Have a personal or library account? Click to login
Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network Cover

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Open Access
|May 2022

References

  1. [1] Zhang, F. (2020). Human-computer interactive gesture feature capture and recognition in virtual reality. Ergonomics in Design: The Quarterly of Human Factors Applications, 29 (2), 9-25. https://doi.org/10.1177%2F1064804620924133
  2. [2] Wang, Y., Chen, M., Wang, X., Chan, R., Li, W. (2018). IoT for next generation racket sports training. IEEE Internet of Things Journal, 5 (6), 4558-4566. https://doi.org/10.1109/JIOT.2018.283734710.1109/JIOT.2018.2837347
  3. [3] Wang, L., Sun, Y., Li, Q., Liu, T., Yi, J. (2020). Two shank-mounted IMUs-based gait analysis and classification for neurological disease patients. IEEE Robotics and Automation Letters, 5 (2), 1970-1976. https://doi.org/10.1109/LRA.2020.297065610.1109/LRA.2020.2970656
  4. [4] Debes, C., Merentitis, A., Sukhanov, S., Niessen, M., Fangiadakis, N., Bauer, A. (2016). Monitoring activities of daily living in smart homes: Understanding human behavior. IEEE Signal Processing Magazine, 33 (2), 81-94. https://doi.org/10.1109/MSP.2015.250388110.1109/MSP.2015.2503881
  5. [5] Wang, J., Chen, Y., Hao, S., Peng X.H., Hu, L.S. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 119, 3-11. https://doi.org/10.1016/j.patrec.2018.02.01010.1016/j.patrec.2018.02.010
  6. [6] Yang, D., Huang, J., Tu, X., Ding, G.Z., Shen, T., Xiao, X.L. (2019). A wearable activity recognition device using Air-pressure and IMU sensors. IEEE Access, 7, 6611-6621. https://doi.org/10.1109/ACCESS.2018.289000410.1109/ACCESS.2018.2890004
  7. [7] Oniga, S., József, S. (2015). Optimal recognition method of human activities using artificial neural networks. Measurement Science Review, 15 (6), 323-327. https://doi.org/10.1515/msr-2015-004410.1515/msr-2015-0044
  8. [8] Yan, H., Zhang, Y., Wang, Y.J., Xu, K.L. (2020). WiAct: A passive WIFI-based human activity recognition system. IEEE Sensors Journal, 20 (1), 296-305. https://doi.org/10.1109/JSEN.2019.293824510.1109/JSEN.2019.2938245
  9. [9] Han, J.S., Ding, H., Qian, C., Xi, W., Wang, Z., Jiang, Z.P., Shangguan, L.F., Zhao, J.Z. (2016). CBID: A customer behavior identification system using passive tags. IEEE/ACM Transactions on Networking, 24 (5), 2885-2898. https://doi.org/10.1109/TNET.2015.250110310.1109/TNET.2015.2501103
  10. [10] Rahaman, H., Dyo, V. (2021). Tracking human motion direction with commodity wireless networks. IEEE Sensors Journal, 21 (20), 23344-23351. https://doi.org/10.1109/JSEN.2021.311113210.1109/JSEN.2021.3111132
  11. [11] Mekruksavanich, S., Hnoohom, N., Jitpattanakul, A. (2018). Smartwatch-based sitting detection with human activity recognition for office workers syndrome. In 2018 International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering. IEEE, 160-164. https://doi.org/10.1109/ECTI-NCON.2018.837830210.1109/ECTI-NCON.2018.8378302
  12. [12] Mekruksavanich, S, Jitpattanakul, A. (2020). Smartwatch-based human activity recognition using hybrid LSTM network. In 2020 IEEE Sensors Conference. IEEE, 1-4. https://doi.org/10.1109/SENSORS47125.2020.927863010.1109/SENSORS47125.2020.9278630
  13. [13] Li, Y., Zhao, K., Duan, M.C., Shi, W., Lin, L.L., Cao, X.Y., Liu, Y., Zhao, J.Z. (2020). Control your home with a smartwatch. IEEE Access, 8, 131601-131613. https://doi.org/10.1109/ACCESS.2020.300732810.1109/ACCESS.2020.3007328
  14. [14] Guo, J.Q., Zhou, X., Sun, Y.C., Ping, G., Zhao, G.X., Li, Z.R. (2016). Smartphone-based patients’ activity recognition by using a self-learning scheme for medical monitoring. Journal of Medical System, 40 (6), 140. https://doi.org/10.1007/s10916-016-0497-210.1007/s10916-016-0497-227106584
  15. [15] Ramanujam, E., Perumal, T., Padmavathi, S. (2021). Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review. IEEE Sensors Journal, 21 (12), 13029-13040. https://doi.org/10.1109/JSEN.2021.306992710.1109/JSEN.2021.3069927
  16. [16] Masoud, M.Z., Jaradat, Y., Manaarah, A., Jannoud, I. (2019). Sensors of smart devices in the internet of everything (IoE) era: Big opportunities and massive doubts. Journal of Sensors, 2019, 6514520. https://doi.org/10.1155/2019/651452010.1155/2019/6514520
  17. [17] Irene, S., Shwetha, N.M., Haribabu, P., Pitchiah, R. (2015). Development of ZigBee triaxial accelerometer based human activity recognition system. In IEEE International Conference on Computer and Information Technology. IEEE, 1460-1466. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.35710.1109/CIT/IUCC/DASC/PICOM.2015.357
  18. [18] Yen, T., Liao, J.X., Huang, Y.K. (2020). Human daily activity recognition performed using wearable inertial sensors combined with deep learning algorithms. IEEE Access, 8, 174105-174114. https://doi.org/10.1109/ACCESS.2020.302593810.1109/ACCESS.2020.3025938
  19. [19] Santoyo-Ramón, J.A., Casilari, E., Cano-García, J.M. (2018). Analysis of a smartphone-based architecture with multiple mobility sensors for fall detection with supervised learning. Sensors, 18 (4), 1155. https://doi.org/10.3390/s1804115510.3390/s18041155594857229642638
  20. [20] Li, H., He, X., Chen, X., Fang, Y.Y., Fang, Q. (2019). Wi-motion: A robust human activity recognition using WIFI signals. IEEE Access, 7, 153287-153299. https://doi.org/10.1109/ACCESS.2019.294810210.1109/ACCESS.2019.2948102
  21. [21] Mellal, L., Laghrouche, M., Bui, H.T. (2017). Field programmable gate array (FPGA) respiratory monitoring system using a flow microsensor and an accelerometer. Measurement Science Review, 17 (2), 61-67. https://doi.org/10.1515/msr-2017-000810.1515/msr-2017-0008
  22. [22] Hsu, Y.L., Yang, S.C., Chang, C.H., Lai, H.C. (2018). Human daily and sport activity recognition using a wearable inertial sensor network. IEEE Access, 6, 31715-31728. https://doi.org/10.1109/ACCESS.2018.283976610.1109/ACCESS.2018.2839766
  23. [23] Tian, Y.M., Zhang, J., Li, L.P., Liu, Z.J. (2021). A novel sensor-based human activity recognition method based on hybrid feature selection and combinational optimization. IEEE Access, 9, 107235-107249. https://doi.org/10.1109/ACCESS.2021.310058010.1109/ACCESS.2021.3100580
  24. [24] Hassan, M.M., Uddin, M.Z., Mohamed, A., Almogren, A. (2018). A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems, 81, 307-313. https://doi.org/10.1016/j.future.2017.11.02910.1016/j.future.2017.11.029
  25. [25] Janarthanan, R., Doss, S., Baskar, S. (2020). Optimized unsupervised deep learning assisted reconstructed coder in the on-nodule wearable sensor for human activity recognition. Measurement, 164 (3), 108050. https://doi.org/10.1016/j.measurement.2020.10805010.1016/j.measurement.2020.108050
  26. [26] Iloga, S., Bordat, A., Kernec, J.L., Romain, O. (2021). Human activity recognition based on acceleration data from smartphones using HMMs. IEEE Access, 9, 139336-139351. https://doi.org/10.1109/ACCESS.2021.311733610.1109/ACCESS.2021.3117336
  27. [27] Coelho, Y.L., Santos, F., Frizera-Neto, A., Bastos-Filho, T.F. (2021). Lightweight framework for human activity recognition on wearable devices. IEEE Sensors Journal, 21 (21), 24471-24481. https://doi.org/10.1109/JSEN.2021.311390810.1109/JSEN.2021.3113908
  28. [28] Ando, B., Baglio, S., Lombardo, C.O., Marletta, V. (2016). A multisensor data-fusion approach for ADL and fall classification. IEEE Transactions on Instrumentation and Measurement, 65 (9), 1960-1967. https://doi.org/10.1109/TIM.2016.255267810.1109/TIM.2016.2552678
  29. [29] Webber, M., Rojas, R.F. (2021). Human activity recognition with accelerometer and gyroscope: A data fusion approach. IEEE Sensors Journal, 21 (15), 16979-16989. https://doi.org/10.1109/JSEN.2021.307988310.1109/JSEN.2021.3079883
  30. [30] Kok, M., Hol, J.D., Schon, T.B. (2017). Using inertial sensors for position and orientation Estimation. Foundations and Trends in Signal Processing, 11 (1-2), 1-153. http://dx.doi.org/10.1561/200000009410.1561/2000000094
  31. [31] Melgani F., Bazi, Y. (2008) Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Transactions on Information Technology in Biomedicine, 12 (5), 667-677. https://doi.org/10.1109/TITB.2008.92314710.1109/TITB.2008.92314718779082
Language: English
Page range: 193 - 201
Submitted on: Jan 1, 2022
|
Accepted on: Apr 20, 2022
|
Published on: May 14, 2022
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2022 Mingxing Zhang, Hongpeng Li, Tian Ge, Zhaozong Meng, Nan Gao, Zonghua Zhang, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.