Deep Learning for Human Activity Recognition
Ming Zeng is a Ph.D. student at Carnegie Mellon University. His research interests include machine learning, deep learning, human behavior modeling, and natural language processing. His work is primarily focused on deep neural networks (DNNs) for human activity recognition. The deep learning models used in his work mainly include convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) with different architectures according to different applications. In addition, instead of treating the deep neural network as a black box, he is trying to interpret the features extracted from DNNs.
Using the CrowdSignals.io Data
Ming will explore the CrowdSignals.io data in the human activity recognition related areas. Deep neural networks can take advantage of the large dataset to train a good model. In addition, because the CrowSignals.io dataset involves a number of sensors, it is worth trying neural networks with a sensor fusion approach.