Abstract
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.
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From a strict point-of-view, we are dealing with a cross-correlation, as the kernel is not flipped.
The data sets are publicly available at: https://www.imec-int.com/en/harrad.
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The research activities described in this paper were funded by Ghent University-imec, the Fund for Scientific Research-Flanders (FWO-Flanders), and the European Union.
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Appendix: Indoor human activity recognition on combined data set
Appendix: Indoor human activity recognition on combined data set
In this study, we developed a deep learning approach toward automatic indoor human activity recognition. Moreover, this approach is validated on two separate data sets that are both applicable in a different domain. For the sake of completeness, we explore the efficacy of an integrated system that is capable of predicting the correct activity when dealing with a combined data set of gestures and events. To that end, both data sets are merged and the 3d-CNN and ResCNN networks are employed for the radar and camera sensors, respectively. The combined data set consists of 3852 samples distributed over 12 different activities. Table 4 lists the total number of samples per activity. Similar to the experiments performed in Sects. 7.2 and 7.3, the sample length is set to 2 s or 30 frames.
Table 8 shows the obtained results of both the radar- and video-based model. The results suggest that our developed approach is valid for the combined data set. The radar-based 3d-CNN achieves 14.40 % and 6.67 % error rate on the cross-validation and random split evaluation approach, respectively. These results are similar to those obtained on the gestures data set (c.f., Sect. 7.2). Similarly, the video-based ResCNN network obtains 3.52 % and 2.70 % error rates for \({\overline{S}}\) and RS, respectively.
Furthermore, an experiment is conducted that shows the benefit of fusing both sensors. More precisely, artificially darkened frames (denoted by the \(*\) operator) are used as input for the video-based model. This input has a clear negative effect on the error rate of the ResCNN network since it degrades by nearly 20 % and 13 % for \({\overline{S}}\) and RS, respectively. However, through the combined use of both sensor-specific networks this effect is not pronounced in the late fusion approach (Fused*). The performance of this approach only degrades by 2 % in comparison with the use of clean RGB data. Moreover, the fused approach that uses artificially darkened video data still outperforms the radar-only approach by a margin of 2 %.
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Vandersmissen, B., Knudde, N., Jalalvand, A. et al. Indoor human activity recognition using high-dimensional sensors and deep neural networks. Neural Comput & Applic 32, 12295–12309 (2020). https://doi.org/10.1007/s00521-019-04408-1
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DOI: https://doi.org/10.1007/s00521-019-04408-1