Caracterización de la transmisión inalámbrica de datos a través de Wi-Fi en un sistema de procesamiento de información biomecánica
Resumen
Este artículo presenta una caracterización de la transmisión inalámbrica de señales biomecánicas en un sistema embebido, donde se utiliza un protocolo TCP en una red de comunicaciones IEEE 802.11 (Wi-Fi). El sistema embebido en estudio, denominado Imocap, permite la recogida, análisis y transmisión de señales biomecánicas en tiempo real para diversas aplicaciones, entre las que destacan el análisis del movimiento de las extremidades inferiores y superiores y la activación de diversos sistemas de control. Para este fin, Imocap está equipado con un módulo transceptor Wi-Fi (ESP8266) y varios periféricos de entrada y salida. El desempeño de la comunicación inalámbrica de Imocap, expuesto en este trabajo, fue analizado a través de diferentes pruebas en condiciones diversas como en interiores, exteriores y en presencia de interferencia, ruido y otras redes inalámbricas. Los diferentes protocolos de prueba realizados dan como resultado que el sistema Imocap: 1) tiene un alcance efectivo máximo de 45,6 m cuando está en modo Access Point; 2) tiene un alcance efectivo máximo de 44,3 m cuando está en modo Station. En interior y en las mismas condiciones, el sistema Imocap: 3) tiene un alcance efectivo máximo de 81,25 m2, ya sea en modo Punto de Acceso o en modo Estación. Los resultados mostraron que la transmisión de información biomecánica a través de Wi-Fi utilizando el protocolo TCP es eficiente y robusta, tanto en interiores como en exteriores, incluso en entornos de interferencia de radiofrecuencia. Se destaca el uso de este protocolo ya que su uso permite que la transmisión de paquetes se realice de forma controlada, permitiendo el manejo y recuperación de errores. De esta manera, es posible llevar a cabo una comunicación inalámbrica eficiente y robusta a través de dispositivos embebidos y portátiles, centrándose principalmente en áreas como la medicina, la telemedicina y la telerehabilitación.
Palabras clave
dispositivos embebidos, ESP8266, Imocap, señales biomecánicas, telerehabilitación, Wi-Fi
Citas
[1] X. Gao, L. Lin, T. Lan, and X. Gan, "Design and Research on the Chinese Medicine Health Management System Based on the Wireless Sensor Network," in The International Conference on Cyber Security Intelligence and Analytics, pp. 55-61, Shenyang, China, 2020. https://doi.org/10.1007/978-3-030-15235-2_9.
[2] X. Zhang, C. Xu, Y. Zhang, and C. Jin, “Efficient Integrity Verification Scheme for Medical Data Records in Cloud-Assisted Wireless Medical Sensor Networks,” Wireless Personal Communications, vol. 96 (2), pp. 1819-1833, 2017. https://doi.org/10.1007/s11277-017-4270-8.
[3] S. Das, S. Barani, S. Wagh, and S.-S. Sonavane, “Extending lifetime of wireless sensor networks using multi-sensor data fusion,” Sadhana, vol. 42 (7), pp. 1083-1090, 2017.
[4] T.-Y. Shi, J. Li, X.-C. Jia, W. Bai, Z.-Y. Wang, and D. Zhou, “Low-latency data gathering with reliability guaranteeing in heterogeneous wireless sensor networks,” International Journal of Automation and Computing, pp. 1-14, 2017. https://doi.org/10.1007/s11633-017-1074-y.
[5] M. Wang, X. Wang, L. Yang, X. Deng, and L. Yi, "Multi-sensor fusion based intelligent sensor relocation for health and safety monitoring in BSNs," Information Fusion, vol. 54, pp. 61-71, 2020. https://doi.org/10.1016/j.inffus.2019.07.002.
[6] E. Bassey, and P. Sallis, "Integration of Microfluidic Sensors for Interactive Remote Wireless Data Transmission," IFMBE Proceedings, vol. 69, pp. 347-352, 2020. https://doi.org/10.1007/978-981-13-5859-3_61.
[7] K. Rao, and K. Supriya, "Design and Development of IoT Based Intravenous Infusion System," Lecture Notes in Electrical Engineering, no. 569, pp. 487-499, 2020. https://doi.org/10.1007/978-981-13-8942-9_40.
[8] B. Kurian, and R. Liyanapathirana, "Machine Learning Techniques for Structural Health Monitoring," in Proceedings of the 13th International Conference on Damage Assessment of Structures, pp. 3-24, Porto, Portugal, 2020. https://doi.org/10.1007/978-981-13-8331-1_1.
[9] B. Adamová, P. Kutilek, O. Cakrt, Z. Svoboda, S. Viteckova, and P. Smrcka, “Quantifying postural stability of patients with cerebellar disorder during quiet stance using three-axis accelerometer,” Biomedical Signal Processing and Control, vol. 40, pp. 378-384, 2018. https://doi.org/10.1016/j.bspc.2017.09.025.
[10] N. Xie, and P.-Y. Mok, “Investigation on human body movements and the resulting body measurement variations,” Advances in Physical Ergonomics and Human Factors, vol. 602, pp. 387-399, 2018. https://doi.org/10.1007/978-3-319-60825-9_42.
[11] A.-K. Seifert, M.-G. Amin, and A.-M. Zoubir, “New analysis of radar micro-Doppler gait signatures for rehabilitation and assisted living”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4004-4008, New Orleans, USA, 2017. https://doi.org/10.1109/ICASSP.2017.7952908.
[12] S. Transue, P. Nguyen, T. Vu, and M.-H. Choi, “Thermal-Depth Fusion for Occluded Body Skeletal Posture Estimation,” in IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 167-176, Philadelphia, USA, 2018. https://doi.org/10.1109/CHASE.2017.75.
[13] S. Zihajehzadeh, and E.-J. Park, “A Novel Biomechanical Model-Aided IMU/UWB Fusion for Magnetometer-Free Lower Body Motion Capture,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47 (6), pp. 927-938, 2017. https://doi.org/10.1109/TSMC.2016.2521823.
[14] L. Malasinghe, N. Ramzan, and K. Dahal, "Remote patient monitoring: a comprehensive study," Journal of Ambient Intelligence and Humanized Computing, vol. 10 (1), pp. 57-76, 2019. https://doi.org/10.1007/s12652-017-0598-x.
[15] C. Abreu, F. Miranda, and P.-M. Mendes, “Smart context-aware QoS-based admission control for biomedical wireless sensor networks,” Journal of Network and Computer Applications, vol. 88, pp. 134–145, 2017. https://doi.org/10.1016/j.jnca.2017.01.034.
[16] I. Bocicor, M. Dascalu, A. Gaczowska, S. Hostiuc, A. Moldoveanu, A. Molina, A.-J. Molnar, I. Negoi, and V. Racovita “Wireless sensor network-based system for the prevention of hospital acquired infections,” in Proceedings of the 12th International Conference on Evaluation of Novel Software Approaches to Software Engineering, pp. 158-167, Porto, Portugal, 2018. https://doi.org/10.5220/0006357801580167.
[17] B. Iancu, R. Kovacs, V. Dadarlat, and A. Peculea, “Interconnecting heterogeneous non-smart medical devices using a wireless sensor networks (WSN) infrastructure,” in International Conference on Advancements of Medicine and Health Care through Technology, pp. 207-212, Cluj-Napoca, Romania, 2017. https://doi.org/10.1007/978-3-319-52875-5_45.
[18] N. Saleh, A. Kassem, and A.-M. Haidar, “Energy-efficient architecture for wireless sensor networks in healthcare applications,” IEEE Access, vol. 6, pp. 6478-6496, 2018. https://doi.org/10.1109/ACCESS.2018.2789918.
[19] G. Abdul-Salaam, A.-H. Abdullah, and M.-H. Anisi, “Energy-Efficient Data Reporting for Navigation in Position-Free Hybrid Wireless Sensor Networks,” IEEE Sensors Journal, vol. 17 (7), pp. 2289-2297, 2017. https://doi.org/10.1109/JSEN.2017.2665663.
[20] R. Talmale, M. Bhat, and N. Thakare, "Efficient Energy Attentive and Fault Recognition Mechanism in Distributed Wireless Sensor Networks: A Review," Advances in Intelligent Systems and Computing, vol. 940, pp. 1081-1092, 2020. https://doi.org/10.1007/978-3-030-16657-1_101.
[21] S. Sharma, J. Singh, R. Kumar, and A. Singh, "Throughput-save ratio optimization in wireless powered communication systems," in International Conference on Information, Communication, Instrumentation and Control (ICICIC), pp. 1-6, Indore, India, 2018. https://doi.org/10.1109/icomicon.2017.8279031.
[22] E. Casilari-Pérez, and F. García-Lagos, "A comprehensive study on the use of artificial neural networks in wearable fall detection systems," Expert Systems with Applications, vol. 138, p. 112811, 2019. https://doi.org/10.1016/j.eswa.2019.07.028.
[23] H. Son, M. Nguyen, H. Vo, and T. Nguyen, "Toward a Privacy Protection Based on Access Control Model in Hybrid Cloud for Healthcare Systems," Advances in Intelligent Systems and Computing, vol. 951, pp. 77-86, 2020. https://doi.org/10.1007/978-3-030-20005-3_8.
[24] M. Muzammal, R. Talat, A. Sodhro, and S. Pirbhulal, "A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks," Information Fusion, vol. 53, pp. 155-164, 2020. https://doi.org/10.1016/j.inffus.2019.06.021.
[25] J. Qi, P. Yang, L. Newcombe, X. Peng, Y. Yang, and Z. Zhao, "An overview of data fusion techniques for Internet of Things enabled physical activity recognition and measure," Information Fusion, vol. 55, pp. 269-280, 2020. https://doi.org/10.1016/j.inffus.2019.09.002.
[26] E. Valchinov, K. Rotas, A. Antoniou, V. Syrimpeis, and N. Pallikarakis, "Wearable System for Early Diagnosis and Follow Up of Spine Curvature Disorders," IFMBE Proceedings, vol. 73, pp. 205-209, 2020. https://doi.org/10.1007/978-3-030-17971-7_32.
[27] S. Baskar, P. Mohamed Shakeel, R. Kumar, M. Burhanuddin, and R. Sampath, "A dynamic and interoperable communication framework for controlling the operations of wearable sensors in smart healthcare applications," Computer Communications, vol. 149, pp. 17-26, 2020. https://doi.org/10.1016/j.comcom.2019.10.004.
[28] A. Shamsul Arefin, K. Nahiyan, and M. Rabbani, "The Basics of Healthcare IoT: Data Acquisition, Medical Devices, Instrumentations and Measurements," A Handbook of Internet of Things in Biomedical and Cyber Physical System, vol. 165, pp. 1-37, 2020. https://doi.org/10.1007/978-3-030-23983-1_1.
[29] A. Ahmad, M. F. Roslan, and A. Amira, “Throughput, latency and cost comparisons of microcontroller-based implementations of wireless sensor network (WSN) in high jump sports,” in International Conference on Electrical and Electronic Engineering: Advancing Engineering Towards Sustainable Future (IC3E), pp. 020010, Toyama, Japan, 2017. https://doi.org/10.1063/1.5002028.
[30] W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, “Device-Free Human Activity Recognition Using Commercial WiFi Devices,” IEEE Journal on Selected Areas in Communications, vol. 35 (5), pp. 1118-1131, 2017. https://doi.org/10.1109/JSAC.2017.2679658.
[31] A. Yilmaz, and A. Gupta, “Indoor positioning using visual and inertial sensors,” in IEEE Sensors Conference (SENSORS), Orlando, USA, 2016. https://doi.org/10.1109/ICSENS.2016.7808526.
[32] Y. Gu, J. Zhan, Y. Ji, J. Li, F. Ren, and S. Gao, “MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices,” IEEE Internet Things Journal, vol. 4 (6), pp. 2326-2341, 2017. https://doi.org/10.1109/JIOT.2017.2754578.
[33] M. Kotaru, A. Anemogiannis, S. Joseph, and S. Katti, “Demo: Position tracking for virtual reality using commodity WiFi,” in Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 488-489, Utah, USA, 2017. https://doi.org/10.1145/3117811.3119865.
[34] C. Prasad, and P. Bojja, "A reliable, energy aware and stable topology for bio-sensors in health-care applications," Journal of Communications, vol. 14 (5), pp. 390-395, 2019. https://doi.org/10.12720/jcm.14.5.390-395.
[35] M. Callejas-Cuervo, M. Vélez-Guerrero, and A. Alarcón-Aldana, Estimadores No Lineales: Aplicación del Filtro de Kalman a Señales Biomecánicas. Tunja: Editorial UPTC, 2019.
[36] S. Majumder, M. Rahman, M. Islam, and D. Ghosh, "Design and Implementation of a Wireless Health Monitoring System for Remotely Located Patients," in International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), pp. 86-91, Dhaka, Bangladesh, 2019. https://doi.org/10.1109/ceeict.2018.8628077.
[37] D. Bora, N. Kumar, and R. Dutta, "Implementation of wireless MEMS sensor network for detection of gait events," IET Wireless Sensor Systems, vol. 9 (1), pp. 48-52, 2019. https://doi.org/10.1049/iet-wss.2018.5049.
[38] L. Fernandez, L. Perez, J. Hernandez, and G.-J. Rodriguez, "Biomechanical Signal Analysis for Evaluation of Gait in Parkinson's Disease," in IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 2018, pp. 792-799, Turin, Italy, 2018. https://doi.org/10.1109/etfa.2018.8502581.
[39] F.-A. Mota, V.-H. Biajo, H.-O. Mota, and F.-H. Vasconcelos, “A wireless sensor network for the biomechanical analysis of the gait,” in IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Turin, Italy, 2017. https://doi.org/10.1109/I2MTC.2017.7969859.
[40] T. Ahmed, and H. Ahmad, "Device free human gesture recognition using Wi-Fi CSI: A survey," Engineering Applications of Artificial Intelligence, vol. 87, p. 103281, 2020. https://doi.org/10.1016/j.engappai.2019.103281.
[41] Y. Li, Y. Zhuang, H. Lan, Q. Zhou, X. Niu, and N. El-Sheimy, “A Hybrid WiFi/magnetic matching/PDR approach for indoor navigation with smartphone sensors,” IEEE Communication Letters, vol. 20 (1), pp. 169-172, 2016. https://doi.org/10.1109/LCOMM.2015.2496940.
[42] M. Merkepci, M. Ozyazici, and N. Dogru, "Photoplethysmography based instant remote monitoring of non-invasive blood pressure and oxygen saturation by using zigbee network," Biomedical Research, vol. 29 (11), pp. 2401-2404, 2018. https://doi.org/10.4066/biomedicalresearch.60-17-1900.
[43] S.-F. Kedar, Abdullah, F. Sudhindra, S.-J. Annarao, R.-M. Vani, and B.-S. Motgi, “Development of Zigbee based tele operated multipurpose robotic arm with hand gesture recognition,” Int. J. Mech. Eng. Technol., vol. 8 (8), pp. 1275-1286, 2017.
[44] D.-E. Bolanakis, “Work in progress: Educating engineers in MEMS sensors; A case study in wireless barometers,” in IEEE Global Engineering Education Conference (EDUCON), pp. 1421-1425, Athens, Greece, 2017. https://doi.org/10.1109/EDUCON.2017.7943034.
[45] A. Sahdom, "Application of Micro Electro-Mechanical Sensors (MEMS) Devices with Wifi Connectivity and Cloud Data Solution for Industrial Noise and Vibration Measurements," Journal of Physics: Conference Series, vol. 1262 (1), p. 012025, 2019. https://doi.org/10.1088/1742-6596/1262/1/012025.
[46] C. Real-Ehrlich, and J. Blankenbach, "Indoor localization for pedestrians with real-time capability using multi-sensor smartphones," Geo-spatial Information Science, vol. 22 (2), pp. 73-88, 2019. https://doi.org/10.1080/10095020.2019.1613778.
[47] A. Murty, M. Satyanarayana, and I. Devi, "Compressor Health Monitoring using IOT," International Journal of Mechanical and Production Engineering Research and Development, vol. 8 (3), pp. 117-124, 2019. https://doi.org/10.24247/ijmperdjun201813.
[48] Ai-Thinker, ESP-12F WiFi Module, 2017. Available: https://www.elecrow.com/download/ESP-12F.pdf.