3 research outputs found
Indoor human activity recognition using high-dimensional sensors and deep neural networks
Many smart home applications rely on indoor human activity recognition. This challenge is currently primarily tackled by employing video camera sensors. However, the use of such sensors is characterized by fundamental technical deficiencies in an indoor environment, often also resulting in a breach of privacy. In contrast, a radar sensor resolves most of these flaws and maintains privacy in particular. In this paper, we investigate a novel approach toward automatic indoor human activity recognition, feeding high-dimensional radar and video camera sensor data into several deep neural networks. Furthermore, we explore the efficacy of sensor fusion to provide a solution in less than ideal circumstances. We validate our approach on two newly constructed and published data sets that consist of 2347 and 1505 samples distributed over six different types of gestures and events, respectively. From our analysis, we can conclude that, when considering a radar sensor, it is optimal to make use of a three-dimensional convolutional neural network that takes as input sequential range-Doppler maps. This model achieves 12.22% and 2.97% error rate on the gestures and the events data set, respectively. A pretrained residual network is employed to deal with the video camera sensor data and obtains 1.67% and 3.00% error rate on the same data sets. We show that there exists a clear benefit in combining both sensors to enable activity recognition in the case of less than ideal circumstances
Multi-Person Continuous Tracking and Identification from mm-Wave micro-Doppler Signatures
In this work, we investigate the use of backscattered mm-wave radio signals
for the joint tracking and recognition of identities of humans as they move
within indoor environments. We build a system that effectively works with
multiple persons concurrently sharing and freely moving within the same indoor
space. This leads to a complicated setting, which requires one to deal with the
randomness and complexity of the resulting (composite) backscattered signal.
The proposed system combines several processing steps: at first, the signal is
filtered to remove artifacts, reflections and random noise that do not
originate from humans. Hence, a density-based classification algorithm is
executed to separate the Doppler signatures of different users. The final
blocks are trajectory tracking and user identification, respectively based on
Kalman filters and deep neural networks. Our results demonstrate that the
integration of the last-mentioned processing stages is critical towards
achieving robustness and accuracy in multi-user settings. Our technique is
tested both on a single-target public dataset, for which it outperforms
state-of-the-art methods, and on our own measurements, obtained with a 77 GHz
radar on multiple subjects simultaneously moving in two different indoor
environments. The system works in an online fashion, permitting the continuous
identification of multiple subjects with accuracies up to 98%, e.g., with four
subjects sharing the same physical space, and with a small accuracy reduction
when tested with unseen data from a challenging real-life scenario that was not
part of the model learning phase.Comment: 16 pages, 12 figures, 5 table