# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 5 Discussion  
From the experimental results of Sections 3 and 4, we can see that the proposed fundamental \(2\times 2\) MIMO radar system ameliorates the limitations posed by the SISO radar configuration. In Figures 2, 3, 8, and 9, adequate falling and walking activity signatures were observed irrespective of the activity direction. Furthermore, to quantify and numerically assess the performance of the SISO and \(2\times 2\) MIMO radar systems for each scenario, we have computed the DTW [44] distance for the walking activity. A numerical analysis of the falling activity signatures yields similar results. Therefore, to be concise, we have only shown the performance of the SISO and \(2\times 2\) MIMO radar systems for the walking activity. The DTW algorithm performs a time series analysis by evaluating the similarity between two temporal sequences. In this algorithm, the two temporal sequences are expanded or stretched such that the overall Euclidean distance between the two sequences is minimized, which makes the algorithm robust to any speed variations, accelerations, or decelerations in the data. This algorithm has been widely used in speech [52], gesture [53], and gait [54,55] recognition. To quantify whether a radar system has captured an adequate human activity signature or not, we first need a reference human activity signature for comparison. The reference activity signature is obtained in favorable conditions, where a person walks in the direction parallel to the boresight of the SISO radar. For each radar  
Figure 11: For Radar\({}_{2}\), the number of steps detected from the smoothed mean radial velocity \(\tilde{v}_{22}(t)\) of a human walking activity according to (**a**) Scenario 1, (**b**) Scenario 2, and (**c**) Scenario 3.  
subsystem, the DTW distance is computed between the reference human activity signature and the captured human activity signature of a particular scenario. For the SISO radar system, equivalent to Radar\({}_{1}\), we obtain a single DTW distance metric for each walking scenario as shown in the second and fourth columns of Table 3. However, for the proposed \(2\times 2\) MIMO radar system, we obtain two distinct DTW distance metrics, one for each radar subsystem (Radar\({}_{1}\) and Radar\({}_{2}\)). For the \(2\times 2\) MIMO radar system, the measured DTW distance metrics of Radar\({}_{1}\) and Radar\({}_{2}\) are compared, and the activity signature with the minimum DTW distance metric is chosen as shown in the last column of Table 3. Note that the radar subsystem with the minimum DTW distance is chosen because its signature would resemble the higher similarity to the reference activity signature. A performance summary of the SISO and \(2\times 2\) MIMO radar system is based on the results shown in Table 3. It is clear that the SISO radar system is unable to obtain an adequate activity signature in Scenario 2 as its DTW distance is very large (see the underlined DTW distance metric in Table 3). Unlike the SISO radar system, the \(2\times 2\) MIMO radar system is able to perform well in all three scenarios, as evident from the measured DTW distances and the results from Sections 3 and 4. Although we have presented the basic \(2\times 2\) distributed MIMO radar system to successfully capture the human activity signature in all directions, this study does not include a machine learning or deep learning-based classification network to recognize the type of human activity. However, an RF-based direction-independent HAR system using the proposed MIMO radar configuration would be considered as an extension of this research. To develop such a direction-independent HAR system, the data observed by Radar\({}_{1}\) and Radar\({}_{2}\) need to be merged or fused together. As previous studies have shown the performance improvements due to the fusion of the data obtained from either homogeneous sensors [17; 56] or heterogeneous sensors [9], we can expect to achieve similar performance gains by fusing the data of Radar\({}_{1}\) and Radar\({}_{2}\).