Digging deeper: towards a better understanding of transfer learning for human activity recognition
暂无分享,去创建一个
[1] Didier Stricker,et al. Introducing a New Benchmarked Dataset for Activity Monitoring , 2012, 2012 16th International Symposium on Wearable Computers.
[2] Qiang Yang,et al. Cross-domain activity recognition via transfer learning , 2011, Pervasive Mob. Comput..
[3] Martin Gjoreski,et al. Cross-dataset deep transfer learning for activity recognition , 2019, UbiComp/ISWC Adjunct.
[4] Kristof Van Laerhoven,et al. Using Wrist-Worn Activity Recognition for Basketball Game Analysis , 2018, iWOAR.
[5] Yiqiang Chen,et al. OCEAN: a new opportunistic computing model for wearable activity recognition , 2016, UbiComp Adjunct.
[6] Florian Huber,et al. Mcfly: Automated deep learning on time series , 2020, SoftwareX.
[7] Xiaohui Peng,et al. Deep Learning for Sensor-based Activity Recognition: A Survey , 2017, Pattern Recognit. Lett..
[8] Daniel Roggen,et al. Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations , 2016, SEMWEB.
[9] Matjaz Gams,et al. Context-based ensemble method for human energy expenditure estimation , 2015, Appl. Soft Comput..
[10] Niall Twomey,et al. Active transfer learning for activity recognition , 2016, ESANN.
[11] Richard Walker,et al. PD Disease State Assessment in Naturalistic Environments Using Deep Learning , 2015, AAAI.
[12] Ling Bao,et al. Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.
[13] Thomas Plötz,et al. Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables , 2016, IJCAI.
[14] Bertram Taetz,et al. IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning , 2018, Sensors.
[15] Jani Bizjak,et al. Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors , 2020, Inf. Fusion.
[16] Luca Benini,et al. Network-Level Power-Performance Trade-Off in Wearable Activity Recognition: A Dynamic Sensor Selection Approach , 2012, TECS.
[17] Sajal K. Das,et al. A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).
[18] Netzahualcóyotl Hernández,et al. Literature Review on Transfer Learning for Human Activity Recognition Using Mobile and Wearable Devices with Environmental Technology , 2020, SN Computer Science.
[19] Paul Lukowicz,et al. Sensor Placement Variations in Wearable Activity Recognition , 2014, IEEE Pervasive Computing.
[20] Paul Lukowicz,et al. Collecting complex activity datasets in highly rich networked sensor environments , 2010, 2010 Seventh International Conference on Networked Sensing Systems (INSS).
[21] Vincent S. Tseng,et al. Transfer Learning on High Variety Domains for Activity Recognition , 2015, ASE BD&SI.
[22] Daniel Roggen,et al. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.
[23] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[24] Kimiaki Shirahama,et al. Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification , 2020, Sensors.