Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

Moya Rueda, Fernando; Grzeszick, René; Fink, Gernot A.; Feldhorst, Sascha; Ten Hompel, Michael

doi:10.3390/informatics5020026

Cited by 201Context-Aware Activity Recognition SystemsHuman Pose and Action RecognitionTime Series Analysis and Forecasting

Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

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Moya Rueda, Fernando, Grzeszick, René, Fink, Gernot A., Feldhorst, Sascha, Ten Hompel, Michael·2018·Informatics

DOI 10.3390/informatics5020026

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Abstract

Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs).

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A majority of CNNs consist of five to nine layers, usually including two to three convolutional layers, two to three max-pooling layers, followed by one to two fully connected layers before the output layer.
“A majority of the CNNs consist of five to nine layers [23,63,64,113,114,165,166,167,168], usually including two to three convolutional layers, two to three max-pooling layers, followed by one to two fully connected layers before feeding the feature representation into the output layer (softmax layer in most cases).”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [114]

Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
Convolutional Two-Stream Network Fusion for Video Action Recognition
Feichtenhofer, C., Pinz, A. J., & Zisserman, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.213 · Cited by 2,842 · see citing works
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 10× · see citing works
“This architecture was introduced in a previous work [11] based on the CNN and deepConvLSTM presented in [4,5,9].”
BackgroundMethodInfluential
A Survey on Human Activity Recognition using Wearable Sensors
Lara, O. D., & Labrador, M. A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.110112.00192 · Cited by 2,662 · see citing works
Convolutional networks and applications in vision
LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213 · see citing works
A tutorial on human activity recognition using body-worn inertial sensors
Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668 · see citing works
Introducing 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 2× · see citing works
“The step size allows a 78% overlapping as in [9,31].”
Background
Proceedings of the 24th International Conference on Neural Information Processing Systems
Shawe-Taylor, J. S., Zemel, R. S., Bartlett, P. L., Pereira, F. B., & Weinberger, K. Q. (2011) · Cited by 1,184 · see citing works
Untitled work
(2015) · Cited by 989 · see citing works
Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J. (2014) · Proceedings of the 6th International Conference on Mobile Computing, Applications and Services · doi:10.4108/icst.mobicase.2014.257786 · Cited by 876 · see citing works
Collecting complex activity datasets in highly rich networked sensor environments
Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., et al. (2010) · 2010 Seventh International Conference on Networked Sensing Systems (INSS) · doi:10.1109/inss.2010.5573462 · Cited by 788 · see citing works
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
Fukushima, K., & Miyake, S. (1982) · Pattern Recognition · doi:10.1016/0031-3203(82)90024-3 · Cited by 746 · see citing works
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S. T., Tröster, G., Millán, J. D. R., & Roggen, D. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.12.014 · Cited by 742 · cited 2× · see citing works
“Sessions ADL3 of participants 2 and 3 are used as validation set, and sessions ADL4 and ADL5 of participants 2 and 3 as testing set, following [3,5,30].”
BackgroundMethod
Permutation Tests for Studying Classifier Performance
Ojala, M., & Garriga, G. C. (2009) · Proceedings · doi:10.1109/icdm.2009.108 · Cited by 578 · see citing works
Word Spotting and Recognition with Embedded Attributes
Almazán, J., Gordo, A., Fornés, A., & Valveny, E. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2339814 · Cited by 514 · see citing works
A deep learning approach to on-node sensor data analytics for mobile or wearable devices
Ravi, D., Wong, C., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2633287 · Cited by 439 · see citing works
Permutation Tests for Studying Classifier Performance
Ojala, M., & Garriga, G. C. (2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1859913 · Cited by 343 · see citing works
Analyzing features for activity recognition
Huỳnh, T., & Schiele, B. (2005) · Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies · doi:10.1145/1107548.1107591 · Cited by 336 · see citing works
Spectral Representations for Convolutional Neural NetworksPreprint
Rippel, O., Snoek, J., & Adams, R. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03767 · Cited by 276 · see citing works
Deep learning for human activity recognition: A resource efficient implementation on low-power devices
Ravi, D., Wong, C., Lo, B., & Yang, G.-Z. (2016) · 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2016.7516235 · Cited by 269 · see citing works
PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents
Sudholt, S., & Fink, G. A. (2016) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2016.0060 · Cited by 243 · see citing works
Self-citation
Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors
Ronao, C. A., & Cho, S.-B. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-26561-2_6 · Cited by 176 · see citing works
Efficient dense labelling of human activity sequences from wearables using fully convolutional networks
Yao, R., Lin, G., Shi, Q., & Ranasinghe, D. C. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.12.024 · Cited by 108 · see citing works
Deep Neural Network based Human Activity Recognition for the Order Picking Process
Grzeszick, R., Lenk, J. M., Rueda, F. M., Fink, G. A., Feldhorst, S., & ten Hompel, M. (2017) · Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction · doi:10.1145/3134230.3134231 · Cited by 93 · see citing works
Self-citation
In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models
Ordóñez, F. J., Englebienne, G., de Toledo, P., van Kasteren, T. L. M., Sanchis, A., & Kröse, B. (2014) · IEEE Pervasive Computing · doi:10.1109/mprv.2014.52 · Cited by 48 · see citing works
Motion Classification for Analyzing the Order Picking Process using Mobile Sensors - General Concepts, Case Studies and Empirical Evaluation
Feldhorst, S., Masoudenijad, M., ten Hompel, M., & Fink, G. A. (2016) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0005828407060713 · Cited by 22 · see citing works
Self-citation
Limited-Memory Warping LCSS for Real-Time Low-Power Pattern Recognition in Wireless Nodes
Roggen, D., Cuspinera, L. P., Pombo, G., Ali, F. H., & Nguyen-Dinh, L.-V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-15582-1_10 · Cited by 20 · see citing works
Analysing acoustic model changes for active learning in automatic speech recognition
Wu, C., Ng, R. W. M., Saz, Ó., & Hain, T. (2017) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2017.7965609 · Cited by 3 · see citing works

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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
“A majority of the CNNs consist of 5 to 9 layers [67, 91, 193, 232, 272, 300, 313], [130], [96], usually including 2 to 3 convolutional layers, 2 to 3 max pooling layers, followed by 1 to 2 fully connected layers before feeding the feature representation into the output layer (softmax layer in most cases).”
BackgroundMethodInfluential
A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications
Pareek, P., & Thakkar, A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09904-8 · Cited by 466 · see citing works
A CNN-LSTM Approach to Human Activity Recognition
Mutegeki, R., & Han, D. S. (2020) · 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic48513.2020.9065078 · Cited by 452 · see citing works
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Foumani, N. M., Miller, L., Tan, C. W., Webb, G. I., Forestier, G., & Salehi, M. (2024) · ACM Computing Surveys · doi:10.1145/3649448 · Cited by 296 · see citing works
Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System
Serpush, F., Menhaj, M. B., Masoumi, B., & Karasfi, B. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1391906 · Cited by 174 · see citing works
Human Activity Recognition: Review, Taxonomy and Open Challenges
Arshad, M. H., Bilal, M., & Gani, A. (2022) · Sensors · doi:10.3390/s22176463 · Cited by 162 · see citing works
Feature learning for Human Activity Recognition using Convolutional Neural Networks
Cruciani, F., Vafeiadis, A., Nugent, C., Cleland, I., McCullagh, P. J., Votis, K., Giakoumis, D., Tzovaras, D. K., Chen, L., & Hamzaoui, R. (2020) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-020-00026-2 · Cited by 145 · 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
Design and Implementation of a Convolutional Neural Network on an Edge Computing Smartphone for Human Activity Recognition
Zebin, T., Scully, P., Peek, N. B., Casson, A. J., & Ozanyan, K. B. (2019) · IEEE Access · doi:10.1109/access.2019.2941836 · Cited by 107 · see citing works
ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition
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
LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes
Niemann, F., Reining, C., Rueda, F. M., Nair, N. R., Steffens, J. A., Fink, G. A., & ten Hompel, M. (2020) · Sensors · doi:10.3390/s20154083 · Cited by 98 · see citing works
Self-citation
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
Workplace Well-Being in Industry 5.0: A Worker-Centered Systematic Review
Antonaci, F. G., Olivetti, E. C., Marcolin, F., Jiménez, I. A. C., Eynard, B., Vezzetti, E., & Moos, S. (2024) · Sensors · doi:10.3390/s24175473 · Cited by 92 · see citing works
Human activity recognition based on smartphone and wearable sensors using multiscale DCNN ensemble
Sena, J., Barreto, J., Caetano, C., Cramer, G., & Schwartz, W. R. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.04.151 · Cited by 81 · see citing works
Human activity recognition from multiple sensors data using deep CNNs
Kaya, Y., & Topuz, E. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15830-y · Cited by 81 · see citing works
Real-time detection of freezing of gait in Parkinson’s disease using multi-head convolutional neural networks and a single inertial sensor
Borzì, L., Sigcha, L., Rodríguez-Martín, D., & Olmo, G. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102459 · Cited by 74 · see citing works
Human Activity Recognition for Production and Logistics—A Systematic Literature Review
Reining, C., Niemann, F., Rueda, F. M., Fink, G. A., & ten Hompel, M. (2019) · Information · doi:10.3390/info10080245 · Cited by 70 · see citing works
Self-citation
A Survey of Deep Learning Based Models for Human Activity Recognition
Khan, N. S., & Ghani, M. S. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08525-w · Cited by 62 · see citing works
An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions
Ravi Raj, K. G., & Kos, A. (2025) · Sensors · doi:10.3390/s25041033 · Cited by 62 · see citing works
RNN-based deep learning for physical activity recognition using smartwatch sensors: A case study of simple and complex activity recognition
Mekruksavanich, S., & Jitpattanakul, A. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022265 · Cited by 56 · see citing works
Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
Pavliuk, O., Mishchuk, M., & Strauß, C. (2023) · Algorithms · doi:10.3390/a16020077 · Cited by 56 · see citing works
A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring
Elfouly, T., & Alouani, A. T. (2025) · Electronics · doi:10.3390/electronics14173443 · Cited by 56 · see citing works
Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology
Livieris, I. E. (2018) · Informatics · doi:10.3390/informatics6010001 · Cited by 51 · see citing works
Artificial Intelligence for the Prediction of Tensile Properties by Using Microstructural Parameters in High Strength Steels
Jung, I. D., Shin, D. S., Kim, D., Lee, J., Lee, M. S., Son, H. J., Reddy, N. S., Kim, M., Moon, S. K., Kim, K. T., et al. (2020) · Materialia · doi:10.1016/j.mtla.2020.100699 · Cited by 51 · see citing works
Efficacy of Imbalanced Data Handling Methods on Deep Learning for Smart Homes Environments
Hamad, R. A., Kimura, M., & Lundström, J. (2020) · SN Computer Science · doi:10.1007/s42979-020-00211-1 · Cited by 51 · see citing works
A review of video-based human activity recognition: theory, methods and applications
Bukht, T. F. N., Rahman, H., Shaheen, M., Algarni, A. M., Almujally, N. A., & Jalal, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19711-w · Cited by 50 · see citing works
Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
Hamad, R. A., Yang, L., Woo, W. L., & Wei, B. W. (2020) · Applied Sciences · doi:10.3390/app10155293 · Cited by 49 · see citing works
Applications of Digital Technologies in Sustainable Logistics and Supply Chain Management
Wei, F., Alias, C., & Noche, B. (2019) · Springer eBooks · doi:10.1007/978-3-319-98467-4_11 · Cited by 46 · see citing works
Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition
Kreuzer, D., & Münz, M. (2021) · Sensors · doi:10.3390/s21030789 · Cited by 46 · see citing works
Application of human activity/action recognition: a review
sedaghati, N., Ardebili, S., & Ghaffari, A. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20576-2 · Cited by 45 · see citing works
Machine learning algorithms can classify outdoor terrain types during running using accelerometry data
Dixon, P. C., Schütte, K. H., Vanwanseele, B., Jacobs, J. V., Dennerlein, J. T., Schiffman, J. M., Fournier, P.-A., & Hu, B. (2019) · Gait & Posture · doi:10.1016/j.gaitpost.2019.09.005 · Cited by 45 · see citing works
Wearables-based multi-task gait and activity segmentation using recurrent neural networks
Martindale, C. F., Christlein, V., Klumpp, P., & Eskofier, B. M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.08.079 · Cited by 44 · see citing works
A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors
Suglia, V., Palazzo, L., Bevilacqua, V., Passantino, A., Pagano, G., & D’Addio, G. (2024) · Sensors · doi:10.3390/s24072199 · Cited by 43 · see citing works
Synthetic Sensor Data for Human Activity Recognition
Alharbi, F., Ouarbya, L., & Ward, J. A. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206624 · Cited by 40 · see citing works
Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone
Nan, Y., Lovell, N. H., Redmond, S. J., Wang, K., Delbaere, K., & van Schooten, K. S. (2020) · Sensors · doi:10.3390/s20247195 · Cited by 39 · see citing works
Sensor-data augmentation for human activity recognition with time-warping and data masking
Jeong, C. Y., Shin, H. C., & Kim, M. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10600-0 · Cited by 38 · see citing works
Self-attention deep ConvLSTM with sparse-learned channel dependencies for wearable sensor-based human activity recognition
Ullah, S., Pirahandeh, M., & Kim, D. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127157 · Cited by 34 · see citing works
Viewmaker Networks: Learning Views for Unsupervised Representation LearningPreprint
Tamkin, A., Wu, M., & Goodman, N. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07432 · Cited by 33 · see citing works
Multi-Modal Deep Learning for Assessing Surgeon Technical Skill
Kasa, K., Burns, D., Goldenberg, M. G., Selim, O., Whyne, C. M., & Hardisty, M. (2022) · Sensors · doi:10.3390/s22197328 · Cited by 32 · see citing works
Human activity recognition adapted to the type of movement
Gil-Martín, M., San-Segundo, R., Fernández-Martínez, F., & de Córdoba, R. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106822 · Cited by 32 · see citing works
Multimodal sensor fusion models for real-time exercise repetition counting with IMU sensors and respiration data
Lee, S., Lim, Y., & Lim, K. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102153 · Cited by 30 · see citing works
Semisupervised Generative Adversarial Networks With Temporal Convolutions for Human Activity Recognition
Zilelioglu, H., Khodabandelou, G., Chibani, A., & Amirat, Y. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3267243 · Cited by 30 · see citing works
A deep local-temporal architecture with attention for lightweight human activity recognition
Ige, A. O., & Noor, M. H. M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110954 · Cited by 30 · see citing works
PatchHAR: A MLP-Like Architecture for Efficient Activity Recognition Using Wearables
Wang, S., Zhang, L., Wang, X., Huang, W., Wu, H., & Song, A. (2024) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2024.3354261 · Cited by 29 · see citing works
A novel approach for suspicious activity detection with deep learning
Dwivedi, N., Singh, D. K., & Kushwaha, D. S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14445-7 · Cited by 29 · see citing works
Suspicious Human Activity Recognition using 2D Pose Estimation and Convolutional Neural Network
Dileep, A. S., Nabilah, S. S., Sreeju, S. A., Farhana, K. S., & Surumy, S. (2022) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet54241.2022.9767152 · Cited by 28 · see citing works
A unified generative model using generative adversarial network for activity recognition
Chan, M. H., & Noor, M. H. M. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02548-0 · Cited by 27 · see citing works
Automatic Smile and Frown Recognition with Kinetic Earables
Lee, S., Min, C., Montanari, A., Mathur, A., Chang, Y., Song, J., & Kawsar, F. (2019) · Augmented Human International Conference · doi:10.1145/3311823.3311869 · Cited by 26 · see citing works
Constrained Evolutionary Piecemeal Training to Design Convolutional Neural Networks
Sapra, D., & Pimentel, A. D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-55789-8_61 · Cited by 25 · see citing works
Deep Learning for Intake Gesture Detection From Wrist-Worn Inertial Sensors: The Effects of Data Preprocessing, Sensor Modalities, and Sensor Positions
Heydarian, H., Rouast, P. V., Adam, M. T. P., Burrows, T. L., Collins, C. E., & Rollo, M. E. (2020) · IEEE Access · doi:10.1109/access.2020.3022042 · Cited by 25 · see citing works
Achieving More with Less: A Lightweight Deep Learning Solution for Advanced Human Activity Recognition (HAR)
AlMuhaideb, S., AlAbdulkarim, L., AlShahrani, D. M., AlDhubaib, H., & AlSadoun, D. E. (2024) · Sensors · doi:10.3390/s24165436 · Cited by 24 · see citing works
An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning
Jeong, C. Y., & Kim, M. (2019) · Sensors · doi:10.3390/s19173688 · Cited by 24 · see citing works
Deep Learning for Classifying Physical Activities from Accelerometer Data
Nunavath, V., Johansen, S., Johannessen, T. S., Jiao, L., Hansen, B. H., Berntsen, S., & Goodwin, M. (2021) · Sensors · doi:10.3390/s21165564 · Cited by 24 · see citing works
Human Activity Recognition Based on Acceleration Data From Smartphones Using HMMs
Iloga, S., Bordat, A., Kernec, J. L., & Romain, O. (2021) · IEEE Access · doi:10.1109/access.2021.3117336 · Cited by 24 · see citing works
Human Activity Recognition Using 1D Convolutional Neural Network
Banjarey, K., Sahu, S. P., & Dewangan, D. K. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-5157-1_54 · Cited by 24 · see citing works
Human Complex Activity Recognition With Sensor Data Using Multiple Features
Huan, R., Jiang, C., Ge, L., Shu, J., Zhan, Z., Chen, P., Chi, K., & Liang, R. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3130913 · Cited by 25 · see citing works
Multiple inputs modeling of hybrid convolutional neural networks for human activity recognition
Lai, Y.-C., Kan, Y., Hsu, K.-C., & Lin, H.-C. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106034 · Cited by 24 · see citing works
Deep Learning Based Real-time Daily Human Activity Recognition and Its Implementation in a Smartphone
Alemayoh, T. T., Lee, J. H., & Okamoto, S. (2019) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/urai.2019.8768791 · Cited by 24 · see citing works
GRU with Dual Attentions for Sensor-Based Human Activity Recognition
Pan, J., Hu, Z., Yin, S., & Li, M. (2022) · Electronics · doi:10.3390/electronics11111797 · Cited by 24 · see citing works
Human Activity and Gesture Recognition: A Review
Saini, R., & Maan, V. (2020) · International Conference Emerging Trends Communication, Control and Computing · doi:10.1109/iconc345789.2020.9117535 · Cited by 23 · see citing works
Feature-Representation Transfer Learning for Human Activity Recognition
Mutegeki, R., & Han, D. S. (2019) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc46691.2019.8939979 · Cited by 22 · see citing works
State-of-the-art survey on activity recognition and classification using smartphones and wearable sensors
Chaurasia, S. K., & Reddy, S. R. N. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11410-0 · Cited by 22 · see citing works
Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures
Spilz, A., & Münz, M. (2022) · Sensors · doi:10.3390/s23010005 · Cited by 22 · see citing works
Acceleration-based Human Activity Recognition of Packaging Tasks Using Motif-guided Attention Networks
Morales, J., Yoshimura, N., Xia, Q., Wada, A., Namioka, Y., & Maekawa, T. (2022) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom53586.2022.9762388 · Cited by 22 · see citing works
Vision Based Detection and Analysis of Human Activities
Ravipati, A., Kondamuri, R. K., Posonia, A. M., & Mayan, J. A. (2023) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei56765.2023.10125829 · Cited by 20 · see citing works
Quantum-Inspired Support Vector Machines for Human Activity Recognition in Industry 4.0
Agarwal, P., & Alam, M. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-6289-8_24 · Cited by 20 · see citing works
Deep neural networks for wearable sensor-based activity recognition in Parkinson’s disease: investigating generalizability and model complexity
Davidashvilly, S., Cardei, M. A., Hssayeni, M. D., Chi, C., & Ghoraani, B. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01214-2 · Cited by 19 · see citing works
Wearable sensors based on artificial intelligence models for human activity recognition
Alarfaj, M., Madini, A. A., Alsafran, A. S., Farag, M. M., Chtourou, S., Afifi, A., Ahmad, A., Rubayyi, O. A., Harbi, A. A., & Thunaian, M. A. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1424190 · Cited by 17 · see citing works
Attention Mechanism-Based Bidirectional Long Short-Term Memory for Cycling Activity Recognition Using Smartphones
Nguyen, V. S., Kim, H.-S., & Suh, D. (2023) · IEEE Access · doi:10.1109/access.2023.3338137 · Cited by 17 · see citing works
Human Activity Recognition and Prediction: Overview and Research Gaps
Nagpal, D., & Gupta, S. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126458 · Cited by 16 · see citing works
Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach
Zhang, S., Geng, B., Varshney, P. K., & Rangaswamy, M. (2019) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011282 · Cited by 16 · see citing works
A hybrid CNN and BLSTM network for human complex activity recognition with multi-feature fusion
Huan, R., Zhan, Z., Ge, L., Chi, K., Chen, P., & Liang, R. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11363-4 · Cited by 16 · see citing works
ThumbUp: Secure Smartwatch Controller for Smart Homes Using Simple Hand Gestures
Yu, X., Zhou, Z., Zhang, L., & Li, X. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3216927 · Cited by 15 · see citing works
Designing Wireless Sensor Network Data Based Machine Learning Approach for Accurate Human Activity Recognition
Marhoon, H. A., Oudah, A. Y., Hussien, N. A., & Alubady, R. (2025) · Journal of Internet Services and Information Security · doi:10.58346/jisis.2025.i1.025 · Cited by 15 · see citing works
A Customized Convolutional Neural Network Model Integrated With Acceleration-Based Smart Insole Toward Personalized Foot Gesture Recognition
Hua, R., & Wang, Y. (2020) · IEEE Sensors Letters · doi:10.1109/lsens.2020.2984152 · Cited by 15 · see citing works
FuMeAuth: Sensor-Based Continuous Authentication Using Fused Memory-Augmented Transformer Autoencoder
Li, Y., Huang, Y., & Huang, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3394437 · Cited by 14 · see citing works
An intelligent system to detect human suspicious activity using deep neural networks
Ramachandran, S., & Palivela, L. H. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179003 · Cited by 14 · see citing works
1D Convolutional Neural Network with Long Short-Term Memory for Human Activity Recognition
Goh, J. X., Lim, K. M., & Lee, C. P. (2021) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet51634.2021.9573979 · Cited by 14 · see citing works
HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data
Mohamed, A., Lejarza, F., Cahail, S., Claudel, C. G., & Thomaz, E. (2022) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops53856.2022.9767342 · Cited by 13 · see citing works
AReNet: Cascade learning of multibranch convolutional neural networks for human activity recognition
Boudjema, A., Titouna, F., & Titouna, C. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17496-y · Cited by 12 · see citing works
Scenario Based Run-Time Switching for Adaptive CNN-Based Applications at the Edge
Minakova, S., Sapra, D., Stefanov, T. P., & Pimentel, A. D. (2022) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3488718 · Cited by 12 · see citing works
Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications
Dinarević, E. C., Husić, J. B., & Baraković, S. (2019) · Sensors · doi:10.3390/s19235206 · Cited by 12 · see citing works
Benchmarking Annotation Procedures for Multi-channel Time Series HAR Dataset
Avsar, H., Altermann, E., Reining, C., Rueda, F. M., Fink, G. A., & ten Hompel, M. (2021) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops51409.2021.9431062 · Cited by 12 · see citing works
Self-citation
Origin: Enabling On-Device Intelligence for Human Activity Recognition Using Energy Harvesting Wireless Sensor Networks
Mishra, C. S., Sampson, J., Kandemir, M., & Narayanan, V. (2021) · Design, Automation, and Test in Europe · doi:10.23919/date51398.2021.9474017 · Cited by 11 · see citing works
A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition
Lee, S., Lee, D.-W., & Kim, M. S. (2023) · Sensors · doi:10.3390/s23042278 · Cited by 11 · see citing works
MLPs Are All You Need for Human Activity Recognition
Ojiako, K., & Farrahi, K. (2023) · Applied Sciences · doi:10.3390/app132011154 · Cited by 11 · see citing works
Deep Learning Model Reuse and Composition in Knowledge Centric Networking
Sapra, D., & Pimentel, A. D. (2020) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn49398.2020.9209668 · Cited by 11 · see citing works
Intelligent textile sensors coupled with machine learning for athlete physiological monitoring: a review of recent progress
Sun, Z., & Yin, L. (2025) · Sensor Review · doi:10.1108/sr-08-2025-0563 · Cited by 10 · see citing works
Ensuring Computers Understand Manual Operations in Production: Deep-Learning-Based Action Recognition in Industrial Workflows
Jiao, Z., Jia, G., & Cai, Y. (2020) · Applied Sciences · doi:10.3390/app10030966 · Cited by 10 · see citing works
Learning Human Activity From Visual Data Using Deep Learning
Alhersh, T., Stuckenschmidt, H., Rehman, A. U., & Belhouari, S. B. (2021) · IEEE Access · doi:10.1109/access.2021.3099567 · Cited by 10 · see citing works
DRN-SEAM: A deep residual network based on squeeze-and-excitation attention mechanism for motion recognition in education
Hua, X. (2022) · Computer Science and Information Systems · doi:10.2298/csis220322041h · Cited by 9 · see citing works
A deep neural network-based approach for seizure activity recognition of epilepsy sufferers
Khurshid, D., Wahid, F., Ali, S., Gumaei, A., Alzanin, S. M., & Mosleh, M. A. A. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1405848 · Cited by 9 · see citing works
Chances of Interpretable Transfer Learning for Human Activity Recognition in Warehousing
Kirchhof, M., Schmid, L., Reining, C., ten Hompel, M., & Pauly, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87672-2_11 · Cited by 9 · see citing works
Personalized Federated Human Activity Recognition through Semi-supervised Learning and Enhanced Representation
Gao, L., & Konomi, S. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3610739 · Cited by 9 · see citing works
Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks
Otebolaku, A., Enamamu, T., Alfoudi, A. S. D., Ikpehai, A., Marchang, J., & Lee, G. M. (2020) · Sensors · doi:10.3390/s20133803 · Cited by 9 · see citing works
Human Activity Detection Using Pose Net
Duth, P. S., & Poojashree, B. S. (2022) · International Conference on Futuristic Technologies (INCOFT) · doi:10.1109/incoft55651.2022.10094346 · Cited by 9 · see citing works
Classification of Center of Mass Acceleration Patterns in Older People with Knee Osteoarthritis and Fear of Falling
González-Olguín, A., Rodríguez, D. R., Córdoba, F. H., Rebolledo, L. M., Taramasco, C., & Cruz, D. R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191912890 · Cited by 8 · see citing works
Wearable Fall Detection Device for Stroke Warning Based on IoT Technology and Convolutional Neural Network
Duc, P. T., & Toan, V. D. (2025) · Measurement Interdisciplinary Research and Perspectives · doi:10.1080/15366367.2025.2464970 · Cited by 8 · see citing works
Knowledge Distillation for Efficient Human Activity Recognition on Resource-Constrained Smartwatches
Fan, Y., Phaphan, W., Jitpattanakul, A., & Mekruksavanich, S. (2026) · IEEE Access · doi:10.1109/access.2026.3664977 · Cited by 8 · see citing works
A lightweight deep learning with feature weighting for activity recognition
Ige, A. O., & Noor, M. H. M. (2022) · Computational Intelligence · doi:10.1111/coin.12565 · Cited by 8 · see citing works

Modeling Human Activity Recognition with Deep Convolutional Neural Networks and Wearable Sensor Data
Islam, S. M. M., & Talukder, K. H. (2026) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2026.04.06
Convolutional Neural Networks for Human Activity Recognition in Time and Frequency-Domain
Sadouk, L., & Gadi, T. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-91337-7_43 · Cited by 10 · see citing works
Human Activity Recognition by Deep Convolution Neural Networks and Principal Component Analysis
Aljarrah, A. A., & Ali, A. H. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-57835-0_10 · Cited by 8 · see citing works
A survey on human activity recognition using CNN and LSTM
Murthy, R. K., Dhanraj, S., Manjunath, T. N., Achyutha, P. N., Prasad, A. N., & Gangambika, G. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns7.12479 · Cited by 6 · see citing works
Untitled work
(2015) · Cited by 989 · see citing works

Authors: Moya Rueda, F., Grzeszick, R., Fink, G. A., Feldhorst, S., & Ten Hompel, M.
Affiliations: Department of Computer Science, TU Dortmund University, 44227 Dortmund, Germany
TU Dortmund University (DE)
Fraunhofer Institute for Material Flow and Logistics (DE)
Fraunhofer IML, 44227 Dortmund, Germany
ORCID: Moya Rueda, Fernando
Grzeszick, René
Fink, Gernot A.
Venue: Informatics
Publisher: MDPI AG
Published: 2018-05-25
Volume: 5
Issue: 2
Pages: 26
ISSN: 2227-9709 (ISSN-L)
DOI: 10.3390/informatics5020026
OpenAlex: W2803675191
Cited by: 196
References: 30
Citations by year: 2026: 12 · 2025: 16 · 2024: 27 · 2023: 30 · 2022: 35 · 2021: 31 · 2020: 29 · 2019: 14 · 2018: 2
FWCI: 9.51
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Informatics (publishedVersion, cc-by)
Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (cc-by)
Funders: Deutsche Forschungsgemeinschaft (Fi799/10-1)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords: human activity recognition, wearable sensors, convolutional neural networks, activities of daily living, multichannel time series, wearable devices

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Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

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