TILES-TIME-SERIES (2018)

This repo contains code for time series analysis for all tiles stream

The related papers are:

1. Feng, T., Booth, B.M. and Narayanan, S.S., 2020, May. Modeling behavior as mutual dependency between physiological signals and indoor location in large-scale wearable sensor study. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1016-1020). IEEE.

2. Feng, T. and Narayanan, S.S., 2020, May. Modeling behavioral consistency in large-scale wearable recordings of human bio-behavioral signals. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1011-1015). IEEE.

3. Feng, T. and Narayanan, S., 2020, July. Modeling Human Movement Behavior Among Nursing Profession. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 4256-4260). IEEE.

4. Feng, T. and Narayanan, S., 2019, July. Imputing missing data in large-scale multivariate biomedical wearable recordings using bidirectional recurrent neural networks with temporal activation regularization. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2529-2534). IEEE.

One thing we have found from this study is that wearable data modeling is still a very challenging task, we are still in the process learning about the limitation of the wearable data, what, how, and when should we sample, and also most importantly ensuring the trustworthy aspect of the wearable data computing.