1 code implementation • 5 Mar 2020 • Feng Jin, Arindam Sengupta, Siyang Cao
Moreover, to circumvent the difficulties in fall data collection/labeling, the VRAE is built upon an autoencoder architecture in a semi-supervised approach, and trained on only normal activities of daily living (ADL) such that in the inference stage the VRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs.
no code implementations • 21 Nov 2019 • Arindam Sengupta, Feng Jin, Renyuan Zhang, Siyang Cao
To the best of the authors' knowledge, this is the first method to detect >15 distinct skeletal joints using mmWave radar reflection signals.
no code implementations • 14 Nov 2019 • Feng Jin, Renyuan Zhang, Arindam Sengupta, Siyang Cao, Salim Hariri, Nimit K. Agarwal, Sumit K. Agarwal
For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature.
1 code implementation • 14 Nov 2019 • Feng Jin, Arindam Sengupta, Siyang Cao, Yao-Jan Wu
In multimodal traffic monitoring, we gather traffic statistics for distinct transportation modes, such as pedestrians, cars and bicycles, in order to analyze and improve people's daily mobility in terms of safety and convenience.
no code implementations • 17 Oct 2018 • Jing Mei, Shiwan Zhao, Feng Jin, Eryu Xia, Haifeng Liu, Xiang Li
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention.
no code implementations • 24 Dec 2017 • Feng Jin, Shiliang Sun
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks.