Human activities recognition with a single writs IMU via a Variational Autoencoder and android deep recurrent neural nets
Open accessAbstract
Human Activity Recognition (HAR) is an active research field because of its versatility towards various application areas such as healthcare and lifecare. In this study, a novel HAR system is proposed based on an autoencoder for denoising and Recurrent Neural Network (RNN) for classification with a single Inertial Measurement Unit (IMU) located on a dominant wrist. A Variational Autoencoder (VAE) is built to denoise IMU signals which improves HAR by Android Deep RNN. Evaluating our VAE and Android Deep RNN HAR system is done in two ways. First, the system is tested on a PC using discrete epochs of activities of daily living. Our results show that VAE improves Signal-to-Noise Ratio (SNR) of the IMU signal from 8.78 to 17.26 dB. In turn, HAR improves from 89.29% to 95.11% in F1-score and from 90.38% to 95.47% in accuracy. Secondly, the system is tested on an Android device (i.e., smartphone) using continuous activity signals. This is done by transferring the PC HAR system to an Android HAR App (i.e., Android Deep RNN). We have achieved 86.13% and 95.09% in accuracy without and with VAE respectively. Our results demonstrate that HAR can be achieved in real-time on a standalone smart device with a single IMU for lifelogging services.
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Autoencoders are commonly used to sanitize and denoise raw sensor data, a known problem with wearable signals that impacts the ability to learn patterns in the data.
“Furthermore, autoencoders are commonly used to sanitize and denoise raw sensor data [127,130,148], a known problem with wearable signals that impacts our ability to learn patterns in the data.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [148]
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Cites
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 2× · see citing works
“, inference on Android device), while most of the previous HAR systems are implemented only for PC [29] [28] [27].”
BackgroundIntroducing a New Benchmarked Dataset for Activity Monitoring
Reiss, A., & Stricker, D. (2012) · 2012 16th International Symposium on Wearable Computers · doi:10.1109/iswc.2012.13 · Cited by 1,456 · cited 1× · see citing works
“In this study, we used a public database (DB), Physical Activity Monitoring for Aging People (PAMAP2) [30] [31].”
MethodA Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder
Park, D., Hoshi, Y., & Kemp, C. C. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2801475 · Cited by 1,131 · see citing works
(2015) · Cited by 989 · see citing works
van Hees, V. T., Gorzelniak, L., Dean‐Leon, E., Eder, M., Pias, M. R., Taherian, S., Ekelund, U., Renström, F., Franks, P. W., Horsch, A., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061691 · Cited by 937 · see citing works
Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables
Hammerla, N. Y., Halloran, S., & Ploetz, T. (2016) · arXiv · doi:10.48550/arxiv.1604.08880 · Cited by 606 · see citing works
Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., & Havinga, P. (2010) · University of Twente Research Information · Cited by 443 · see citing works
Creating and benchmarking a new dataset for physical activity monitoring
Reiss, A., & Stricker, D. (2012) · Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments · doi:10.1145/2413097.2413148 · Cited by 295 · see citing works
Can Deep Learning Revolutionize Mobile Sensing?
Lane, N. D., & Georgiev, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications · doi:10.1145/2699343.2699349 · Cited by 282 · see citing works
ECG signal enhancement based on improved denoising auto-encoder
Xiong, P., Wang, H., Liu, M., Zhou, S., Hou, Z., & Liu, X. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.02.015 · Cited by 174 · see citing works
Optimising sampling rates for accelerometer-based human activity recognition
Khan, A., Hammerla, N., Mellor, S., & Plötz, T. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.01.001 · Cited by 171 · see citing works
Lai, Y.-H., Chen, F., Wang, S., Lu, X., Tsao, Y., & Lee, C. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2613960 · Cited by 122 · see citing works
Margarito, J., Helaoui, R., Bianchi, A., Sartor, F., & Bonomi, A. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2471094 · Cited by 118 · see citing works
Ghasemzadeh, H. G., & Jafari, R. (2010) · IEEE Sensors Journal · doi:10.1109/jsen.2010.2048205 · Cited by 109 · see citing works
Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body
Arif, M., & Kattan, A. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130851 · Cited by 100 · see citing works
An effective deep autoencoder approach for online smartphone-based human activity recognition
Almaslukh, B., Artoli, A. M., & Al-Muhtadi, J. F. (2017) · Cited by 94 · see citing works
Li, H., & Misra, S. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2766130 · Cited by 86 · see citing works
A Footwear-Based Methodology for Fall Detection
Montanini, L., Campo, A. D., Perla, D., Spinsante, S., & Gambi, E. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2778742 · Cited by 85 · see citing works
Multicolumn bidirectional long short-term memory for mobile devices-based human activity recognition
Tao, D., Wen, Y., & Hong, R. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2561962 · Cited by 83 · see citing works
Lin, B.-S., Hsiao, P.-C., Yang, S., Su, C.-S., & Lee, I. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2720727 · Cited by 82 · see citing works
Activity recognition with smartphone support
Guiry, J. J., van de Ven, P. W. J., Nelson, J., Warmerdam, L., & Riper, H. M. (2014) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2014.02.009 · Cited by 79 · see citing works
Fusing Heterogeneous Features From Stacked Sparse Autoencoder for Histopathological Image Analysis
Zhang, X., Dou, H., Ju, T., Xu, J., & Zhang, S. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2461671 · Cited by 78 · see citing works
Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering
Grozdić, Đ., & Jovičić, S. T. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2738559 · Cited by 72 · see citing works
Wearable Motion Sensor Based Analysis of Swing Sports
Anand, A., Sharma, M., Srivastava, R., Kaligounder, L., & Prakash, D. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.0-149 · Cited by 54 · see citing works
Real time human activity recognition on smartphones using LSTM networks
Milenkoski, M., Trivodaliev, K. V., Kalajdziski, S., Jovanov, M., & Stojkoska, B. R. (2018) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro.2018.8400205 · Cited by 44 · see citing works
Real-time food intake classification and energy expenditure estimation on a mobile device
Ravì, D., Lo, B. P. L., & Yang, G. (2015) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2015.7299410 · Cited by 36 · see citing works
IMU-based underwater sensing system for swimming stroke classification and motion analysis
Zhang, Z., Xu, D., Zhou, Z., et al. (2017) · IEEE International Conference on Cyborg and Bionic Systems (CBS) · doi:10.1109/cbs.2017.8266113 · Cited by 34 · see citing works
Activity Detection and Analysis Using Smartphone Sensors
Vaughn, A., Biocco, P., Liu, Y., et al. (2018) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2018.00022 · Cited by 34 · see citing works
Embedded system for fall detection using body-worn accelerometer and depth sensor
Kępski, M., & Kwolek, B. (2015) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2015.7341404 · Cited by 33 · see citing works
A continuous patient activity monitor: validation and relation to disability
Walker, D., Heslop, P., Plummer, C., Essex, T., & Chandler, S. (1997) · Physiological Measurement · doi:10.1088/0967-3334/18/1/003 · Cited by 32 · see citing works
Mohammed, S., & Tashev, I. (2017) · 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2017.7936037 · Cited by 29 · see citing works
Valarezo, E., Rivera, P., Park, J. M., et al. (2017) · Journal of ICT Design Engineering and Technological Science · doi:10.33150/jitdets-1.1.1 · Cited by 14 · see citing works
Implementation of safety alert system for elderly people using multi-sensors
Singh, N. R., Rothe, P. R., & Rathkanthiwar, A. P. (2017) · 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca.2017.8203688 · Cited by 12 · see citing works
Detailed Activity Recognition with Smartphones
RoyChowdhury, I., Saha, J., & Chowdhury, C. (2018) · International Conference on Emerging Applications of Information Technology · doi:10.1109/eait.2018.8470425 · Cited by 12 · see citing works
Changing the Game of Mobile Data Analysis with Deep Learning
Kasnesis, P., Patrikakis, C. Z., & Venieris, I. (2017) · IT Professional · doi:10.1109/mitp.2017.265110829 · Cited by 9 · see citing works
Temporal Learning Using Echo State Network for Human Activity Recognition
Basterrech, S., & Ojha, V. (2016) · European Network Intelligence Conference (ENIC) · doi:10.1109/enic.2016.039 · Cited by 6 · see citing works
Heymann, J., Haeb‐Umbach, R., Golik, P., & Schlüter, R. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178933 · Cited by 5 · see citing works
Elakkiya, T. (2017) · doi:10.1109/iconstem.2017.8261318 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- M. Jongprasithporn, N. Yodpijit and R. Srivilai, "A smartphone-based real-time simple activity recognition -IEEE Xplore Document," 3rd International Conference on Control, Automation and Robotics, pp. 539-542. (2017)
- E. Bulbul, A. Centin and I. A. Dogru, "Human Activity Recognition Using Smartpones," 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1-6. (2018)
- S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, pp. 1735-1780. (1997)
- C. Beleites, R. Salzer, and V. Sergo, "Validation of soft classification models using partial class memberships: An extended concept of sensitivity & co. applied to grading of astrocytoma tissues," Chemometrics and Intelligent Laboratory Systems, vol. 122, pp. 12 - 22. (2013)
Cited by
Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“Furthermore, autoencoders are commonly used to sanitize and denoise raw sensor data [98, 190, 280], a known problem with wearable signals that impacts our ability to learn patterns in the data.”
Background
- Authors
- Valarezo, A., Rivera, L., Park, H., Park, N., & Kim, T.-S.
- Affiliations
- Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Republic of Korea
- Kyung Hee University (KR)
- Venue
- Computer Science and Information Systems
- Publisher
- National Library of Serbia
- Published
- 2020
- Volume
- 17
- Issue
- 2
- Pages
- 581-597
- ISSN
- 1820-0214 (ISSN-L)
- 2406-1018
- OpenAlex
- W3037306157
- Cited by
- 12
- References
- 38
- Citations by year
- 2025: 3 · 2023: 5 · 2022: 1 · 2021: 3
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Computer Science and Information Systems (publishedVersion, cc-by)
- Funders
- Escuela Superior Politécnica del Litoral
- National Research Foundation
- National Research Foundation of Korea (NRF-2019R1A2C1003713)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- human activity recognition, inertial measurement unit, variational autoencoder, signal denoising, recurrent neural networks, wrist-worn sensors, lifelogging, activities of daily living, real-time activity classification
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