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Found 5 papers, 2 papers with code
mmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation using mmWave Radars
In this paper we presented mmPose-NLP, a novel Natural Language Processing (NLP) inspired Sequence-to-Sequence (Seq2Seq) skeletal key-point estimator using millimeter-wave (mmWave) radar data.
mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder
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.
mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs
To the best of the authors' knowledge, this is the first method to detect >15 distinct skeletal joints using mmWave radar reflection signals.
Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs
For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature.
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring
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.
