ST-DeepHAR: Deep Learning Model for Human Activity Recognition in IoHT Applications
Abstract
Human activity recognition (HAR) has been regarded as an indispensable part of many smart home systems and smart healthcare applications. Specifically, HAR is of great importance in the Internet of Healthcare Things (IoHT), owing to the rapid proliferation of Internet of Things (IoT) technologies embedded in various smart appliances and wearable devices (such as smartphones and smartwatches) that have a pervasive impact on an individual's life. The inertial sensors of smartphones generate massive amounts of multidimensional time-series data, which can be exploited effectively for HAR purposes. Unlike traditional approaches, deep learning techniques are the most suitable choice for such multivariate streams. In this study, we introduce a supervised dual-channel model that comprises long short-term memory (LSTM), followed by an attention mechanism for the temporal fusion of inertial sensor data concurrent with a convolutional residual network for the spatial fusion of sensor data. We also introduce an adaptive channel-squeezing operation to fine-tune convolutional a neural network feature extraction capability by exploiting multichannel dependency. Finally, two widely available and public HAR data sets are used in experiments to evaluate the performance of our model. The results demonstrate that our proposed approach can overcome state-of-the-art methods.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recently, many efforts have been put into improving the generalizability of models in HAR.
“Recently, many efforts have been put into improving the generalizability of models in HAR [86,302,303].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [303]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-80 · Cited by 2,973 · see citing works
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 4× · see citing works
“To examine the proposed ST-deepHAR in IoHT-based applications, two widely used data sets [9], [10] have been aggregated from inertial sensors and employed for training and evaluation of the proposed ST-deepHAR and other models.”
BackgroundMethodInfluentialAnguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35395-6_30 · Cited by 998 · see citing works
LSTM-CNN Architecture for Human Activity Recognition
Xia, K., Huang, J., & Wang, H. (2020) · IEEE Access · doi:10.1109/access.2020.2982225 · Cited by 851 · see citing works
Deep Learning Models for Real-time Human Activity Recognition with Smartphones
Wan, S., Qi, L., Xu, X., Tong, C., & Gu, Z. (2019) · Mobile Networks and Applications · doi:10.1007/s11036-019-01445-x · Cited by 597 · see citing works
Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things
Zhou, X., Liang, W., Wang, K. I., Wang, H. H., Yang, L. T., & Jin, Q. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2985082 · Cited by 557 · see citing works
Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., & De Munari, I. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2920283 · Cited by 466 · see citing works
A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition
Chen, K., Yao, L., Zhang, D., Wang, X., Chang, X., & Nie, F. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2927224 · Cited by 368 · see citing works
InnoHAR: A Deep Neural Network for Complex Human Activity Recognition
Xu, C., Chai, D., He, J., Zhang, X., & Duan, S. (2019) · IEEE Access · doi:10.1109/access.2018.2890675 · Cited by 333 · see citing works
Bi-LSTM Network for Multimodal Continuous Human Activity Recognition and Fall Detection
Li, H., Shrestha, A., Heidari, H., Kernec, J. L., & Fioranelli, F. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2946095 · Cited by 282 · see citing works
Wang, K., He, J., & Zhang, L. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2917225 · Cited by 205 · see citing works
Zhang, H., Xiao, Z., Wang, J., Li, F., & Szczerbicki, E. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2949715 · Cited by 199 · see citing works
Adaptive Pooling Operators for Weakly Labeled Sound Event Detection
McFee, B., Salamon, J., & Bello, J. P. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2858559 · Cited by 165 · see citing works
TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition
Huang, J., Lin, S., Wang, N., et al. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2909688 · Cited by 146 · see citing works
A Novel Ensemble ELM for Human Activity Recognition Using Smartphone Sensors
Chen, Z., Jiang, C., & Xie, L. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2869843 · Cited by 144 · see citing works
Human Activity Recognition Based on Motion Sensor Using U-Net
Zhang, Y., Zhang, Z., Zhang, Y., Bao, J., Zhang, Y., & Deng, H. (2019) · IEEE Access · doi:10.1109/access.2019.2920969 · Cited by 121 · see citing works
Deep Spatial–Temporal Model Based Cross-Scene Action Recognition Using Commodity WiFi
Sheng, B., Xiao, F., Sha, L., & Sun, L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2973272 · Cited by 116 · see citing works
Chen, Z., Jiang, C., Xiang, S., Ding, J., Wu, M., & Li, X. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2945467 · Cited by 105 · see citing works
Human Action Recognition Using Deep Learning Methods on Limited Sensory Data
Tüfek, N., Yalcin, M., Altıntaş, M., Kalaoğlu, F., Li, Y., & Bahadır, S. K. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2956901 · Cited by 105 · see citing works
A Novel Semisupervised Deep Learning Method for Human Activity Recognition
Zhu, Q., Chen, Z., & Soh, Y. C. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2889315 · Cited by 97 · see citing works
Efficient Human Activity Recognition Solving the Confusing Activities Via Deep Ensemble Learning
Zhu, R., Xiao, Z., Li, Y., Yang, M., Tan, Y., Zhou, L., Lin, S., & Wen, H. (2019) · IEEE Access · doi:10.1109/access.2019.2922104 · Cited by 86 · see citing works
Interpretable and Accurate Convolutional Neural Networks for Human Activity Recognition
Kim, E. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2972628 · Cited by 85 · see citing works
Wearable Computing for Internet of Things: A Discriminant Approach for Human Activity Recognition
Lu, W., Fan, F., Chu, J., Jing, P., & Su, Y. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2873594 · Cited by 82 · see citing works
Music Recommendation System Using Human Activity Recognition From Accelerometer Data
Kim, H., Kim, G. Y., & Kim, J. Y. (2019) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2019.2924177 · Cited by 81 · see citing works
Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs
Muñoz-Organero, M. (2019) · IEEE Access · doi:10.1109/access.2019.2921096 · Cited by 70 · see citing works
Keshavarzian, A., Sharifian, S., & Seyedin, S. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.06.009 · Cited by 64 · see citing works
Learning-Aided User Identification Using Smartphone Sensors for Smart Homes
Qin, Z., Hu, L., Zhang, N., et al. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2900862 · Cited by 61 · see citing works
PSDRNN: An Efficient and Effective HAR Scheme Based on Feature Extraction and Deep Learning
Xiao, L., Wang, Y., Zhang, B., & Ma, J. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2968920 · Cited by 56 · see citing works
Dezert-Smarandache Theory-Based Fusion for Human Activity Recognition in Body Sensor Networks
Dong, Y., Li, X., Dezert, J., Khyam, M. O., Noor‐A‐Rahim, M., & Ge, S. S. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2976812 · Cited by 52 · see citing works
Hierarchical Two-Stream Growing Self-Organizing Maps With Transience for Human Activity Recognition
Nawaratne, R., Alahakoon, D., Silva, D. D., Kumara, H., & Yu, X. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2957454 · Cited by 35 · see citing works
Using Latent Knowledge to Improve Real-Time Activity Recognition for Smart IoT
Yan, S., Lin, K.-J., Zheng, X., et al. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2891659 · Cited by 32 · see citing works
3-D Human Pose Estimation Using Iterative Conditional Squeeze and Excitation Networks
McLaughlin, N., del Rincón, J. M., & Miller, P. C. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2964992 · Cited by 18 · see citing works
A Localized Learning Approach Applied to Human Activity Recognition
Amer, A. Y. A., Aerts, J., Vanrumste, B., & Luca, S. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2964738 · Cited by 12 · see citing works
Flagged first, then by citations
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
Human Activity Recognition Based on Residual Network and BiLSTM
Li, Y., & Wang, L. (2022) · Sensors · doi:10.3390/s22020635 · Cited by 156 · see citing works
- Authors
- Abdel-Basset, M., Hawash, H., Chakrabortty, R. K., Ryan, M., Elhoseny, M., & Song, H.
- Affiliations
- Zagazig University (EG)
- UNSW Sydney (AU)
- Mansoura University (EG)
- Embry-Riddle Aeronautical University Daytona Beach Florida Campus (US)
- ORCID
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-03-15
- Volume
- 8
- Issue
- 6
- Pages
- 4969-4979
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3093916778
- Cited by
- 136
- References
- 34
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 34 · 2023: 38 · 2022: 26 · 2021: 12
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Scholarly Commons (Embry–Riddle Aeronautical University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, Internet of Medical Things, inertial data, smartphone sensors, wearable devices, LSTM, attention mechanism, home automation, smart healthcare
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
