Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

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 …

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 …

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 …

Abstract In practice, Wearable Human Activity Recognition (WHAR) models usually face
performance degradation on the new user due to user variance. Unsupervised domain …

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 …