃度之時間趨勢圖29
圖 21 每日平均CO濃度之時間趨勢圖30
圖 22 每日平均NO2濃度之時間趨勢圖30
圖 23 每日平均O3濃度之時間趨勢圖31
圖 24 每日平均TVOC濃度之時間趨勢圖31
圖 25 每日平均心率之時間趨勢圖32
圖 26 每日平均血氧濃度之時間趨勢圖32
圖 27 每日平均皮膚導電度之時間趨勢圖33
圖 28 每日平均皮膚溫度之時間趨勢圖33
圖 29 每日平均收縮壓濃度之時間趨勢圖34
圖 30 每日平均舒張壓濃度之時間趨勢圖34
圖 31 每日平均熱量消耗之時間趨勢圖35

表目錄
表 1傳統採樣與感測器監測及其潛在應用之可行性分析8
表 2穿戴式有害物感測裝置感測元件之型號與規格24
表 3穿戴式疲勞生物感測裝置感測元件之型號與規格26
表 4 A廠PM2.5與各個生理指標之相關性36
表 5 B廠PM2.5與各個生理指標之相關性36
表 6 A廠CO2與各個生理指標之相關性36
表 7 B廠CO2與各個生理指標之相關性37
表 8 A廠CO與各個生理指標之相關性37
表 9 B廠CO與各個生理指標之相關性37
表 10 A廠NO2與各個生理指標之相關性38
表 11 B廠NO2與各個生理指標之相關性38
表 12 A廠O3與各個生理指標之相關性38
表 13 B廠O3與各個生理指標之相關性39
表 14 A廠TVOC與各個生理指標之相關性39
表 15 B廠TVOC與各個生理指標之相關性39
表 16 PM2.5高濃度組與低濃度組各生理指標之差異性40
表 17 TVOC高濃度組與低濃度組各生理指標之差異性41
表 18 CO2高濃度組與低濃度組各生理指標之差異性41
表 19 CO高濃度組與低濃度組各生理指標之差異性42
表 20 NO2高濃度組與低濃度組各生理指標之差異性42
表 21 O3高濃度組與低濃度組各生理指標之差異性43

[1] FINSTERER, Josef; MAHJOUB, Sinda Zarrouk. Fatigue in healthy and diseased
individuals. American Journal of Hospice and Palliative Medicine® , 2014, 31.5: 562575.
[2] ENOKA, Roger M.; DUCHATEAU, Jacques. Translating fatigue to human
performance. Med Sci Sports Exerc, 2016, 48.11: 2228-2238.
[3] WHITEHEAD, Lisa. The measurement of fatigue in chronic illness: a systematic
review of unidimensional and multidimensional fatigue measures. Journal of pain and symptom management, 2009, 37.1: 107-128.
[4] SMETS, E. M. A., et al. The Multidimensional Fatigue Inventory (MFI)
psychometric qualities of an instrument to assess fatigue. Journal of psychosomatic research, 1995, 39.3: 315-325.
[5] ESCORIHUELA, Rosa María, et al. Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis. Journal of translational medicine, 2020, 18.1: 1-12.
[6] VERLAAN, L., et al. Accelerometer-based physical activity monitoring in patients with knee osteoarthritis: objective and ambulatory assessment of actual physical activity during daily life circumstances. The open biomedicalengineering journal, 2015, 9: 157.
[7] ZIELINSKI, Mark R.; SYSTROM, David M.; ROSE, Noel R. Fatigue, sleep, and
autoimmune and related disorders. Frontiers in immunology, 2019, 10: 1827.
[8] VGONTZAS, Alexandros N., et al. Adverse effects of modest sleep restriction on sleepiness, performance, and inflammatory cytokines. The Journal of Clinical
Endocrinology & Metabolism, 2004, 89.5: 2119-2126.
[9] SHAFFER, Fred; GINSBERG, Jay P. An overview of heart rate variability metrics and norms. Frontiers in public health, 2017, 258.
[10] THAYER, J. F.; FISCHER, J. E. Heart rate variability, overnight urinary
norepinephrine and C‐reactive protein: evidence for the cholinergic anti‐inflammatory pathway in healthy human adults. Journal of internal medicine, 2013, 273.4: 382-392.
[11] HYNYNEN, E. S. A., et al. Heart rate variability during night sleep and after awakening in overtrained athletes. Medicine & Science in Sports & Exercise, 2013, 45.7: 1337-1343.
[12] UMAIR, Muhammad, et al. HRV and stress: A mixed-methods approach for
comparison of wearable heart rate sensors for biofeedback. IEEE Access, 2021, 9:
14005-14024.
[13] DRUMMOND, Grant R., et al. Immune mechanisms of hypertension. Nature
Reviews Immunology, 2019, 19.8: 517-532.
[14] AL-QATATSHEH, Ahmed, et al. Blood pressure sensors: Materials, fabrication
methods, performance evaluations and future perspectives. Sensors, 2020, 20.16:
4484.
[15] DING, Xiao-Rong, et al. Continuous blood pressure measurement from invasive
to unobtrusive: Celebration of 200th birth anniversary of Carl Ludwig. IEEE journal of biomedical and health informatics, 2016, 20.6: 1455-1465.
[16] PROENÇA, Martin, et al. Method-comparison study between a watch-like sensor
and a cuff-based device for 24-h ambulatory blood pressure monitoring. Scientific Reports, 2023, 13.1: 6149.( THOMAS, Simi Susan, et al. BioWatch—A wrist watch based signal acquisition system for physiological signals including blood pressure. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2014. p. 2286-2289.
[17] KUMAR, Ashok, et al. Flexible and wearable capacitive pressure sensor for
blood pressure monitoring. Sensing and Bio-Sensing Research, 2021, 33:
100434.( KIM, Joshua, et al. Soft wearable pressure sensors for beat‐to‐beat blood pressure monitoring. Advanced healthcare materials, 2019, 8.13: 1900109.
[18] ALLEN, John. Photoplethysmography and its application in clinical
physiological measurement. Physiological measurement, 2007, 28.3: R1.
[19] PUNJABI, Naresh M. The epidemiology of adult obstructive sleep apnea.
Proceedings of the American Thoracic Society, 2008, 5.2: 136-143.
[20] SAMEH, Radwa, et al. Design and implementation of an SPO2 based sensor for
heart monitoring using an android application. In: Journal of Physics: Conference Series. IOP Publishing, 2020. p. 012004.
[21] CASTELLANI, John W.; YOUNG, Andrew J. Human physiological responses to
cold exposure: Acute responses and acclimatization to prolonged exposure.
Autonomic Neuroscience, 2016, 196: 63-74.
[22] YOKOTA, Tomoyuki, et al. Ultraflexible, large-area, physiological temperature sensors for multipoint measurements. Proceedings of the National Academy of Sciences, 2015, 112.47: 14533-14538.
[23] SIBINSKI, Maciej; JAKUBOWSKA, Malgorzata; SLOMA, Marcin. Flexible
temperature sensors on fibers. Sensors, 2010, 10.9: 7934-7946.
[24] GOEDE, Henk, et al. Future prospects of occupational exposure modelling of
substances in the context of time-resolved sensor data. Annals of Work Exposures and Health, 2021, 65.3: 246-254.
[25] HUANG, Fu-Chuan, et al. Time location analysis for exposure assessment
studies of indoor workers based on active RFID technology. Journal of Environmental Monitoring, 2010, 12.2: 514-523.
[26] NEGI, Indira, et al. Novel monitor paradigm for real-time exposure assessment. Journal of exposure science & environmental epidemiology, 2011, 21.4: 419-426.
[27] ROSÉN, G., et al. A review of video exposure monitoring as an occupational
hygiene tool. Annals of Occupational Hygiene, 2005, 49.3: 201-217.
[28] BEURSKENS-COMUTH, Petra AWV; VERBIST, Koen; BROUWER, Derk.
Video exposure monitoring as part of a strategy to assess exposure to nanoparticles. Annals of occupational hygiene, 2011, 55.8: 937-945.
[29] LUN, Roanna. Human activity tracking and recognition using Kinect sensor.
2018.
[30] National Institute for Occupational Safety and Health (NIOSH). 2018. Right
sensors used right for occupational safety, health, well-being, and productivity. In Hoover MD, Cauda M, editor. NIOSH Center for Direct Reading and Sensor Technologies. United Steelworkers Health and Safety Conference, Pittsburgh.
[31] THOMAS, Geb W., et al. Low-cost, distributed environmental monitors for
factory worker health. Sensors, 2018, 18.5: 1411.
[32] ZUIDEMA, Christopher, et al. Estimating personal exposures from a multi
hazard sensor network. Journal of exposure science & environmental epidemiology,
2020, 30.6: 1013-1022.
[33] ZUIDEMA, Christopher, et al. Mapping occupational hazards with a multi
sensor network in a heavy-vehicle manufacturing facility. Annals of work exposures and health, 2019, 63.3: 280-293.
[34] HONEYCUTT, Wesley T.; LEY, M. Tyler; MATERER, Nicholas F. Precision and
limits of detection for selected commercially available, low-cost carbon dioxide and methane gas sensors. Sensors, 2019, 19.14: 3157.
[35] SERAFINI, Martina, et al. A wearable electrochemical gas sensor for ammonia
detection. Sensors, 2021, 21.23: 7905.
[36] REID, Michael, et al. NaDos: A real-time, wearable, personal exposure monitor for hazardous organic vapors. Sensors and Actuators B: Chemical, 2018, 255: 29963003.
[37] CHEN, Cheng, et al. A new sensor for the assessment of personal exposure to
volatile organic compounds. Atmospheric environment, 2012, 54: 679-687.
[38] THOMAS, Geb W., et al. Low-cost, distributed environmental monitors for
factory worker health. Sensors, 2018, 18.5: 1411.
[39] ZUIDEMA, Christopher, et al. Mapping occupational hazards with a multi
sensor network in a heavy-vehicle manufacturing facility. Annals of work exposures and health, 2019, 63.3: 280-293.
[40] CROSS, Eben S., et al. Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements. Atmospheric Measurement Techniques, 2017, 10.9: 3575-3588.
[41] ZIKOVA, Nadezda, et al. Estimating hourly concentrations of PM2. 5 across a
metropolitan area using low-cost particle monitors. Sensors, 2017, 17.8: 1922.
[42] IOANNIS, George., et al. Performance comparison of low-cost gas and particles sensors.2021.9.
[43] SHIM, In-Keun, et al. Prevalence of sick building syndrome symptoms and
subjective–objective indoor air quality of stores in underground shopping districts of Korea. Building and Environment, 2023, 228: 109882.
[44] MENTESE, Sibel, et al. A long-term multi-parametric monitoring study: Indoor air quality (IAQ) and the sources of the pollutants, prevalence of sick building syndrome (SBS) symptoms, and respiratory health indicators. Atmospheric Pollution Research, 2020, 11.12: 2270-2281.
[45] WOYKE, Simon, et al. Effects of carbon dioxide and temperature on the oxygen hemoglobin dissociation curve of human blood: Implications for avalanche victims. Frontiers in medicine, 2022, 8: 808025.