A CNN-LSTM Approach to Human Activity Recognition
Abstract
To understand human behavior and intrinsically anticipate human intentions, research into human activity recognition HAR) using sensors in wearable and handheld devices has intensified. The ability for a system to use as few resources as possible to recognize a user's activity from raw data is what many researchers are striving for. In this paper, we propose a holistic deep learning-based activity recognition architecture, a convolutional neural network-long short-term memory network (CNN-LSTM). This CNN-LSTM approach not only improves the predictive accuracy of human activities from raw data but also reduces the complexity of the model while eliminating the need for advanced feature engineering. The CNN-LSTM network is both spatially and temporally deep. Our proposed model achieves a 99% accuracy on the iSPL dataset, an internal dataset, and a 92 % accuracy on the UCI HAR public dataset. We also compared its performance against other approaches. It competes favorably against other deep neural network (DNN) architectures that have been proposed in the past and against machine learning models that rely on manually engineered feature datasets.
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?
Transformers have demonstrated efficiency for seq-to-seq recognition in HAR systems.
“Besides, transformers are becoming popular for HAR systems and have demonstrated efficiency for seq-to-seq recognition [21].”
From DS-MS-TCN: Otago Exercises Recognition With a Dual-Scale Multi-Stage Temporal Convolutional Network (2024) · cites this paper as [21]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors
Moya Rueda, F., Grzeszick, R., Fink, G. A., Feldhorst, S., & Ten Hompel, M. (2018) · Informatics · doi:10.3390/informatics5020026 · Cited by 196 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671 · see citing works
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
“Similarly, other researchers [8, 82, 94, 95, 197, 245, 301, 302], [238] have also developed the CNN-LSTM model in varios application scenarios by taking advantage of the feature extraction ability of CNN and the time-series data reasoning ability of LSTM.”
BackgroundMulti-input CNN-GRU based human activity recognition using wearable sensors
Dua, N., Singh, S. N., & Semwal, V. B. (2021) · Computing · doi:10.1007/s00607-021-00928-8 · Cited by 432 · see citing works
A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
Challa, S. K., Kumar, A., & Semwal, V. B. (2022) · The Visual Computer · doi:10.1007/s00371-021-02283-3 · Cited by 338 · see citing works
Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor
Khatun, M. A., Yousuf, M. A., Ahmed, S., Uddin, M. Z., Alyami, S. A., Al-Ashhab, S., Akhdar, H. F., Khan, A., Azad, A., & Moni, M. A. (2022) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2022.3177710 · Cited by 288 · see citing works
Deep learning for early warning signals of tipping points
Bury, T. M., Sujith, R. I., Pavithran, I., Scheffer, M., Lenton, T. M., Anand, M., & Bauch, C. T. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106140118 · Cited by 284 · see citing works
“As a result, this approach excels at pattern recognition and sequence prediction (25, 26).”
BackgroundMethodMultiscale Deep Feature Learning for Human Activity Recognition Using Wearable Sensors
Tang, Y., Zhang, L., Min, F., & He, J. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3161812 · Cited by 224 · see citing works
Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model
Luwe, Y. J., Lee, C. P., & Lim, K. M. (2022) · Informatics · doi:10.3390/informatics9030056 · Cited by 92 · see citing works
Hoai Thu, N. T., & Han, D. S. (2021) · IEEE Access · doi:10.1109/access.2021.3122298 · Cited by 54 · see citing works
Self-citationHuman Activity Recognition Data Analysis: History, Evolutions, and New Trends
Ariza-Colpas, P. P., Vicario, E., Oviedo-Carrascal, A. I., Butt Aziz, S., Piñeres-Melo, M. A., Quintero-Linero, A., & Patara, F. (2022) · Sensors · doi:10.3390/s22093401 · Cited by 52 · see citing works
Convolutional neural network and long short-term memory models for ice-jam predictions
Madaeni, F., Chokmani, K., Lhissou, R., Homayouni, S., Gauthier, Y., & Tolszczuk-Leclerc, S. (2022) · The Cryosphere · doi:10.5194/tc-16-1447-2022 · Cited by 51 · see citing works
Coffen, B., & Mahmud, M. S. (2021) · 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM) · doi:10.1109/healthcom49281.2021.9399005 · Cited by 44 · see citing works
Light Residual Network for Human Activity Recognition using Wearable Sensor Data
Calatrava-Nicolás, F. M., & Mozos, O. M. (2023) · IEEE Sensors Letters · doi:10.1109/lsens.2023.3311623 · Cited by 22 · see citing works
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models
Shang, M., Dedeyne, L., Dupont, J., Vercauteren, L., Amini, N., Lapauw, L., Gielen, E., Verschueren, S., Varon, C., De Raedt, W., et al. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3355299 · Cited by 21 · see citing works
DS-MS-TCN: Otago Exercises Recognition With a Dual-Scale Multi-Stage Temporal Convolutional Network
Shang, M., Dedeyne, L., Dupont, J., Vercauteren, L., Amini, N., Lapauw, L., Gielen, E., Verschueren, S., Varon, C., De Raedt, W., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3455426 · Cited by 5 · see citing works
“Recently, researchers have applied the combination of CNN and Long short-term memory (LSTM) cells (CNN-LSTM) [21], [22], which outperformed single CNN or LSTM network on many datasets.”
Background
- Authors
- Mutegeki, R., & Han, D. S.
- Affiliations
- Kyungpook National University (KR)
- Venue
- 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
- Publisher
- IEEE
- Published
- 2020-02
- Pages
- 362-366
- OpenAlex
- W3017361427
- Cited by
- 452
- References
- 2
- Citations by year
- 2026: 30 · 2025: 78 · 2024: 113 · 2023: 105 · 2022: 77 · 2021: 43 · 2020: 6
- FWCI
- 17.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (publishedVersion)
- 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, CNN-LSTM, wearable sensors, handheld devices, raw sensor data, low-resource models, UCI HAR dataset, spatio-temporal modeling
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
