## 3 Physical and Mixed Safety
### Trustworthy and Explainable SNNs
The authors tested their TA-SNN with three different tasks, i.e., gesture recognition (using the DVS128 Gesture dataset [87], achieving 98.61% accuracy), image classification (using the CIFAR10 DVS dataset [104], achieving 72.00% accuracy), and spoken digit recognition (using the Spiking Heidelberg Digits dataset [105], achieving 91.08% accuracy). The authors stated that their attention mechanism did not impact accuracy significantly, but the reduction in the number of events significantly reduced the amount of computation. [PERSON] et al. [106] remarked that most current (in 2021) SNNs for processing event streams focus on object-recognition tasks. The authors proposed a hierarchical SNN to recognize actions using motion information. Their SNN was equipped with a motion-perception layer and a motion-pooling layer, consisting of motion-sensitive neurons. Neurons from the motion-pooling layer have a larger receptive field than neurons from the motion-perception layer. However, both layers were characterized by the same neural dynamics. The SNN consisted of five spiking layers, including the input and classifier layers. The solution was evaluated with three datasets, i.e., the DailyAction-DVS dataset [106], the DVS128 Gesture dataset [87], and the Action Recognition dataset [107]. For the DailyAction-DVS dataset--which could help recognize human action for safety applications--the proposed solution achieved 90.3% accuracy. For the DVS128 Gesture and Action Recognition datasets, the achieved accuracies were 92.7% and 78.1%, respectively. The accuracies achieved for the datasets were better than the accuracies from other papers quoted by the authors. Instead of detecting or classifying objects or actions, [PERSON] et al. [108] focused on a robust relative localization system, utilizing a DVS camera, Inertial Measurement Unit (IMU) sensor, and stationary flickering LEDs as landmarks. However, they did not utilize SNNs in this work. The authors tested and evaluated their system by mounting it to a small Unnamed Aerial Vehicle (UAV) and comparing the localization results with the Optitrack Prime 13 motion-capture system, a ground truth for localization. They achieved a maximum positioning error equal to 0.0137 m, a mean positioning error of 0.0052 m, a maximum orientation error of 2.16\({}^{\circ}\), a mean orientation error of 0.567\({}^{\circ}\), and a relative positioning error of 0.074% at a 7 m range. The test was performed indoors. The authors stated that the proposed system outperformed SOTA methods in terms of localization accuracy and execution time. Table 2 summarizes all datasets mentioned in this section related to SNNs and DVS processing. The first column references the dataset; the second column contains the dataset name; the third column points to papers described in this subsection and utilizing the dataset; the fourth column briefly describes the dataset; the fifth column references the dataset download page. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref. ** & **Name** & **Papers** & **Description** & **Total Samples** & **Download** \\ \hline
[86] & N-MNIST & [85; 109] & The spiking version of the MNIST dataset. It consists of handwritten digits. The dataset is generated with a DVS camera and consists of 70,000 samples. & 70 k & https://www. garrickorchard. com/datasets/n-mnist (accessed on 16 July 2023) \\
[87] & DVS128 Gesture & [85; 109] & A spiking dataset was recorded with a DVS camera, comprising 11 hand gesture categories, under 3 different illumination conditions, with 29 subjects. The camera resolution is 128 \(\times\) 128 spiking pixels. The recordings are 6 s long on average [103]. There are 1342 recordings in total. & 1342 recordings in total.