As part of the Directed Research at WPI, I worked on researching for Deep Learning Methodologies that can be used for Alcogait.
Each of the folders are different implementations and consist of the respective files. Both papers use different variants of LSTM. The whole idea of evaluating these methodologies is to be able to replicate and understand results for Human Activity Recognition. Gait Analysis is a subset of Human Activity Recognition. We focus on different publicly available datasets (Opportunity, UCI HAR dataset, etc.). The results have been reproduced as mentioned in the paper.
Paper for ensemble methods:
Guan, Y., Ploetz, T., 2017. Ensembles of deep lstm learners for activity recognition using wearables. arXiv preprint arXiv:1703.09370 .
Paper for bi-directional LSTM:
Hammerla, N.Y., Halloran, S., Ploetz, T., 2016. Deep, convolutional, and recurrent models for human activity recognition using wearables, in: IJCAI.