Araştırma Makalesi
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Yıl 2021, Cilt: 3 Sayı: Special Issue: Full Papers of 2nd International Congress of Updates in Biomedical Engineering, 87 - 93, 13.01.2021

Öz

Kaynakça

  • 1. D. L. Hall ve S. A. McMullen, Mathematical techniques in multisensor data fusion. Artech House, 2004.
  • 2. W. Elmenreich, “An introduction to sensor fusion”, Vienna University of Technology, Austria, c. 502, ss. 1–28, 2002.
  • 3. B. R. Bracio, W. Horn, ve D. P. Moller, “Sensor fusion in biomedical systems”, içinde Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.’Magnificent Milestones and Emerging Opportunities in Medical Engineering’(Cat. No. 97CH36136), 1997, c. 3, ss. 1387–1390.
  • 4. J. J. A. Mendes Jr, M. E. M. Vieira, M. B. Pires, ve S. L. Stevan Jr, “Sensor fusion and smart sensor in sports and biomedical applications”, Sensors, c. 16, sy 10, s. 1569, 2016.
  • 5. J. A. Castellanos ve J. D. Tardos, Mobile robot localization and map building: A multisensor fusion approach. Springer Science & Business Media, 2012.
  • 6. G. Retscher, “Multi-sensor systems for pedestrian navigation”, içinde Proceedings of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2004), 2004, ss. 1076–1087.
  • 7. P. Li, R. Meziane, M. J.-D. Otis, H. Ezzaidi, ve P. Cardou, “A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection”, içinde 2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings, 2014, ss. 55–60.
  • 8. H. Weiqi, F. Y. Kai, C. Zhi-En, A. A. P. Wai, ve C. Sher-Yi, “Multimodal Sensory Headband for Personalized Relaxation Management”, içinde Proceedings of the international Convention on Rehabilitation Engineering & Assistive Technology, 2015, ss. 1–4.
  • 9. J. Khan, M. U. G. Khan, R. Iqbal, ve O. Riaz, “Robust Multi-sensor Fusion for the Development of EEG Controlled Vehicle”, IEEE Sensors Journal, 2020.
  • 10. M. Derawi ve I. Voitenko, “Fusion of gait and ECG for biometric user authentication”, içinde 2014 International Conference of the Biometrics Special Interest Group (BIOSIG), 2014, ss. 1–4.
  • 11. A. El Ali vd., “ThermalWear: Exploring Wearable On-chest Thermal Displays to Augment Voice Messages with Affect”, içinde Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, ss. 1–14.
  • 12. G. Cosoli, S. Spinsante, ve L. Scalise, “Wrist-worn and chest-strap wearable devices: Systematic review on accuracy and metrological characteristics”, Measurement, s. 107789, 2020.
  • 13. J. C. Alvarez, R. C. González, D. Alvarez, A. M. López, ve J. Rodríguez-Uría, “Multisensor approach to walking distance estimation with foot inertial sensing”, içinde 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, ss. 5719– 5722.
  • 14. B. Zhou vd., “Smart soccer shoe: monitoring foot-ball interaction with shoe integrated textile pressure sensor matrix”, içinde Proceedings of the 2016 ACM International Symposium on Wearable Computers, 2016, ss. 64–71.
  • 15. B. N. Balmain vd., “Using smart socks to detect step-count at slow walking speeds in healthy adults”, International journal of sports medicine, c. 40, sy 02, ss. 133–138, 2019.
  • 16. K. S. Abhishek, L. C. K. Qubeley, ve D. Ho, “Glove-based hand gesture recognition sign language translator using capacitive touch sensor”, içinde 2016 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC), 2016, ss. 334–337.
  • 17. C. Eckhardt, J. Sullivan, ve K. Pietroszek, “Flex: hand gesture recognition using muscle flexing sensors”, içinde Proceedings of the 5th Symposium on Spatial User Interaction, 2017, ss. 164–164.
  • 18. D. Phan, L. Y. Siong, P. N. Pathirana, ve A. Seneviratne, “Smartwatch: Performance evaluation for long-term heart rate monitoring”, içinde 2015 International symposium on bioelectronics and bioinformatics (ISBB), 2015, ss. 144–147.
  • 19. M. R. Ahsan, M. I. Ibrahimy, ve O. O. Khalifa, “EMG signal classification for human computer interaction: a review”, European Journal of Scientific Research, c. 33, sy 3, ss. 480–501, 2009.
  • 20. T. Subba ve T. S. Chingtham, “A Survey: EMG Signal-Based Controller for Human–Computer Interaction”, içinde Advances in Communication, Cloud, and Big Data, Springer, 2019, ss. 117– 125.
  • 21. J. Ryu, J. Seo, H. Jebelli, ve S. Lee, “Automated action recognition using an accelerometer- embedded wristband-type activity tracker”, Journal of construction engineering and management, c. 145, sy 1, s. 04018114, 2019.
  • 22. S. Zang, Q. Wang, Q. Mi, J. Zhang, ve X. Ren, “A facile, precise radial artery pulse sensor based on stretchable graphene-coated fiber”, Sensors and Actuators A: Physical, c. 267, ss. 532–537, 2017.
  • 23. W. Boucsein, Electrodermal Activity. Springer Science & Business Media, 2012.

Multi-Sensor Glove Design and Bio-Signal Data Collection

Yıl 2021, Cilt: 3 Sayı: Special Issue: Full Papers of 2nd International Congress of Updates in Biomedical Engineering, 87 - 93, 13.01.2021

Öz

In many fields such as biomedical, robotics, mobile devices, multi-sensor systems are used to solve problems that have low performance when performed with a single sensor. These systems are used in many applications like pedometers, emotion recognition and navigation. In this paper a multi-sensor glove system is proposed to measure stress and effort parameters of a person. The multi-system includes sensors for galvanic skin response (GSR), oximeter and inertial measurement unit (IMU). The GSR sensor simply measures the electrical conductivity of the skin, which increases when sweating due to the salt in the sweat. The GSR sensor is placed on the glove with the index finger. The oximeter sensor is used to measure the heart rate and blood oxygen saturation. It is an infrared sensor and measures the reflecting infrared light from the blood cells. The heart rate sensor is used to detect both effort and stress levels based on the heart pulse rate. The IMU is a ready-to-use multi-sensor sensor that includes a gyroscope and an accelerometer. In this study an IMU sensor with 6 degrees of freedom was used to measure acceleration and angular rotation values generated by hand movements. All these sensors are connected to a microcontroller. Due to the lowest sampling rate of the multi-sesnor system, the IMU sensor, includes the entire system configured for measurement at 100Hz. These measurements are combined on the microcontroller and sent to the computer via Bluetooth. The computer program stores the incoming data and visualizes the individual channels simultaneously. Measurements were taken during standing, walking, climbing and jumping activities performed by wearing a multi-sensor glove. It has been observed that measurements can be taken successfully from all sensors on the system.

Kaynakça

  • 1. D. L. Hall ve S. A. McMullen, Mathematical techniques in multisensor data fusion. Artech House, 2004.
  • 2. W. Elmenreich, “An introduction to sensor fusion”, Vienna University of Technology, Austria, c. 502, ss. 1–28, 2002.
  • 3. B. R. Bracio, W. Horn, ve D. P. Moller, “Sensor fusion in biomedical systems”, içinde Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.’Magnificent Milestones and Emerging Opportunities in Medical Engineering’(Cat. No. 97CH36136), 1997, c. 3, ss. 1387–1390.
  • 4. J. J. A. Mendes Jr, M. E. M. Vieira, M. B. Pires, ve S. L. Stevan Jr, “Sensor fusion and smart sensor in sports and biomedical applications”, Sensors, c. 16, sy 10, s. 1569, 2016.
  • 5. J. A. Castellanos ve J. D. Tardos, Mobile robot localization and map building: A multisensor fusion approach. Springer Science & Business Media, 2012.
  • 6. G. Retscher, “Multi-sensor systems for pedestrian navigation”, içinde Proceedings of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2004), 2004, ss. 1076–1087.
  • 7. P. Li, R. Meziane, M. J.-D. Otis, H. Ezzaidi, ve P. Cardou, “A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection”, içinde 2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings, 2014, ss. 55–60.
  • 8. H. Weiqi, F. Y. Kai, C. Zhi-En, A. A. P. Wai, ve C. Sher-Yi, “Multimodal Sensory Headband for Personalized Relaxation Management”, içinde Proceedings of the international Convention on Rehabilitation Engineering & Assistive Technology, 2015, ss. 1–4.
  • 9. J. Khan, M. U. G. Khan, R. Iqbal, ve O. Riaz, “Robust Multi-sensor Fusion for the Development of EEG Controlled Vehicle”, IEEE Sensors Journal, 2020.
  • 10. M. Derawi ve I. Voitenko, “Fusion of gait and ECG for biometric user authentication”, içinde 2014 International Conference of the Biometrics Special Interest Group (BIOSIG), 2014, ss. 1–4.
  • 11. A. El Ali vd., “ThermalWear: Exploring Wearable On-chest Thermal Displays to Augment Voice Messages with Affect”, içinde Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, ss. 1–14.
  • 12. G. Cosoli, S. Spinsante, ve L. Scalise, “Wrist-worn and chest-strap wearable devices: Systematic review on accuracy and metrological characteristics”, Measurement, s. 107789, 2020.
  • 13. J. C. Alvarez, R. C. González, D. Alvarez, A. M. López, ve J. Rodríguez-Uría, “Multisensor approach to walking distance estimation with foot inertial sensing”, içinde 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, ss. 5719– 5722.
  • 14. B. Zhou vd., “Smart soccer shoe: monitoring foot-ball interaction with shoe integrated textile pressure sensor matrix”, içinde Proceedings of the 2016 ACM International Symposium on Wearable Computers, 2016, ss. 64–71.
  • 15. B. N. Balmain vd., “Using smart socks to detect step-count at slow walking speeds in healthy adults”, International journal of sports medicine, c. 40, sy 02, ss. 133–138, 2019.
  • 16. K. S. Abhishek, L. C. K. Qubeley, ve D. Ho, “Glove-based hand gesture recognition sign language translator using capacitive touch sensor”, içinde 2016 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC), 2016, ss. 334–337.
  • 17. C. Eckhardt, J. Sullivan, ve K. Pietroszek, “Flex: hand gesture recognition using muscle flexing sensors”, içinde Proceedings of the 5th Symposium on Spatial User Interaction, 2017, ss. 164–164.
  • 18. D. Phan, L. Y. Siong, P. N. Pathirana, ve A. Seneviratne, “Smartwatch: Performance evaluation for long-term heart rate monitoring”, içinde 2015 International symposium on bioelectronics and bioinformatics (ISBB), 2015, ss. 144–147.
  • 19. M. R. Ahsan, M. I. Ibrahimy, ve O. O. Khalifa, “EMG signal classification for human computer interaction: a review”, European Journal of Scientific Research, c. 33, sy 3, ss. 480–501, 2009.
  • 20. T. Subba ve T. S. Chingtham, “A Survey: EMG Signal-Based Controller for Human–Computer Interaction”, içinde Advances in Communication, Cloud, and Big Data, Springer, 2019, ss. 117– 125.
  • 21. J. Ryu, J. Seo, H. Jebelli, ve S. Lee, “Automated action recognition using an accelerometer- embedded wristband-type activity tracker”, Journal of construction engineering and management, c. 145, sy 1, s. 04018114, 2019.
  • 22. S. Zang, Q. Wang, Q. Mi, J. Zhang, ve X. Ren, “A facile, precise radial artery pulse sensor based on stretchable graphene-coated fiber”, Sensors and Actuators A: Physical, c. 267, ss. 532–537, 2017.
  • 23. W. Boucsein, Electrodermal Activity. Springer Science & Business Media, 2012.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ahmet Çağdaş Seçkin 0000-0002-9849-3338

Yayımlanma Tarihi 13 Ocak 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 3 Sayı: Special Issue: Full Papers of 2nd International Congress of Updates in Biomedical Engineering

Kaynak Göster

APA Seçkin, A. Ç. (2021). Multi-Sensor Glove Design and Bio-Signal Data Collection. Natural and Applied Sciences Journal, 3(Special Issue: Full Papers of 2nd International Congress of Updates in Biomedical Engineering), 87-93.