A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM
Open accessAbstract
Recently, the demand for human activity recognition has become more and more urgent. It is widely used in indoor positioning, medical monitoring, safe driving, etc. Existing activity recognition approaches require either the location information of the sensors or the specific domain knowledge, which are expensive, intrusive, and inconvenient for pervasive implementation. In this paper, a human activity recognition algorithm based on SDAE (Stacking Denoising Autoencoder) and LightGBM (LGB) is proposed. The SDAE is adopted to sanitize the noise in raw sensor data and extract the most effective characteristic expression with unsupervised learning. The LGB reveals the inherent feature dependencies among categories for accurate human activity recognition. Extensive experiments are conducted on four datasets of distinct sensor combinations collected by different devices in three typical application scenarios, which are human moving modes, current static, and dynamic behaviors of users. The experimental results demonstrate that our proposed algorithm achieves an average accuracy of 95.99%, outperforming other comparative algorithms using XGBoost, CNN (Convolutional Neural Network), CNN + Statistical features, or single SDAE.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
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 [130]
Gao et al. explores the use of stacking autoencoders to denoise raw sensor data to improve HAR using the UCI dataset.
“Gao et al. explores the use of stacking autoencoders to denoise raw sensor data to improve HAR using the UCI dataset [82,130].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [130]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · 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 1× · see citing works
“By repeating the error classification samples in the validation set of the previous fold, the attention of the n th fold error samples can be improved in the n + 1 th fold training.”
MethodCyberguide: A mobile context‐aware tour guide
Abowd, G. D., Atkeson, C. G., Hong, J., Long, S., Rob Kooper, A., & Pinkerton, M. (1997) · Wireless Networks · doi:10.1023/a:1019194325861 · Cited by 1,379 · see citing works
Anguita, 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
Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
Ignatov, A. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.09.027 · Cited by 817 · see citing works
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers
Mannini, A., & Sabatini, A. M. (2010) · Sensors · doi:10.3390/s100201154 · Cited by 785 · see citing works
Window Size Impact in Human Activity Recognition
Baños, O., Gálvez, J. M., Damas, M., Pomares, H., & Rojas, I. (2014) · Sensors · doi:10.3390/s140406474 · Cited by 656 · see citing works
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · Applied Sciences · doi:10.3390/app7101101 · Cited by 596 · see citing works
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · doi:10.20944/preprints201706.0033.v1 · Cited by 596 · see citing works
Towards Environment Independent Device Free Human Activity Recognition
Jiang, W., Miao, C., Ma, F., Yao, S., Wang, Y., Yuan, Y., Xue, H., Song, C., Ma, X., Koutsonikolas, D., et al. (2018) · Proceedings of the 24th Annual International Conference on Mobile Computing and Networking · doi:10.1145/3241539.3241548 · Cited by 573 · see citing works
A Study on Human Activity Recognition Using Accelerometer Data from Smartphones
Bayat, A., Pomplun, M., & Tran, D. A. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.07.009 · Cited by 531 · see citing works
A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer
Chen, Y., & Xue, Y. (2015) · 2015 IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.263 · Cited by 378 · see citing works
On-body localization of wearable devices: An investigation of position-aware activity recognition
Sztyler, T., & Stuckenschmidt, H. (2016) · 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) · doi:10.1109/percom.2016.7456521 · Cited by 369 · see citing works
Seyfioğlu, M. S., Özbayoğlu, A. M., & Gürbüz, S. Z. (2018) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2799758 · Cited by 354 · see citing works
An empirical analysis of feature engineering for predictive modeling
Heaton, J. (2016) · SoutheastCon 2016 · doi:10.1109/secon.2016.7506650 · Cited by 301 · see citing works
Deep recurrent neural network for mobile human activity recognition with high throughput
Inoue, M., Inoue, S., & Nishida, T. (2017) · Artificial Life and Robotics · doi:10.1007/s10015-017-0422-x · Cited by 277 · see citing works
HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices
Khalifa, S., Lan, G., Hassan, M., Seneviratne, A. P., & Das, S. K. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2761744 · Cited by 160 · see citing works
GCHAR: An efficient Group-based Context—aware human activity recognition on smartphone
Cao, L., Wang, Y., Zhang, B., Jin, Q., & Vasilakos, A. V. (2017) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2017.05.007 · Cited by 154 · see citing works
Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning
Pei, L., Liu, J., Guinness, R. E., Chen, Y., Kuusniemi, H., & Chen, R. (2012) · Sensors · doi:10.3390/s120506155 · Cited by 130 · see citing works
Zou, H., Zhou, Y., Yang, J., Jiang, H., Xie, L.-H., & Spanos, C. (2018) · 2018 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2018.8422895 · Cited by 127 · see citing works
Locomotion Activity Recognition Using Stacked Denoising Autoencoders
Gu, F., Khoshelham, K., Valaee, S., Shang, J., & Zhang, R. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2823084 · Cited by 122 · see citing works
Kawaguchi, N., Ogawa, N., Iwasaki, Y., Kaji, K., Terada, T., Murao, K., Inoue, S., Kawahara, Y., Sumi, Y., & Nishio, N. (2011) · Augmented Human International Conference · doi:10.1145/1959826.1959853 · Cited by 113 · see citing works
Multilevel Depth and Image Fusion for Human Activity Detection
Ni, B., Pei, Y., Moulin, P., & Yan, S. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2276433 · Cited by 111 · see citing works
Human Activities Recognition in Android Smartphone Using Support Vector Machine
Tran, D. N., & Phan, D. D. (2016) · International Conference on Intelligent Systems, Modelling and Simulation · doi:10.1109/isms.2016.51 · Cited by 105 · see citing works
HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing
Bharti, P., De, D., Chellappan, S., & Das, S. K. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2841905 · Cited by 96 · see citing works
Small lung nodules detection based on local variance analysis and probabilistic neural network
Woźniak, M., Połap, D., Capizzi, G., Sciuto, G. L., Kośmider, L., & Frankiewicz, K. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.025 · Cited by 93 · see citing works
Leonardis, G. D., Rosati, S., Balestra, G., Agostini, V., Panero, E., Gastaldi, L., & Knaflitz, M. (2018) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2018.8438750 · Cited by 92 · see citing works
Unsupervised Feature Learning for Human Activity Recognition Using Smartphone Sensors
Li, Y., Shi, D., Ding, B., & Liu, D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13817-6_11 · Cited by 89 · see citing works
Robust feature learning by stacked autoencoder with maximum correntropy criterion
Qi, Y., Wang, Y., Zheng, X., & Wu, Z. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854900 · Cited by 85 · see citing works
Wang, G., Li, Q., Wang, L., Wang, W., Wu, M., & Liu, T. (2018) · Sensors · doi:10.3390/s18061965 · Cited by 82 · see citing works
Driver behavior recognition based on deep convolutional neural networks
Yan, S., Teng, Y., Smith, J. S., & Zhang, B. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603248 · Cited by 77 · see citing works
A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition
Almaslukh, B., Artoli, A. M., & Al-Muhtadi, J. F. (2018) · Sensors · doi:10.3390/s18113726 · Cited by 71 · see citing works
Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors
Rahim, K. N. K. A., Elamvazuthi, I., Izhar, L. I., & Capi, G. (2018) · Sensors · doi:10.3390/s18124132 · Cited by 53 · see citing works
Human activity recognition with HMM-DNN model
Zhang, L., Wu, X. H., & Luo, D. (2015) · IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · doi:10.1109/icci-cc.2015.7259385 · Cited by 51 · see citing works
Multi-threaded learning control mechanism for neural networks
Połap, D., Woźniak, M., Wei, W., & Damaševičius, R. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.050 · Cited by 49 · see citing works
Neural Network-Based User-Independent Physical Activity Recognition for Mobile Devices
Kolosnjaji, B., & Eckert, C. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24834-9_44 · Cited by 45 · see citing works
Deep Dilation on Multimodality Time Series for Human Activity Recognition
Xi, R., Li, M., Hou, M., Fu, M., Qu, H., Liu, D., & Haruna, C. R. (2018) · IEEE Access · doi:10.1109/access.2018.2870841 · Cited by 35 · see citing works
Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification
Zhao, W., Meng, Q., Zeng, M., & Qi, P.-F. (2017) · Sensors · doi:10.3390/s17122855 · Cited by 34 · see citing works
Siirtola, P., & Röning, J. (2012) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-28765-7_75 · Cited by 31 · see citing works
Automated Recognition of Complex Agitation Behavior of Dementia Patients Using Video Camera
Fook, V. F. S., Thang, P. V., Htwe, T. M., Qiu, Q., Wai, A. A. P., Jayachandran, M., Biswas, J., & Yap, P. L. K. (2007) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/health.2007.381605 · Cited by 27 · see citing works
Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
Liu, J., Chen, F., & Wang, D. (2018) · Sensors · doi:10.3390/s18124273 · Cited by 25 · see citing works
Genome-Wide Linkage Disequilibrium and Haplotype Maps
Belmont, J. W., & Gibbs, R. A. (2004) · American Journal of PharmacoGenomics · doi:10.2165/00129785-200404040-00005 · Cited by 22 · see citing works
Convolution neutral network enhanced binary sensor network for human activity recognition
Liu, G., Liang, J., Lan, G., Hao, Q., & Chen, M. (2016) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2016.7808519 · Cited by 21 · see citing works
On-Body Sensor Positions Hierarchical Classification
Sang, V. N. T., Yano, S., & Kondo, T. (2018) · Sensors · doi:10.3390/s18113612 · Cited by 20 · see citing works
Deep learning approach for EEG compression in mHealth system
Said, A. B., Mohamed, A. M. S., & Elfouly, T. (2017) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2017.7986507 · Cited by 14 · see citing works
Classification of human activity by using a Stacked Autoencoder
Badem, H., Çalışkan, A., Baştürk, A., & Yüksel, M. E. (2016) · Medical Technologies Congress (TIPTEKNO) · doi:10.1109/tiptekno.2016.7863135 · Cited by 13 · see citing works
TaxiMedia: An interactive context-aware entertainment and advertising system
Alt, F., Shirazi, A. S., Pfeiffer, M., Holleis, P., & Schmidt, A. (2009) · GI-Jahrestagung · Cited by 8 · see citing works
Complex activity recognition with multi-modal multi-positional body sensing
Bharti, P. (2017) · Journal of Biometrics & Biostatistics · doi:10.4172/2155-6180-c1-005 · Cited by 6 · see citing works
Chen, Y.-J., Cheng, S., & Yang, C.-K. (2017) · International Symposium on Communications and Information Technologies · doi:10.1109/iscit.2017.8261171 · Cited by 1 · 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
“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.”
BackgroundMethodKumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09986-x · Cited by 220 · see citing works
Deep learning based human activity recognition (HAR) using wearable sensor data
Gupta, S. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100046 · Cited by 225 · see citing works
A review of machine learning-based human activity recognition for diverse applications
Kulsoom, F., Narejo, S., Mehmood, Z., Chaudhry, H. N., Butt, A., & Bashir, A. K. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07665-9 · Cited by 138 · see citing works
Application of Improved LightGBM Model in Blood Glucose Prediction
Wang, Y., & Wang, T. (2020) · Applied Sciences · doi:10.3390/app10093227 · Cited by 129 · see citing works
Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living
Rahman, S., Irfan, M., Raza, M., Ghori, K. M., Yaqoob, S., & Awais, M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17031082 · Cited by 119 · see citing works
Thakur, D., Biswas, S., Ho, E. S. L., & Chattopadhyay, S. (2022) · IEEE Access · doi:10.1109/access.2022.3140373 · Cited by 104 · see citing works
Zhou, S., Zhang, D., Wang, M., Liu, Z., Gan, W., Zhao, Z., Xue, S., Müller, B., Zhou, M., Ni, X., et al. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.142286 · Cited by 94 · see citing works
Deep learning for the internet of things: Potential benefits and use-cases
Saleem, T. J., & Chishti, M. A. (2020) · Digital Communications and Networks · doi:10.1016/j.dcan.2020.12.002 · Cited by 91 · see citing works
A survey on unsupervised learning for wearable sensor-based activity recognition
Ige, A. O., & Noor, M. H. M. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109363 · Cited by 88 · see citing works
Wang, Q., Luo, H., Xiong, H., Men, A., Zhao, F., Xia, M. Y., & Ou, C. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3016146 · Cited by 85 · see citing works
Self-citationAn ensemble of autonomous auto-encoders for human activity recognition
Garcia, K. D., de Sá, C. R., Poel, M., Carvalho, T., Mendes‐Moreira, J., Cardoso, J. M. P., de Carvalho, A. C. P. L. F., & Kok, J. N. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.01.125 · Cited by 74 · see citing works
Potortì, F., Torres-Sospedra, J., Quezada-Gaibor, D., Jiménez, A. R., Seco, F. A., Pérez-Navarro, A., Ortiz, M., Zhu, N., Renaudin, V., Ichikari, R., et al. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3083149 · Cited by 74 · see citing works
Self-citationSensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model
Rustam, F., Reshi, A. A., Ashraf, I., Mehmood, A., Ullah, S., Khan, D. M., & Choi, G. S. (2020) · IEEE Access · doi:10.1109/access.2020.3041822 · Cited by 72 · see citing works
Personalized Stride-Length Estimation Based on Active Online Learning
Wang, Q., Luo, H., Ye, L., Men, A., Zhao, F., Huang, Y., & Ou, C. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2971318 · Cited by 48 · see citing works
Self-citationA Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data
Dua, N., Singh, S. N., Challa, S. K., Semwal, V. B., & Kumar, M. L. S. S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24352-3_5 · Cited by 48 · see citing works
Recent Advances in Pedestrian Navigation Activity Recognition: A Review
Wang, Q., Luo, H., Wang, Q., Sun, L., Ma, Z., Zhang, C., Fu, M., & Zhao, F. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3153610 · Cited by 45 · see citing works
Self-citationQi, J., Gao, X., & Huang, N. (2020) · Entropy · doi:10.3390/e22040478 · Cited by 41 · see citing works
Self-citationGuo, W., Chen, W.-B., Yeh, S., Chang, C.-H., & Chen, H. (2021) · Water · doi:10.3390/w13070920 · Cited by 40 · see citing works
A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory
Bragança, H. L. D. S., Colonna, J. G., Lima, W. S., & Souto, E. (2020) · Sensors · doi:10.3390/s20071856 · Cited by 29 · see citing works
Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review
Kimbugwe, N., Pei, T., & Kyebambe, M. N. (2021) · Energies · doi:10.3390/en14196384 · Cited by 32 · see citing works
A survey on intelligent human action recognition techniques
Kumar, R., & Kumar, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17529-6 · Cited by 34 · see citing works
Li, H., Zhang, J., Zhang, S., Zhang, W., Zhang, S., Yu, P., & Song, Z. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108751 · Cited by 29 · see citing works
Human Action Recognition Using CNN-SVM Model
Athavale, V. A., Gupta, S. C., Kumar, D., & Savita, S. (2021) · Advances in science and technology · doi:10.4028/www.scientific.net/ast.105.282 · Cited by 28 · see citing works
Progress of Human Action Recognition Research in the Last Ten Years: A Comprehensive Survey
Singh, P. K., Kundu, S., Adhikary, T., Sarkar, R., & Bhattacharjee, D. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09681-9 · Cited by 31 · see citing works
Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection
Wang, Y., Liu, S., Li, S., Duan, J., Hou, Z., Yu, J., & Ma, K. (2019) · Future Internet · doi:10.3390/fi11070155 · Cited by 24 · see citing works
Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition
Jun, K., & Choi, S. (2020) · Sensors · doi:10.3390/s20226467 · Cited by 21 · see citing works
Classified Early Warning and Forecast of Severe Convective Weather Based on LightGBM Algorithm
Liu, X., Duan, H., Huang, W., Guo, R., & Duan, B. (2021) · Atmospheric and Climate Sciences · doi:10.4236/acs.2021.112017 · Cited by 20 · see citing works
On enabling collaborative non-intrusive load monitoring for sustainable smart cities
Shi, Y., Li, W., Chang, X., Yang, T., Sun, Y., & Zomaya, A. Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-33131-0 · Cited by 20 · see citing works
Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition
Azadi, B., Haslgrübler, M., Anzengruber-Tánase, B., Sopidis, G., & Ferscha, A. (2024) · Sensors · doi:10.3390/s24020681 · Cited by 20 · see citing works
Feature learning using convolutional denoising autoencoder for activity recognition
Noor, M. H. M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05638-4 · Cited by 18 · see citing works
Transferable Tree-Based Ensemble Model for Non-Intrusive Load Monitoring
Chang, X., Li, W., Xia, C., Yang, Q., Ma, J., Yang, T., & Zomaya, A. Y. (2022) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2022.3175941 · Cited by 17 · see citing works
Zhang, C., Li, D., Qi, J., Liu, J., & Wang, Y. (2021) · Sensors · doi:10.3390/s21134522 · Cited by 16 · see citing works
Kang, J., Zou, X., Tan, J., Li, J., & Karimian, H. (2023) · Sustainability · doi:10.3390/su151411408 · Cited by 14 · see citing works
Lakoju, M., Ajienka, N., Khanesar, M. A., Burnap, P., & Branson, D. T. (2021) · Sensors · doi:10.3390/s21154991 · Cited by 14 · see citing works
Minea, M. G., Dumitrescu, C. M., & Costea, I. M. (2021) · Sensors · doi:10.3390/s21248272 · Cited by 13 · see citing works
GAN for Generating User-Specific Human Activity Data From An Incomplete Training Corpus
Gerych, W., Kim, H., DeOliveira, J., Martin, M., Buquicchio, L., Chandrasekaran, K., Alajaji, A. S., Mansoor, H., Rundensteiner, E. A., & Agu, E. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671280 · Cited by 13 · see citing works
Waghchaware, S., & Joshi, R. D. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02122-6 · Cited by 16 · see citing works
A Review of Physical Human Activity Recognition Chain Using Sensors
AlShorman, O., Alshorman, B., & Masadeh, M. (2020) · Indonesian Journal of Electrical Engineering and Informatics (IJEEI) · doi:10.52549/ijeei.v8i3.2312 · Cited by 12 · see citing works
Human Activity Recognition: A Review of RFID and Wearable Sensor Technologies Powered by AI
Kanjilal, R., Küçük, M. F., & Uysal, I. (2025) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2025.3561345 · Cited by 12 · see citing works
Guo, W., Chen, W., & Chang, C.-H. (2023) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2023.101378 · Cited by 11 · see citing works
A Review of Physical Human Activity Recognition Chain Using Sensors
AlShorman, O., Alshorman, B., & Masadeh, M. (2020) · Indonesian Journal of Electrical Engineering and Informatics (IJEEI) · doi:10.11591/ijeei.v8i3.2312 · Cited by 18 · see citing works
Self-Powered Wearable IoT Devices for Health and Activity Monitoring
Bhat, G., Gupta, U., Tuncel, Y., Karabacak, F., Ozev, S., & Ogras, U. Y. (2020) · Foundations and Trends® in Electronic Design Automation · doi:10.1561/1000000056 · Cited by 14 · see citing works
A Method of Self-Supervised Denoising and Classification for Sensor-Based Human Activity Recognition
Lu, L., & Deng, T. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3323314 · Cited by 10 · see citing works
Yamane, T., Kimura, M., & Morita, M. (2025) · Sensors · doi:10.3390/s25123780 · Cited by 13 · see citing works
Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition
Thapa, K., Seo, Y., Yang, S., & Kim, K. (2023) · Sensors · doi:10.3390/s23020683 · Cited by 11 · see citing works
Rivera, P., Añazco, E. V., & Kim, T. (2020) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421520066 · Cited by 10 · see citing works
Xia, M. Y., Que, S., Liu, N., Wang, Q., & Li, T. (2025) · Electronics · doi:10.3390/electronics14081594 · Cited by 9 · see citing works
Self-citationEnhanced Prediction Model for Human Activity Using an End-to-End Approach
Park, S., Lee, H. O., Hwang, Y. M., Ko, S., & Lee, B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3223674 · Cited by 9 · see citing works
Ge, W., Mou, G., Agu, E., & Lee, K. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631444 · Cited by 9 · see citing works
Chang, X., Li, W., & Zomaya, A. Y. (2022) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3538637.3538840 · Cited by 9 · see citing works
An enhanced ResNet deep learning method for multimodal signal-based locomotion intention recognition
Sun, H., Gu, X., Zhang, Y., Sun, F., Zhang, S., Wang, D., & Yu, H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107254 · Cited by 9 · see citing works
Self-citationAn Optimal Feature Selection Method for Human Activity Recognition Using Multimodal Sensory Data
Haider, T., Khan, M. H., & Farid, M. S. (2024) · Information · doi:10.3390/info15100593 · Cited by 9 · see citing works
Türkmen, G., & Sezen, A. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.329 · Cited by 7 · see citing works
Multi-Dataset Human Activity Recognition: Leveraging Fusion for Enhanced Performance
Alanazi, M., Alsharif, B., Altaher, A. S., Altaher, A., & Ilyas, M. (2023) · IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) · doi:10.1109/honet59747.2023.10374662 · Cited by 7 · see citing works
Facilitated and Enhanced Human Activity Recognition via Semi-supervised LightGBM
Zhang, Y., Zhao, X., & Li, Z. (2020) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps50303.2020.9367452 · Cited by 7 · see citing works
Self-citationAnjali, A., Dai, H., Kumar, J., & Pandey, O. J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3323328 · Cited by 6 · see citing works
Ige, A. O., & Noor, M. H. M. (2022) · International Conference on Information and Communications Technology (ICOIACT) · doi:10.1109/icoiact55506.2022.9972095 · Cited by 6 · see citing works
VL-GAN: A generative classification approach for fingerprint-based indoor localization
Mallik, M., Chakraborty, S., Sasidhar, K., & Chowdhury, C. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128400 · Cited by 5 · see citing works
Marhraoui, Y. E., Bounhar, O., Boukallel, M., Anastassova, M., Bouilland, S., & Ammi, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3519225 · Cited by 7 · see citing works
Applying MLP-Mixer and gMLP to Human Activity Recognition
Miyoshi, T., Koshino, M., & Nambo, H. (2025) · Sensors · doi:10.3390/s25020311 · Cited by 5 · see citing works
Deep Learning Algorithms for Human Activity Recognition in Manual Material Handling Tasks
Bassani, G., Avizzano, C. A., & Filippeschi, A. (2025) · Sensors · doi:10.3390/s25216705 · Cited by 5 · see citing works
Chadha, J., Jain, A., Kumar, Y., & Modi, N. (2024) · SN Computer Science · doi:10.1007/s42979-024-03300-7 · Cited by 5 · see citing works
Semantically Encoding Activity Labels for Context-Aware Human Activity Recognition
Ge, W., Mou, G., Agu, E., & Lee, K. (2025) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom64205.2025.00039 · Cited by 4 · see citing works
Zhu, M., Shia, B., Su, M., & Liu, J. (2024) · Mathematics · doi:10.3390/math12101582 · Cited by 4 · see citing works
Thomas, A. L., & Judith, J. E. (2024) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct61902.2024.10672919 · Cited by 4 · see citing works
Transition Model–driven Unsupervised Localization Framework Based on Crowd-sensed Trajectory Data
Shen, X., Li, C., Chen, W., Wang, Y., & Ge, Q. (2022) · ACM Transactions on Sensor Networks · doi:10.1145/3499425 · Cited by 4 · see citing works
Guo, W., Chen, W., & Chang, C.-H. (2025) · Natural Hazards · doi:10.1007/s11069-025-07187-2 · Cited by 3 · see citing works
Smartphone based Human Activity Recognition using CNNs and Autoencoder Features
Mitra, S., & Kanungoe, P. (2023) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei56765.2023.10126051 · Cited by 3 · see citing works
One-D Convolution Neural Network Models for Human Activity Recognition using mHealth Datasets
Yogesh, K. M., Arpitha, S., & Gad, I. (2023) · International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) · doi:10.1145/3616901.3616933 · Cited by 2 · see citing works
A Comprehensive Review of Deep Learning for Activity Recognition
Thakur, D., & Fortino, G. (2024) · Internet of things · doi:10.1007/978-3-031-60027-2_4 · Cited by 2 · see citing works
Guo, W., Chen, W., & Chang, C.-H. (2025) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-025-03033-w · Cited by 2 · see citing works
Rachmadi, R. R., Sudarsono, A., & Santoso, T. B. (2021) · Jurnal Komputer Terapan · doi:10.35143/jkt.v7i2.5164 · Cited by 2 · see citing works
Mrinali, A., & Gupta, P. (2025) · Journal of Optics · doi:10.1007/s12596-025-02753-0 · Cited by 1 · see citing works
A two-phase hybrid clustering framework exploring transitional activities in HAR
Woo, M., Harby, A. A., Zulkernine, F. H., & Abdulsalam, H. M. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00503-6 · Cited by 1 · see citing works
Smetanin, A., Dukhanov, A. V., Юрьевич, Г. М., & Gudov, E. V. (2026) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2026-26-1-104-115 · Cited by 1 · see citing works
Takeda, N., Legaspi, R., Nishimura, Y., Ikeda, K., Minamikawa, A., Plötz, T., & Chernova, S. (2024) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-230429 · Cited by 1 · see citing works
Greeting Gesture Analysis Using Boosting Algorithms and SHAP
Wibowo, A. W., Sato-Shimokawara, E., & Kurnianingsih, K. (2024) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scisisis61014.2024.10759915 · Cited by 1 · see citing works
An Autoencoder Framework for Few-Shot Human Activity Recognition with Sensor Data
Han, Z., & He, M. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2468-3_16 · Cited by 1 · see citing works
Kojima, R., Legaspi, R., Yoshihara, K., & Wada, S. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-8269-1_3 · Cited by 1 · see citing works
Potortì, F., Torres-Sospedra, J., Quezada-Gaibor, D., Jiménez, A. R., Seco, F. A., Pérez-Navarro, A., Ortiz, M., Zhu, N., & Al., E. (2021) · UCL Discovery (University College London) · doi:10.5281/zenodo.4948278 · Cited by 1 · see citing works
Unsupervised learning approaches for non-stationary data streams
Garcia, K. D. (2021) · doi:10.3990/1.9789036551601
DAP PLXENT#x1D107; : Develop pair-Authentication Protocol with DAP
Maeda, S., Okano, M., Nishigaki, M., & Ohki, T. (2024) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3640471.3680449
Contrastive Learning with Auxiliary User Detection for Identifying Activities
Ge, W., Mou, G., Agu, E., & Lee, K. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00190
Advanced Anti-Lost System for Indoor Environments using Smart Devices
Guo, S. H., Li, M., & Li, Y. (2025) · World Conference on Computing and Communication Technologies (WCCCT) · doi:10.1109/wccct65447.2025.11027944
Mao, J., Jiang, Y., ZHANG, X., & Xu, J. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3664558
Wang, Q., Ma, J., Fu, M., Wang, J., Bai, Y. B., Jiang, Z., & Li, H. (2026) · iScience · doi:10.1016/j.isci.2026.115252
Self-citationIntuitive Multi‐Scale Visual Feature Fusion for Automated Classification of Human Activity
Gautam, V., Kaur, G., SINHA, A., Verma, A. K., & Nag, A. (2026) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/cps2.70054
Ghazi, M. E., & Aknin, N. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21806-5
- Authors
- Gao, X., Luo, H., Wang, Q., Zhao, F., Ye, L., & Zhang, Y.
- Affiliations
- Chinese Academy of Sciences (CN)
- Beijing University of Posts and Telecommunications (CN)
- Beijing Information Science & Technology University (CN)
- Corresponding author
- Luo, Haiyong
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-02-23
- Volume
- 19
- Issue
- 4
- Pages
- 947
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6412893
- OpenAlex
- W2916712302
- Cited by
- 92
- References
- 52
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 16 · 2023: 13 · 2022: 12 · 2021: 17 · 2020: 15 · 2019: 1
- FWCI
- 5.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program (2018YFB0505200)
- National Natural Science Foundation of China (61872046)
- BUPT Excellent Ph.D. Students Foundation (CX2018102)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, LightGBM, unsupervised feature extraction, sensor fusion, wearable sensors, indoor positioning, medical monitoring
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
