Characterization of Wireless Data Transmission over Wi-Fi in a Biomechanical Information Processing System

Revista Facultad de Ingeniería

View Publication Info
 
 
Field Value
 
Title Characterization of Wireless Data Transmission over Wi-Fi in a Biomechanical Information Processing System
Caracterización de la transmisión inalámbrica de datos a través de Wi-Fi en un sistema de procesamiento de información biomecánica
 
Creator Callejas-Cuervo, Mauro
Vélez-Guerrero, Manuel Andrés
Alarcón-Aldana, Andrea Catherine
 
Subject biomechanical signals
embedded device
ESP8266
Imocap
telerehabilitation
Wi-Fi
dispositivos embebidos
ESP8266
Imocap
señales biomecánicas
telerehabilitación
Wi-Fi
 
Description This paper presents a characterization of the wireless transmission of biomechanical signals in an embedded system, where a TCP protocol is used in an IEEE 802.11 communications network (Wi-Fi). The embedded system under study, called Imocap, allows the collection, analysis and transmission of biomechanical signals in real-time for various applications, among which the analysis of the movement of the lower and upper extremities and the operation of various control systems stand out. To accomplish this, Imocap is equipped with a Wi-Fi transceiver module (ESP8266) and various input and output peripherals. The wireless communication performance of Imocap, exposed in this paper, was analyzed through different tests in miscellaneous conditions like indoors, outdoors and in the presence of interference, noise and other wireless networks. The different test protocols conducted result in the Imocap system: 1) has a maximum effective range of 45.6 m when in Access Point mode; 2) has a maximum effective range of 44.3 m when in Station mode. In indoors and under the same conditions, the Imocap system: 3) has a maximum effective range of 81.25 m2, either Access Point or Station mode. The results showed that the transmission of biomechanical information through Wi-Fi using the TCP protocol is efficient and robust, both indoors and outdoors, even in environments of radio frequency interference. The use of this protocol is emphasized since its use allows the transmission of packages to be carried out in a controlled manner, allowing the error handling and recovery. In this way, it is possible to carry out efficient and robust wireless communication through embedded and portable devices, focusing mainly on areas such as medicine, telemedicine and telerehabilitation.
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.
 
Publisher Universidad Pedagógica y Tecnológica de Colombia
 
Date 2019-11-06
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
research
investigación
 
Format application/pdf
application/xml
 
Identifier https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10228
10.19053/01211129.v29.n54.2020.10228
 
Source Revista Facultad de Ingeniería; Vol 29 No 54 (2020): Continuos Publication; e10228
Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e10228
2357-5328
0121-1129
 
Language eng
 
Relation /*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/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.
/*ref*/Ai-Thinker, ESP-12F WiFi Module, 2017. Available: https://www.elecrow.com/download/ESP-12F.pdf.
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10228/8440
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10228/9166
 
Coverage N.A.
N.A.
 
Rights http://creativecommons.org/licenses/by/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library