Multiple patients behavior detection in real-time using mmWave radar and deep CNNs

Feng Jin, Renyuan Zhang, Arindam Sengupta, Siyang Cao, Salim Hariri, Nimit K. Agarwal, Sumit K. Agarwal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering point cloud of each one. For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature. A three-layer CNN model is created to classify the behavior for each patient. The tracking and point clouds detection algorithm was also implemented on an mmWave radar hardware platform with an embedded graphics processing unit (GPU) board to collect Doppler pattern and run the CNN model. A training dataset of six types of behavior were collected, over a long duration, to train the model using Adam optimizer with an objective to minimize cross-entropy loss function. Lastly, the system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient's behavior in a two-patient scenario.

Original languageEnglish (US)
Title of host publication2019 IEEE Radar Conference, RadarConf 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728116792
DOIs
StatePublished - Apr 2019
Event2019 IEEE Radar Conference, RadarConf 2019 - Boston, United States
Duration: Apr 22 2019Apr 26 2019

Publication series

Name2019 IEEE Radar Conference, RadarConf 2019

Conference

Conference2019 IEEE Radar Conference, RadarConf 2019
CountryUnited States
CityBoston
Period4/22/194/26/19

Keywords

  • Behavior detection
  • CNN
  • Doppler pattern
  • Fall detection
  • mmWave radar

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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  • Cite this

    Jin, F., Zhang, R., Sengupta, A., Cao, S., Hariri, S., Agarwal, N. K., & Agarwal, S. K. (2019). Multiple patients behavior detection in real-time using mmWave radar and deep CNNs. In 2019 IEEE Radar Conference, RadarConf 2019 [8835656] (2019 IEEE Radar Conference, RadarConf 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RADAR.2019.8835656