Towards a mobile system with a new wearable device and an AI application for walking and running activities
Resumo
Recognizing human activities from mobile applications is a challenging task due to the complexity of the context. Several healthcare applications consider wearable devices, including in the orthopedic area. Considering this context, we proposed a novel mobile application that can recognize walking activities with data collected by new wearable sensors on the user’s leg. The wearable system collects the data, which is processed using Edge AI. Then, we propose to present the generated information as a digital twin considering the user’s movements based on the sensor data. For this work, the wearable device and AI movement classification are operational, while the mobile application is still in development.
Referências
(2022). Dataset for human activity recognition har. [link].
Ann, O. C. and Theng, L. B. (2014). Human activity recognition: a review. In 2014 IEEE international conference on control system, computing and engineering (ICCSCE 2014), pages 389–393. IEEE.
Borowski-Beszta, M. and Polasik, M. (2020). Wearable devices: new quality in sports and finance. Journal of Physical Education and Sport, 20:1077–1084.
Cosoli, G., Antognoli, L., Veroli, V., and Scalise, L. (2022). Accuracy and precision of wearable devices for real-time monitoring of swimming athletes. Sensors, 22(13):4726.
Demrozi, F., Pravadelli, G., Bihorac, A., and Rashidi, P. (2020). Human activity recognition using inertial, physiological and environmental sensors: A comprehensive survey. IEEE Access, 8:210816–210836.
Győrbíró, N., Fábián, Á., and Hományi, G. (2009). An activity recognition system for mobile phones. Mobile Networks and Applications, 14(1):82–91.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Hossin, M. and Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process, 5(2):1.
Lara, D. and Labrador, M. A. (2012). A mobile platform for real-time human activity recognition. In 2012 IEEE Consumer Communications and Networking Conference (CCNC), pages 667–671.
Lee, S., Park, M., Lee, K., and Lee, J. (2019). Scalable muscle-actuated human simulation and control. ACM Transactions On Graphics (TOG), 38(4):1–13.
Lu, T.-W. and Chang, C.-F. (2012). Biomechanics of human movement and its clinical applications. The Kaohsiung journal of medical sciences, 28(2):S13–S25.
Mekruksavanich, S. and Jitpattanakul, A. (2021). Biometric user identification based on human activity recognition using wearable sensors: An experiment using deep learning models. Electronics, 10(3).
Niu, L., Han, Q., Wang, T., and Quan, G. (2018). Reliability-aware energy management for embedded real-time systems with (m, k)-hard timing constraint. Journal of Signal Processing Systems, 90:515–536.
Ravi, N., Dandekar, N., Mysore, P., and Littman, M. L. (2005). Activity recognition from accelerometer data. In Aaai, volume 5, pages 1541–1546. Pittsburgh, PA.
Schiewe, A., Krekhov, A., Kerber, F., Daiber, F., and Krüger, J. (2020). A study on real-time visualizations during sports activities on smartwatches. In 19th International Conference on Mobile and Ubiquitous Multimedia, pages 18–31.
Taghavi, S., Davari, F., Malazi, H. T., and Abin, A. A. (2019). Tennis stroke detection using inertial data of a smartwatch. In 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), pages 466–474. IEEE.
Wang, P., Liu, H., Wang, L., and Gao, R. X. (2018). Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. CIRP Annals, 67(1):17–20.
Xu, Y. and Qiu, T. T. (2021). Human activity recognition and embedded application based on convolutional neural network. Journal of Artificial Intelligence and Technology, 1(1):51–60.
Zaki, T. H. M., Sahrim, M., Jamaludin, J., Balakrishnan, S. R., Asbulah, L. H., and Hussin, F. S. (2020a). The study of drunken abnormal human gait recognition using accelerometer and gyroscope sensors in mobile application. In 2020 16th IEEE International Colloquium on Signal Processing Its Applications (CSPA), pages 151–156.
Zaki, T. H. M., Sahrim, M., Jamaludin, J., Balakrishnan, S. R., Asbulah, L. H., and Hussin, F. S. (2020b). The study of drunken abnormal human gait recognition using accelerometer and gyroscope sensors in mobile application. In 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), pages 151–156. IEEE.
Zhuang, Z. and Xue, Y. (2019). Sport-related human activity detection and recognition using a smartwatch. Sensors, 19(22):5001.
Ann, O. C. and Theng, L. B. (2014). Human activity recognition: a review. In 2014 IEEE international conference on control system, computing and engineering (ICCSCE 2014), pages 389–393. IEEE.
Borowski-Beszta, M. and Polasik, M. (2020). Wearable devices: new quality in sports and finance. Journal of Physical Education and Sport, 20:1077–1084.
Cosoli, G., Antognoli, L., Veroli, V., and Scalise, L. (2022). Accuracy and precision of wearable devices for real-time monitoring of swimming athletes. Sensors, 22(13):4726.
Demrozi, F., Pravadelli, G., Bihorac, A., and Rashidi, P. (2020). Human activity recognition using inertial, physiological and environmental sensors: A comprehensive survey. IEEE Access, 8:210816–210836.
Győrbíró, N., Fábián, Á., and Hományi, G. (2009). An activity recognition system for mobile phones. Mobile Networks and Applications, 14(1):82–91.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Hossin, M. and Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process, 5(2):1.
Lara, D. and Labrador, M. A. (2012). A mobile platform for real-time human activity recognition. In 2012 IEEE Consumer Communications and Networking Conference (CCNC), pages 667–671.
Lee, S., Park, M., Lee, K., and Lee, J. (2019). Scalable muscle-actuated human simulation and control. ACM Transactions On Graphics (TOG), 38(4):1–13.
Lu, T.-W. and Chang, C.-F. (2012). Biomechanics of human movement and its clinical applications. The Kaohsiung journal of medical sciences, 28(2):S13–S25.
Mekruksavanich, S. and Jitpattanakul, A. (2021). Biometric user identification based on human activity recognition using wearable sensors: An experiment using deep learning models. Electronics, 10(3).
Niu, L., Han, Q., Wang, T., and Quan, G. (2018). Reliability-aware energy management for embedded real-time systems with (m, k)-hard timing constraint. Journal of Signal Processing Systems, 90:515–536.
Ravi, N., Dandekar, N., Mysore, P., and Littman, M. L. (2005). Activity recognition from accelerometer data. In Aaai, volume 5, pages 1541–1546. Pittsburgh, PA.
Schiewe, A., Krekhov, A., Kerber, F., Daiber, F., and Krüger, J. (2020). A study on real-time visualizations during sports activities on smartwatches. In 19th International Conference on Mobile and Ubiquitous Multimedia, pages 18–31.
Taghavi, S., Davari, F., Malazi, H. T., and Abin, A. A. (2019). Tennis stroke detection using inertial data of a smartwatch. In 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), pages 466–474. IEEE.
Wang, P., Liu, H., Wang, L., and Gao, R. X. (2018). Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. CIRP Annals, 67(1):17–20.
Xu, Y. and Qiu, T. T. (2021). Human activity recognition and embedded application based on convolutional neural network. Journal of Artificial Intelligence and Technology, 1(1):51–60.
Zaki, T. H. M., Sahrim, M., Jamaludin, J., Balakrishnan, S. R., Asbulah, L. H., and Hussin, F. S. (2020a). The study of drunken abnormal human gait recognition using accelerometer and gyroscope sensors in mobile application. In 2020 16th IEEE International Colloquium on Signal Processing Its Applications (CSPA), pages 151–156.
Zaki, T. H. M., Sahrim, M., Jamaludin, J., Balakrishnan, S. R., Asbulah, L. H., and Hussin, F. S. (2020b). The study of drunken abnormal human gait recognition using accelerometer and gyroscope sensors in mobile application. In 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), pages 151–156. IEEE.
Zhuang, Z. and Xue, Y. (2019). Sport-related human activity detection and recognition using a smartwatch. Sensors, 19(22):5001.
Publicado
06/08/2023
Como Citar
ALVIM, Patrick B. N.; SILVA, Jonathan C. F. da; AMORIM, Vicente J. P.; LAZARONI, Pedro S. O.; SILVA, Mateus Coelho; OLIVEIRA, Ricardo A. R..
Towards a mobile system with a new wearable device and an AI application for walking and running activities. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 50. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 155-166.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2023.230082.
