Don't freeze: Finetune encoders for better Self-Supervised HAR (2307.01168v1)
Abstract: Recently self-supervised learning has been proposed in the field of human activity recognition as a solution to the labelled data availability problem. The idea being that by using pretext tasks such as reconstruction or contrastive predictive coding, useful representations can be learned that then can be used for classification. Those approaches follow the pretrain, freeze and fine-tune procedure. In this paper we will show how a simple change - not freezing the representation - leads to substantial performance gains across pretext tasks. The improvement was found in all four investigated datasets and across all four pretext tasks and is inversely proportional to amount of labelled data. Moreover the effect is present whether the pretext task is carried on the Capture24 dataset or directly in unlabelled data of the target dataset.
- Capture-24: Activity tracker dataset for human activity recognition.
- Translating videos into synthetic training data for wearable sensor-based activity recognition systems using residual deep convolutional networks. Applied Sciences 11, 7 (2021), 3094.
- On the Role of Features in Human Activity Recognition. In Proceedings of the 2019 ACM International Symposium on Wearable Computers (London, United Kingdom) (ISWC ’19). Association for Computing Machinery, New York, NY, USA, 78–88. https://doi.org/10.1145/3341163.3347727
- Contrastive Predictive Coding for Human Activity Recognition. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 2, Article 65 (jun 2021), 26 pages. https://doi.org/10.1145/3463506
- Assessing the State of Self-Supervised Human Activity Recognition using Wearables. arXiv:2202.12938 [eess.SP]
- Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition. In 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom). 232–241. https://doi.org/10.1109/PERCOM56429.2023.10099197
- MyoGym: Introducing an Open Gym Data Set for Activity Recognition Collected Using Myo Armband. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (Maui, Hawaii) (UbiComp ’17). Association for Computing Machinery, New York, NY, USA, 537–546. https://doi.org/10.1145/3123024.3124400
- IMUTube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 3 (2020), 1–29.
- Protecting Sensory Data against Sensitive Inferences. In Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems (Porto, Portugal) (W-P2DS’18). Association for Computing Machinery, New York, NY, USA, Article 2, 6 pages. https://doi.org/10.1145/3195258.3195260
- MHEALTH Dataset. UCI Machine Learning Repository. DOI: 10.24432/C5TW22.
- What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks? arXiv:2202.05998 [cs.LG]
- Attila Reiss. 2012. PAMAP2 Physical Activity Monitoring. UCI Machine Learning Repository. DOI: 10.24432/C5NW2H.
- Multi-task Self-Supervised Learning for Human Activity Detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (jun 2019), 1–30. https://doi.org/10.1145/3328932
- SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (mar 2021), 1–30. https://doi.org/10.1145/3448112
- Exploring Contrastive Learning in Human Activity Recognition for Healthcare. arXiv:2011.11542 [cs.LG]
- Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data. arXiv:2206.02909 [eess.SP]
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
