A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is …
Y Yin, L Xie, Z Jiang, F Xiao, J Cao… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Due to the ever-growing powers in sensing, computing, communicating and storing, mobile devices (eg, smartphone, smartwatch, smart glasses) become ubiquitous and an …
Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for various mobile sensing applications, including human activity recognition, human-computer interaction …
The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR) …
Unsupervised domain adaptation methods aim at generalizing well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are …
Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in …
H Qian, T Tian, C Miao - Proceedings of the 28th ACM SIGKDD …, 2022 - dl.acm.org
Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large …
R Hu, L Chen, S Miao, X Tang - … of the AAAI Conference on artificial …, 2023 - ojs.aaai.org
Abstract In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain …
Y Li, X Wang, L An - Proceedings of the ACM on Interactive, Mobile …, 2023 - dl.acm.org
Currently, federated learning (FL) can enable users to collaboratively train a global model while protecting the privacy of user data, which has been applied to human activity …