Li, Boning, and Sano, Akane. Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress. Retrieved from https://par.nsf.gov/biblio/10185017. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4.2 Web. doi:10.1145/3397318.

Li, Boning, & Sano, Akane. Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4 (2). Retrieved from https://par.nsf.gov/biblio/10185017. https://doi.org/10.1145/3397318

Li, Boning, and Sano, Akane. "Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4 (2). Country unknown/Code not available. https://doi.org/10.1145/3397318. https://par.nsf.gov/biblio/10185017.

@article{osti_10185017,
place = {Country unknown/Code not available}, title = {Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress}, url = {https://par.nsf.gov/biblio/10185017}, DOI = {10.1145/3397318}, abstractNote = {}, journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies}, volume = {4}, number = {2}, author = {Li, Boning and Sano, Akane}, }