1. bookVolume 22 (2022): Issue 4 (August 2022)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

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

Published Online: 14 May 2022
Volume & Issue: Volume 22 (2022) - Issue 4 (August 2022)
Page range: 193 - 201
Received: 01 Jan 2022
Accepted: 20 Apr 2022
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.

Keywords

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo