RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

ImageNet classification with deep convolutional neural networks

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Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

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Deep learning for sensor-based activity recognition: A survey

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LSTM-CNN Architecture for Human Activity Recognition

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The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

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UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

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UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

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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

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Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314 · cited 1× · see citing works

Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

Kanjo, E., Younis, E. M. G., & Ang, C. S. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.001 · Cited by 293 · see citing works

Understanding and improving recurrent networks for human activity recognition by continuous attention

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PD Disease State Assessment in Naturalistic Environments Using Deep Learning

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On attention models for human activity recognition

Murahari, V. S., & Plötz, T. (2018) · Proceedings of the 2018 ACM International Symposium on Wearable Computers · doi:10.1145/3267242.3267287 · Cited by 157 · see citing works

A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data

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Protecting Sensory Data against Sensitive Inferences

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Towards multimodal deep learning for activity recognition on mobile devices

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Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations

Morales, F. J. O., & Roggen, D. (2016) · Proceedings of the 2016 ACM International Symposium on Wearable Computers · doi:10.1145/2971763.2971764 · Cited by 136 · see citing works

A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities

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Deep Neural Network based Human Activity Recognition for the Order Picking Process

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SensoryGANs: An Effective Generative Adversarial Framework for Sensor-based Human Activity Recognition

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Toward a Wearable System for Predicting Freezing of Gait in People Affected by Parkinson's Disease

Demrozi, F., Bacchin, R., Tamburin, S., Cristani, M., & Pravadelli, G. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2952618 · Cited by 86 · see citing works

Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Fu, Z., He, X., Wang, E., Huo, J., Huang, J., & Wu, D. (2021) · Sensors · doi:10.3390/s21030885 · Cited by 79 · see citing works

Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices

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