Design of flexible wearable sensing systems_Journal of Biomedical Engineering

Authors：

CHEN Hongyu ^1,2 , WANG Zaihao ² , MENG Long ² , XU Ke ⁴ , WANG Zeyu ⁴ , CHEN Chen ³ ,  CHEN Wei ^1,2,3

1. Greater Bay Area Institute of Precision Medicine, Guangzhou 511466, P. R. China;
2. Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, P. R. China;
3. Human Phenome Institute, Fudan University, Shanghai 201203, P. R. China;
4. Imperial College London, London SW7 2AZ, UK;

Corresponding author：

CHEN Wei, Email: w_chen@fudan.edu.cn

Keywords：

Flexible wearable; Body sensing network; Physiological signal monitoring; Motion signal monitoring

DOI：

10.7507/1001-5515.202208012

Video：

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The aging population and the increasing prevalence of chronic diseases in the elderly have brought a significant economic burden to families and society. The non-invasive wearable sensing system can continuously and real-time monitor important physiological signs of the human body and evaluate health status. In addition, it can provide efficient and convenient information feedback, thereby reducing the health risks caused by chronic diseases in the elderly. A wearable system for detecting physiological and behavioral signals was developed in this study. We explored the design of flexible wearable sensing technology and its application in sensing systems. The wearable system included smart hats, smart clothes, smart gloves, and smart insoles, achieving long-term continuous monitoring of physiological and motion signals. The performance of the system was verified, and the new sensing system was compared with commercial equipment. The evaluation results demonstrated that the proposed system presented a comparable performance with the existing system. In summary, the proposed flexible sensor system provides an accurate, detachable, expandable, user-friendly and comfortable solution for physiological and motion signal monitoring. It is expected to be used in remote healthcare monitoring and provide personalized information monitoring, disease prediction, and diagnosis for doctors/patients.

Citation： CHEN Hongyu, WANG Zaihao, MENG Long, XU Ke, WANG Zeyu, CHEN Chen, CHEN Wei. Design of flexible wearable sensing systems. Journal of Biomedical Engineering, 2023, 40(6): 1071-1083. doi: 10.7507/1001-5515.202208012 Copy

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3.	Yang G. Body sensor networks. London: Springer, 2006.
4.	Gope P, Hwang T. BSN-Care: A secure IoT-based modern healthcare system using body sensor network. IEEE Sens J, 2015, 16(5): 1368-1376.
5.	Ng W C, Seet H L, Lee K S, et al. Micro-spike EEG electrode and the vacuum-casting technology for mass production. J Mater Process Tech, 2009, 209(9): 4434-4438.
6.	Lin C T, Liao L D, Liu Y H, et al. Novel dry polymer foam electrodes for long-term EEG measurement. IEEE Trans Biomed Eng, 2010, 58(5): 1200-1207.
7.	Fiedler P, Mühle R, Griebel S, et al. Contact pressure and flexibility of multipin dry EEG electrodes. IEEE Trans Neural Syst Rehabil Eng g, 2018, 26(4): 750-757.
8.	Pandian P S, Mohanavelu K, Safeer K P, et al. Smart Vest: Wearable multi-parameter remote physiological monitoring system. Med Eng Phys, 2008, 30(4): 466-477.
9.	Yamamoto Y, Yamamoto D, Takada M, et al. Efficient skin temperature sensor and stable gel‐less sticky ECG sensor for a wearable flexible healthcare patch. Adv Healthc Mater, 2017, 6(17): 1700495.
10.	Pani D, Dessì A, Saenz-Cogollo J F, et al. Fully textile, PEDOT: PSS based electrodes for wearable ECG monitoring systems. IEEE Trans Biomed Eng, 2015, 63(3): 540-549.
11.	Castrillón R, Pérez J J, Andrade-Caicedo H. Electrical performance of PEDOT: PSS-based textile electrodes for wearable ECG monitoring: a comparative study. Biomed Eng Online, 2018, 17(1): 1-23.
12.	Min S D, Yun Y, Shin H. Simplified structural textile respiration sensor based on capacitive pressure sensing method. IEEE Sens J, 2014, 14(9): 3245-3251.
13.	Di H, Sun S, Che Y. Respiration measurement using fibre-optic deformation sensor. J Mod Optic, 2017, 64(6): 639-645.
14.	Lee Y K, Shin H S, Jo J, et al. Development of a wristwatch-type PPG array sensor module// 2011 IEEE International Conference on Consumer Electronics-Berlin (ICCE-Berlin). Berlin: IEEE, 2011: 168-171.
15.	Tamura T, Maeda Y, Sekine M, et al. Wearable photoplethysmographic sensors—past and present. Electronics, 2014, 3(2): 282-302.
16.	Thomas S S, Nathan V, Zong C, et al. BioWatch: A noninvasive wrist-based blood pressure monitor that incorporates training techniques for posture and subject variability. IEEE J Biomed Health Inform, 2015, 20(5): 1291-1300.
17.	Lu J, Zheng X, Sheng M, et al. Efficient human activity recognition using a single wearable sensor. IEEE IoT J, 2020, 7(11): 11137-11146.
18.	Tian Y, Wang X, Chen W, et al. Adaptive multiple classifiers fusion for inertial sensor based human activity recognition. Cluster Comput, 2019, 22(4): 8141-8154.
19.	Laudanski A, Brouwer B, Li Q. Activity classification in persons with stroke based on frequency features. Med Eng Phys, 2015, 37(2): 180-186.
20.	Zhang Q, Wang Y L, Xia Y, et al. A low-cost and highly integrated sensing insole for plantar pressure measurement. Sens Bio-Sens Res, 2019, 26: 100298.
21.	Chen H, Zhao Y, Mei Z, et al. Characterization of a novel carbonized foam electrode for wearable bio-potential recording// 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Las Vegas: IEEE, 2018: 173-176.
22.	Chen H, Bao S, Ma J, et al. A wearable daily respiration monitoring system using PDMS-graphene compound tensile sensor for adult// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019: 1269-1273.
23.	Bao S, Yin S, Chen H, et al. A wearable multimode system with soft sensors for lower limb activity evaluation and rehabilitation//2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Berlin: IEEE, 2018: 1-6.
24.	Wang Z, Chen C, Li W, et al. A multichannel EEG acquisition system with novel Ag NWs/PDMS flexible dry electrodes// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE, 2018: 1299-1302.
25.	Wang L F, Liu J Q, Yang B, et al. PDMS-based low cost flexible dry electrode for long-term EEG measurement. IEEE Sens J, 2012, 12(9): 2898-2904.
26.	Larmagnac A, Eggenberger S, Janossy H, et al. Stretchable electronics based on Ag-PDMS composites. Sci Rep, 2014, 4(1): 1-7.
27.	Yu Y H, Chen S H, Chang C L, et al. New flexible silicone-based EEG dry sensor material compositions exhibiting improvements in lifespan, conductivity, and reliability. Sensors, 2016, 16(11): 1826.
28.	Yuan W, Gu W, Lin J, et al. 49‐3L: Late‐News Paper: Flexible and stretchable hybrid electronics systems for wearable applications. SID Symp Dig Tech Papers, 2016, 47(1): 668-671.
29.	Yuan W, Gu W, Lin J, et al. Printed flexible and stretchable hybrid electronic systems for wearable applications// 2016 6th Electronic System-Integration Technology Conference (ESTC). Grenoble: IEEE, 2016: 1-4.
30.	Boon P, Van Hoey G, Vanrumste B, et al. True versus standard international 10-20 EEG electrode positions and the spherical head model. Electroenceph Clin Neurophysiol, 1997, 103(1): 196-197.
31.	Puurtinen M, Viik J, Hyttinen J. Best electrode locations for a small bipolar ECG device: signal strength analysis of clinical data. Ann Biomed Eng, 2009, 37(2): 331-336.
32.	Tang Y, Li D, Ao D, et al. Ultralight, highly flexible and conductive carbon foams for high performance electromagnetic shielding application. J Mater Sci, 2018, 29(16): 13643-13652.
33.	Liu W, Liu N, Yue Y, et al. A flexible and highly sensitive pressure sensor based on elastic carbon foam. J Mater Chem C, 2018, 6(6): 1451-1458.
34.	Wakuda D, Suganuma K. Stretchable fine fiber with high conductivity fabricated by injection forming. Appl Phys Lett, 2011, 98(7): 33.
35.	Li Q, Tao X. A stretchable knitted interconnect for three-dimensional curvilinear surfaces. Text Res J, 2011, 81(11): 1171-1182.
36.	Zhu D, Bieger J, Molina G G, et al. A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci, 2010, 2010: 702357.
37.	Fang Z, Cao X, Li C, et al. Investigation of carbon foams as microwave absorber: Numerical prediction and experimental validation. Carbon, 2006, 44(15): 3368-3370.

1. Bloom D E, Cafiero E, Jané-Llopis E, et al. The global economic burden of noncommunicable diseases. Geneva: World Economic Forum, 2012.
2. Prince M J, Wu F, Guo Y, et al. The burden of disease in older people and implications for health policy and practice. The Lancet, 2015, 385(9967): 549-562.
3. Yang G. Body sensor networks. London: Springer, 2006.
4. Gope P, Hwang T. BSN-Care: A secure IoT-based modern healthcare system using body sensor network. IEEE Sens J, 2015, 16(5): 1368-1376.
5. Ng W C, Seet H L, Lee K S, et al. Micro-spike EEG electrode and the vacuum-casting technology for mass production. J Mater Process Tech, 2009, 209(9): 4434-4438.
6. Lin C T, Liao L D, Liu Y H, et al. Novel dry polymer foam electrodes for long-term EEG measurement. IEEE Trans Biomed Eng, 2010, 58(5): 1200-1207.
7. Fiedler P, Mühle R, Griebel S, et al. Contact pressure and flexibility of multipin dry EEG electrodes. IEEE Trans Neural Syst Rehabil Eng g, 2018, 26(4): 750-757.
8. Pandian P S, Mohanavelu K, Safeer K P, et al. Smart Vest: Wearable multi-parameter remote physiological monitoring system. Med Eng Phys, 2008, 30(4): 466-477.
9. Yamamoto Y, Yamamoto D, Takada M, et al. Efficient skin temperature sensor and stable gel‐less sticky ECG sensor for a wearable flexible healthcare patch. Adv Healthc Mater, 2017, 6(17): 1700495.
10. Pani D, Dessì A, Saenz-Cogollo J F, et al. Fully textile, PEDOT: PSS based electrodes for wearable ECG monitoring systems. IEEE Trans Biomed Eng, 2015, 63(3): 540-549.
11. Castrillón R, Pérez J J, Andrade-Caicedo H. Electrical performance of PEDOT: PSS-based textile electrodes for wearable ECG monitoring: a comparative study. Biomed Eng Online, 2018, 17(1): 1-23.
12. Min S D, Yun Y, Shin H. Simplified structural textile respiration sensor based on capacitive pressure sensing method. IEEE Sens J, 2014, 14(9): 3245-3251.
13. Di H, Sun S, Che Y. Respiration measurement using fibre-optic deformation sensor. J Mod Optic, 2017, 64(6): 639-645.
14. Lee Y K, Shin H S, Jo J, et al. Development of a wristwatch-type PPG array sensor module// 2011 IEEE International Conference on Consumer Electronics-Berlin (ICCE-Berlin). Berlin: IEEE, 2011: 168-171.
15. Tamura T, Maeda Y, Sekine M, et al. Wearable photoplethysmographic sensors—past and present. Electronics, 2014, 3(2): 282-302.
16. Thomas S S, Nathan V, Zong C, et al. BioWatch: A noninvasive wrist-based blood pressure monitor that incorporates training techniques for posture and subject variability. IEEE J Biomed Health Inform, 2015, 20(5): 1291-1300.
17. Lu J, Zheng X, Sheng M, et al. Efficient human activity recognition using a single wearable sensor. IEEE IoT J, 2020, 7(11): 11137-11146.
18. Tian Y, Wang X, Chen W, et al. Adaptive multiple classifiers fusion for inertial sensor based human activity recognition. Cluster Comput, 2019, 22(4): 8141-8154.
19. Laudanski A, Brouwer B, Li Q. Activity classification in persons with stroke based on frequency features. Med Eng Phys, 2015, 37(2): 180-186.
20. Zhang Q, Wang Y L, Xia Y, et al. A low-cost and highly integrated sensing insole for plantar pressure measurement. Sens Bio-Sens Res, 2019, 26: 100298.
21. Chen H, Zhao Y, Mei Z, et al. Characterization of a novel carbonized foam electrode for wearable bio-potential recording// 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Las Vegas: IEEE, 2018: 173-176.
22. Chen H, Bao S, Ma J, et al. A wearable daily respiration monitoring system using PDMS-graphene compound tensile sensor for adult// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019: 1269-1273.
23. Bao S, Yin S, Chen H, et al. A wearable multimode system with soft sensors for lower limb activity evaluation and rehabilitation//2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Berlin: IEEE, 2018: 1-6.
24. Wang Z, Chen C, Li W, et al. A multichannel EEG acquisition system with novel Ag NWs/PDMS flexible dry electrodes// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE, 2018: 1299-1302.
25. Wang L F, Liu J Q, Yang B, et al. PDMS-based low cost flexible dry electrode for long-term EEG measurement. IEEE Sens J, 2012, 12(9): 2898-2904.
26. Larmagnac A, Eggenberger S, Janossy H, et al. Stretchable electronics based on Ag-PDMS composites. Sci Rep, 2014, 4(1): 1-7.
27. Yu Y H, Chen S H, Chang C L, et al. New flexible silicone-based EEG dry sensor material compositions exhibiting improvements in lifespan, conductivity, and reliability. Sensors, 2016, 16(11): 1826.
28. Yuan W, Gu W, Lin J, et al. 49‐3L: Late‐News Paper: Flexible and stretchable hybrid electronics systems for wearable applications. SID Symp Dig Tech Papers, 2016, 47(1): 668-671.
29. Yuan W, Gu W, Lin J, et al. Printed flexible and stretchable hybrid electronic systems for wearable applications// 2016 6th Electronic System-Integration Technology Conference (ESTC). Grenoble: IEEE, 2016: 1-4.
30. Boon P, Van Hoey G, Vanrumste B, et al. True versus standard international 10-20 EEG electrode positions and the spherical head model. Electroenceph Clin Neurophysiol, 1997, 103(1): 196-197.
31. Puurtinen M, Viik J, Hyttinen J. Best electrode locations for a small bipolar ECG device: signal strength analysis of clinical data. Ann Biomed Eng, 2009, 37(2): 331-336.
32. Tang Y, Li D, Ao D, et al. Ultralight, highly flexible and conductive carbon foams for high performance electromagnetic shielding application. J Mater Sci, 2018, 29(16): 13643-13652.
33. Liu W, Liu N, Yue Y, et al. A flexible and highly sensitive pressure sensor based on elastic carbon foam. J Mater Chem C, 2018, 6(6): 1451-1458.
34. Wakuda D, Suganuma K. Stretchable fine fiber with high conductivity fabricated by injection forming. Appl Phys Lett, 2011, 98(7): 33.
35. Li Q, Tao X. A stretchable knitted interconnect for three-dimensional curvilinear surfaces. Text Res J, 2011, 81(11): 1171-1182.
36. Zhu D, Bieger J, Molina G G, et al. A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci, 2010, 2010: 702357.
37. Fang Z, Cao X, Li C, et al. Investigation of carbon foams as microwave absorber: Numerical prediction and experimental validation. Carbon, 2006, 44(15): 3368-3370.

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