Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer.

Human activity recognition (HAR) is an important component in health-care systems. For example, it can enable context-aware applications such as elderly care and patient monitoring. Relying on a set of training data, supervised machine learning algorithms form the core intelligence of most existing HAR systems.

Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach.

Recent advancements in mobile devices, data analysis, and wearable sensors render the capability of in-place health monitoring. Supervised machine learning algorithms, the core intelligence of these systems, learn from labeled training data. However, labeling vast amount of data is time-consuming and expensive.