Abstract
The electrocardiogram, electroencephalogram, blood pressure, temperature, etc. are only some of the physiological signals that may be monitored using a variety of innovative technologies using sensor nodes in the IoT. The consequences of many different technical advances and IoT for medical healthcare applications. The purpose of this research is to create a wearable sensor data collection and analysis system based on optical communication using techniques from quantum photonics’ integrated machine learning architecture. Here, the optical connection module is set up so that healthcare monitoring data may be obtained via wearable sensors. This information was analyzed using quantum photonics and a spline-based feedforward neural computing architecture trained with extreme cognitive learning. Energy efficiency, battery life, dependability, mean absolute error (MAE), and optical signal-to-noise ratio are all examined in the experimental study of data from wearable sensors and optical communication. 95% energy efficiency, 81% battery life, 61% dependability, 51% mean absolute error (MAE), and 59% overall signal to noise ratio were all achieved by the proposed method.
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References
Ajay, P., & Huang, R. (2022). Wearable Sensor Data for Classification and Analysis of Functional Fitness Exercises Using Unsupervised Deep Learning Methodologies. Security and Communication Networks,
Antwi-Afari, M.F., Qarout, Y., Herzallah, R., Anwer, S., Umer, W., Zhang, Y., Manu, P.: Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data. Autom. Constr. 136, 104181 (2022)
Apicella, A., Arpaia, P., De Benedetto, E., Donato, N., Duraccio, L., Giugliano, S., Prevete, R.: Enhancement of SSVEPs classification in BCI-based wearable instrumentation through machine learning techniques. IEEE Sens. J. 22(9), 9087–9094 (2022)
Bijalwan, V., Semwal, V.B., Gupta, V.: Wearable sensor-based pattern mining for human activity recognition: Deep learning approach. Indust. Robot: Int. J. Robot. Res. Appl 49(1), 21–33 (2022)
Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomed. Signal Process. Control 71, 103197 (2022)
Chen, S., Tan, F., Lyu, W., Luo, H., Yu, J., Qu, J., Yu, C.: Deep learning-based ballistocardiography reconstruction algorithm on the optical fiber sensor. Opt. Express 30(8), 13121–13133 (2022)
Cuțitoi, A.C.: Remote patient monitoring systems, wearable internet of medical things sensor devices, and deep learning-based computer vision algorithms in COVID-19 screening detection diagnosis and treatment. American J. Med. Res. 9(1), 129–144 (2022)
Dai N, Lei IM, Li Z, Li Y, Fang P, & Zhong J, (2022), Recent advances in wearable electromechanical sensors—Moving towards machine learning-assisted wearable sensing systems, Nano Energy
Dong, B., Zhang, Z., Shi, Q., Wei, J., Ma, Y., **ao, Z., Lee, C.: Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface. Sci. Adv. 8(3), eab19874 (2022)
Feng, Y., Ju, L., Jia, H., Liu, H., Ding, X., & Zhang, W. (2023). Intentionally Light-Loss Carbon-Optic Fiber (COF) Twisted Sensor for Calf Strength Sensing via Monitoring Vastus Medialis. IEEE Sensors Journal.
Dua, N., Singh, S. N., Challa, S. K., Semwal, V. B., & Sai Kumar, M. L. S.(2023) A Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data. In Machine Learning, Image Processing, Network Security and Data Sciences: 4th International Conference, MIND 2022 Virtual Event Proceedings Part I Springer Nature Switzerland Cham 52 71
Filosa, M., Massari, L., Ferraro, D., D’Alesio, G., D’Abbraccio, J., Aliperta, A., Oddo, C.M.: A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions. Artifi. Int. Med. 130, 102328 (2022)
Guo, Y., Zhu, J., **ong, L., Guan, J.: Finger motion detection based on optical fiber Bragg grating with polyimide substrate. Sens. Actuators, A 338, 113482 (2022)
Incel, O. D., & Bursa, S. O. (2023). On-device deep learning for mobile and wearable sensing applications: A review. IEEE Sensors Journal.
Jiang, Y., An, J., Liang, F., Zuo, G., Yi, J., Ning, C., Wang, Z.L.: Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction. Nano Res. 15(9), 8389–8397 (2022)
Li, T., Qiao, F., Huang, P.A., Su, Y., Wang, L., Li, X., Zhou, Z.: Flexible optical fiber-based smart textile sensor for human-machine interaction. IEEE Sens. J. 22(20), 19336–19345 (2022)
Mekruksavanich, S., & Jitpattanakul, A. (2022). Cnn-based deep learning network for human activity recognition during physical exercise from accelerometer and photoplethysmographic sensors. In Computer Networks, Big Data and IoT: Proceedings of ICCBI 2021 (pp. 531–542). Singapore: Springer Nature Singapore.
Pal, D., Kumar, A., Gautam, A., Thangaraj, J.: FBG based optical weight measurement system and its performance enhancement using machine learning. IEEE Sens. J. 22(5), 4113–4121 (2022)
Qi, W., Su, H.: A cybertwin based multimodal network for ecg patterns monitoring using deep learning. IEEE Trans. Industr. Inf. 18(10), 6663–6670 (2022)
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Fortino, G.: Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges. Inform. Fusion 80, 241–265 (2022)
Shi, C., Tang, Z., Zhang, H., Liu, Y.: Development of an FBG-based wearable sensor for simultaneous respiration and heartbeat measurement. IEEE Trans. Instrum. Meas. 72, 1–9 (2022)
Vidya, B., Sasikumar, P.: Wearable multi-sensor data fusion approach for human activity recognition using machine learning algorithms. Sens. Actuators, A 341, 113557 (2022)
Wang, Q., Lyu, W., Cheng, Z., & Yu, C. (2023). Noninvasive Measurement of Vital Signs with the Optical Fiber Sensor Based on Deep Learning. Journal of Lightwave Technology.
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PK: Conceived and design the analysis: Writing- Original draft preparation. NS: Collecting the Data, TGK: Contributed data and analysis stools, PK: Performed and analysis, MS: Performed and analysis, RRS: Wrote the Paper: Editing and Figure Design.
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Kumar, P., Sharma, N., Kumar, T.G. et al. Explainable AI based wearable electronic optical data analysis with quantum photonics and quadrature amplitude neural computing. Opt Quant Electron 55, 760 (2023). https://doi.org/10.1007/s11082-023-05037-8
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DOI: https://doi.org/10.1007/s11082-023-05037-8