TY - JOUR
T1 - Improving Human Activity Recognition for Sparse Radar Point Clouds
T2 - A Graph Neural Network Model with Pre-Trained 3D Human-Joint Coordinates
AU - Lee, Gawon
AU - Kim, Jihie
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - Many devices have been used to detect human action, including wearable devices, cameras, lidars, and radars. However, some people, such as the elderly and young children, may not know how to use wearable devices effectively. Cameras have the disadvantage of invading privacy, and lidar is rather expensive. In contrast, radar, which is widely used commercially, is easily accessible and relatively cheap. However, due to the limitations of radio waves, radar data are sparse and not easy to use for human activity recognition. In this study, we present a novel human activity recognition model that consists of a pre-trained model and graph neural networks (GNNs). First, we overcome the sparsity of the radar data. To achieve that, we use a model pre-trained with the 3D coordinates of radar data and Kinect data that represents the ground truth. With this pre-trained model, we extract reliable features as 3D human joint coordinate estimates from sparse radar data. Then, a GNN model is used to extract additional information in the spatio-temporal domain from these joint coordinate estimates. Our approach was evaluated using the MMActivity dataset, which includes five different human activities. Our system achieved an accuracy of 96%. The experimental result demonstrates that our algorithm is more effective than five other baseline models.
AB - Many devices have been used to detect human action, including wearable devices, cameras, lidars, and radars. However, some people, such as the elderly and young children, may not know how to use wearable devices effectively. Cameras have the disadvantage of invading privacy, and lidar is rather expensive. In contrast, radar, which is widely used commercially, is easily accessible and relatively cheap. However, due to the limitations of radio waves, radar data are sparse and not easy to use for human activity recognition. In this study, we present a novel human activity recognition model that consists of a pre-trained model and graph neural networks (GNNs). First, we overcome the sparsity of the radar data. To achieve that, we use a model pre-trained with the 3D coordinates of radar data and Kinect data that represents the ground truth. With this pre-trained model, we extract reliable features as 3D human joint coordinate estimates from sparse radar data. Then, a GNN model is used to extract additional information in the spatio-temporal domain from these joint coordinate estimates. Our approach was evaluated using the MMActivity dataset, which includes five different human activities. Our system achieved an accuracy of 96%. The experimental result demonstrates that our algorithm is more effective than five other baseline models.
KW - Graph neural network
KW - Human activity detection
KW - Human activity recognition
KW - MmWave radar
KW - Point clouds
UR - http://www.scopus.com/inward/record.url?scp=85124966293&partnerID=8YFLogxK
U2 - 10.3390/app12042168
DO - 10.3390/app12042168
M3 - Article
AN - SCOPUS:85124966293
SN - 2076-3417
VL - 12
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 4
M1 - 2168
ER -