Reconhecimento de Atividade Humana Usando Sinais de Redes Wi-Fi
Resumo
Os sinais de Wi-Fi foram originalmente desenvolvidos com foco em comunicação. No entanto, os sinais Wi-Fi têm sido avaliados como ferramenta para sensoriamento humano. Nesse sentido, neste artigo apresenta uma proposta para reconhecimento de atividade humana (HAR Human Activity Recognition) utilizando dispositivos Wi-Fi. Com essa proposta, é possível inferir a posição de uma pessoa monitorada em um ambiente interno. Para isso, o sinal Wi-Fi que contém a Informação do Estado do Canal (CSI) é processado. Foram selecionados e avaliados cinco algoritmos de classificação diferentes para inferir a posição dos indivíduos e comparar o desempenho. O método proposto foi avaliado em um conjunto de dados de sinais CSI coletados de 125 participantes.Referências
Beddiar, D. R., Nini, B., Sabokrou, M., and Hadid, A. (2020). Vision-based human activity recognition: a survey. Multimedia Tools and Applications, 79(41-42):30509–30555.
Bocus, M., Piechocki, R., and Chetty, K. (2021). A Comparison of UWB CIR and WiFi CSI for Human Activity Recognition. In Proceedings of the IEEE Radar Conference (RadarCon). IEEE Radar Conference (RadarCon).
Caballero, E., Galdino, I., Soto, J. C., Ramos, T., Guerra, R., Muchaluat-Saade, D., and Albuquerque, C. (2023). Human activity recognition using wi-fi csi. In Proceedings of the 17th EAI International Conference on Pervasive Computing Technologies for Healthcare.
Ding, J. and Wang, Y. (2019). WiFi CSI-based human activity recognition using deep recurrent neural network. IEEE Access, 7:174257–174269.
Forbes, G., Massie, S., and Craw, S. (2020). Wifi-based human activity recognition using Raspberry Pi. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), pages 722–730. IEEE.
Galdino, I., Soto, J. C., Caballero, E., Ferreira, V., Ramos, T. C., Muchaluat-Saade, D., and Albuquerque, C. (2023). eHealth CSI: A Wi-Fi CSI dataset of human activities. IEEE Access.
Gringoli, F., Schulz, M., Link, J., and Hollick, M. (2019). Free your csi: A channel state information extraction platform for modern wi-fi chipsets. In Proceedings of the 13th International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization, WiNTECH ’19, page 21–28, New York, NY, USA. Association for Computing Machinery.
Hsieh, C.-F., Chen, Y.-C., Hsieh, C.-Y., and Ku, M.-L. (2020). Device-free indoor human activity recognition using wi-fi rssi: Machine learning approaches. In 2020 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan), pages 1–2. IEEE.
IEEE 802.11 Working Group (2021). Ieee 802.11ax-2021 - ieee standard for information technology–telecommunications and information exchange between systems local and metropolitan area networks–specific requirements part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications amendment 1: Enhancements for high-efficiency wlan. Technical report, IEEE.
Kim, K., Jalal, A., and Mahmood, M. (2019). Vision-based human activity recognition system using depth silhouettes: A smart home system for monitoring the residents. Journal of Electrical Engineering & Technology, 14:2567–2573.
Lee, S., Park, Y. D., Suh, Y. J., and Jeon, S. (2018). Design and implementation of monitoring system for breathing and heart rate pattern using WiFi signals. IEEE Annual Consumer Communications and Networking Conference, pages 1–7.
Li, H., He, X., Chen, X., Fang, Y., and Fang, Q. (2019). Wi-motion: A robust human activity recognition using WiFi signals. IEEE Access, 7:153287–153299.
Loncar-Turukalo, T., Zdravevski, E., da Silva, J. M., Chouvarda, I., Trajkovik, V., et al. (2019). Literature on wearable technology for connected health: scoping review of research trends, advances, and barriers. Journal of medical Internet research, 21(9):e14017.
Ma, Y., Zhou, G., and Wang, S. (2019). Wifi sensing with channel state information: A survey. ACM Computing Surveys (CSUR), 52(3):1–36.
Meneghello, F., Garlisi, D., Dal Fabbro, N., Tinnirello, I., and Rossi, M. (2022). SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points. IEEE Transactions on Mobile Computing.
Muaaz, M., Chelli, A., Gerdes, M. W., and Pätzold, M. (2022). Wi-Sense: A passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems. Annals of Telecommunications, 77(3-4):163–175.
Schäfer, J., Barrsiwal, B. R., Kokhkharova, M., Adil, H., and Liebehenschel, J. (2021). Human activity recognition using CSI information with nexmon. Applied Sciences, 11(19):8860.
Sheng, B., Xiao, F., Sha, L., and Sun, L. (2020). Deep spatial–temporal model based cross-scene action recognition using commodity WiFi. IEEE Internet of Things Journal, 7(4):3592–3601.
Soto, J. C., Galdino, I., Caballero, E., Ferreira, V., Muchaluat-Saade, D., and Albuquerque, C. (2022). A survey on vital signs monitoring based on wi-fi csi data. Computer Communications, 195:99–110.
Uddin, M. Z., Hassan, M. M., Alsanad, A., and Savaglio, C. (2020). A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Information Fusion, 55:105–115.
Wang, W., Liu, A. X., Shahzad, M., Ling, K., and Lu, S. (2017). Device-free human activity recognition using commercial wifi devices. IEEE Journal on Selected Areas in Communications, 35(5):1118–1131.
Wang, Y., Cang, S., and Yu, H. (2019). A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, 137:167–190.
Wang, Y., Wu, K., and Ni, L. M. (2016). Wifall: Device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing, 16(2):581–594.
Weinstein, S. and Ebert, P. (1971). Data transmission by Frequency-Division Multiplexing using the Discrete Fourier Transform. IEEE Transactions on Communication Technology, 19(5):628–634.
Yang, J., Liu, Y., Liu, Z., Wu, Y., Li, T., and Yang, Y. (2021). A framework for human activity recognition based on WiFi CSI signal enhancement. International Journal of Antennas and Propagation, 2021:1–18.
Bocus, M., Piechocki, R., and Chetty, K. (2021). A Comparison of UWB CIR and WiFi CSI for Human Activity Recognition. In Proceedings of the IEEE Radar Conference (RadarCon). IEEE Radar Conference (RadarCon).
Caballero, E., Galdino, I., Soto, J. C., Ramos, T., Guerra, R., Muchaluat-Saade, D., and Albuquerque, C. (2023). Human activity recognition using wi-fi csi. In Proceedings of the 17th EAI International Conference on Pervasive Computing Technologies for Healthcare.
Ding, J. and Wang, Y. (2019). WiFi CSI-based human activity recognition using deep recurrent neural network. IEEE Access, 7:174257–174269.
Forbes, G., Massie, S., and Craw, S. (2020). Wifi-based human activity recognition using Raspberry Pi. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), pages 722–730. IEEE.
Galdino, I., Soto, J. C., Caballero, E., Ferreira, V., Ramos, T. C., Muchaluat-Saade, D., and Albuquerque, C. (2023). eHealth CSI: A Wi-Fi CSI dataset of human activities. IEEE Access.
Gringoli, F., Schulz, M., Link, J., and Hollick, M. (2019). Free your csi: A channel state information extraction platform for modern wi-fi chipsets. In Proceedings of the 13th International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization, WiNTECH ’19, page 21–28, New York, NY, USA. Association for Computing Machinery.
Hsieh, C.-F., Chen, Y.-C., Hsieh, C.-Y., and Ku, M.-L. (2020). Device-free indoor human activity recognition using wi-fi rssi: Machine learning approaches. In 2020 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan), pages 1–2. IEEE.
IEEE 802.11 Working Group (2021). Ieee 802.11ax-2021 - ieee standard for information technology–telecommunications and information exchange between systems local and metropolitan area networks–specific requirements part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications amendment 1: Enhancements for high-efficiency wlan. Technical report, IEEE.
Kim, K., Jalal, A., and Mahmood, M. (2019). Vision-based human activity recognition system using depth silhouettes: A smart home system for monitoring the residents. Journal of Electrical Engineering & Technology, 14:2567–2573.
Lee, S., Park, Y. D., Suh, Y. J., and Jeon, S. (2018). Design and implementation of monitoring system for breathing and heart rate pattern using WiFi signals. IEEE Annual Consumer Communications and Networking Conference, pages 1–7.
Li, H., He, X., Chen, X., Fang, Y., and Fang, Q. (2019). Wi-motion: A robust human activity recognition using WiFi signals. IEEE Access, 7:153287–153299.
Loncar-Turukalo, T., Zdravevski, E., da Silva, J. M., Chouvarda, I., Trajkovik, V., et al. (2019). Literature on wearable technology for connected health: scoping review of research trends, advances, and barriers. Journal of medical Internet research, 21(9):e14017.
Ma, Y., Zhou, G., and Wang, S. (2019). Wifi sensing with channel state information: A survey. ACM Computing Surveys (CSUR), 52(3):1–36.
Meneghello, F., Garlisi, D., Dal Fabbro, N., Tinnirello, I., and Rossi, M. (2022). SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points. IEEE Transactions on Mobile Computing.
Muaaz, M., Chelli, A., Gerdes, M. W., and Pätzold, M. (2022). Wi-Sense: A passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems. Annals of Telecommunications, 77(3-4):163–175.
Schäfer, J., Barrsiwal, B. R., Kokhkharova, M., Adil, H., and Liebehenschel, J. (2021). Human activity recognition using CSI information with nexmon. Applied Sciences, 11(19):8860.
Sheng, B., Xiao, F., Sha, L., and Sun, L. (2020). Deep spatial–temporal model based cross-scene action recognition using commodity WiFi. IEEE Internet of Things Journal, 7(4):3592–3601.
Soto, J. C., Galdino, I., Caballero, E., Ferreira, V., Muchaluat-Saade, D., and Albuquerque, C. (2022). A survey on vital signs monitoring based on wi-fi csi data. Computer Communications, 195:99–110.
Uddin, M. Z., Hassan, M. M., Alsanad, A., and Savaglio, C. (2020). A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Information Fusion, 55:105–115.
Wang, W., Liu, A. X., Shahzad, M., Ling, K., and Lu, S. (2017). Device-free human activity recognition using commercial wifi devices. IEEE Journal on Selected Areas in Communications, 35(5):1118–1131.
Wang, Y., Cang, S., and Yu, H. (2019). A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, 137:167–190.
Wang, Y., Wu, K., and Ni, L. M. (2016). Wifall: Device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing, 16(2):581–594.
Weinstein, S. and Ebert, P. (1971). Data transmission by Frequency-Division Multiplexing using the Discrete Fourier Transform. IEEE Transactions on Communication Technology, 19(5):628–634.
Yang, J., Liu, Y., Liu, Z., Wu, Y., Li, T., and Yang, Y. (2021). A framework for human activity recognition based on WiFi CSI signal enhancement. International Journal of Antennas and Propagation, 2021:1–18.
Publicado
20/05/2024
Como Citar
CABALLERO, Egberto; GALDINO, Iandra; SOTO, Julio C. H.; RAMOS, Taiane C.; GUERRA, Raphael; MUCHALUAT-SAADE, Débora; ALBUQUERQUE, Célio.
Reconhecimento de Atividade Humana Usando Sinais de Redes Wi-Fi. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 42. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 966-979.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2024.1518.
