# RFID RSS Fingerprinting System for Wearable Human Activity Recognition
## 3 Measurement and Discussion  
Three sets of testing datasets were sampled carried out under LOS and NLOS propagation condition to validate the robustness of the system. Firstly, known stationary positions were used as test position data and mapped against all six positions in the reference database. The same procedure was repeated for all other ADL positions. Table 3 illustrates the distance results of two stationary position of sitting and laying on the floor positions against all six reference positions. From Table 3, it can be seen that the lowest RSSIs are recorded for both correct positions, which validates the robustness of the proposed system for tracking multiple positions. Moreover, six real-life static and dynamic positions including walking, standing, sitting, resting and laying on the floor were sampled iteratively. Each test dataset was collected over a time period of 15s for each sampled iteration. The sampled test dataset was used to estimate the Euclidean distance between the test data and each of the reference position database shown in Figure 4.  
Figure 4: System performance for walking position within the measured time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test/Ref Positions** & **Walking** & **Standing** & **Sitting** & **Resting** & **Laying on Floor** & **Laying in Bed** \\  & **Reference** & **Reference** & **Reference** & **Reference** & **Reference** & **Reference** \\ \hline Sitting Test & 2.4 & 4.0 & 1.4 & 1.4 & 2.9 & 2.5 \\ Laying on Floor Test & 3.2 & 4.7 & 3.3 & 3.7 & 2.1 & 3.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Euclidean distance for “Sitting on the chair” and “Laying on the floor” of patients with the reference position database. Figure 4 shows that the proposed system accurately identifies walking as the smallest distance in all time periods, indicated by the outer polygon. It is worth noting that since the walking speed of the patient is relatively high compared to standing in the position, the RSSI variation for walking and standing is 3 dB which shows good correlation. Moreover, seven iterative samplings of standing position of 7s in each sampled iteration are recorded. The Euclidean distance for the standing activity is performed as presented in Figure 5. In Figure 5, it is shown that the standing position exhibits the lowest RSSI of 0.5 dB, which indicate the nearest true position detected. Moreover, it can be seen that the second nearest position is inferred by the proposed system is walking. Similar to the standing position, the accuracy of the proposed system is tested for the sitting position using a sampling timeframe of 7s. The result of the sitting position is presented in Figure 6. In Figure 6, the system recognises the correct position as sitting for all time frames. However, it is interesting to observe that the next nearest position detected is resting. This is due to the fact that the tag readings for sitting and resting positions are quite similar. It worth noting that fall is a common cause of domestic accidents. To this end, the volunteers are sampled for laying on the fall. The obtained results are compared with the reference database. Figure 7 illustrates the result for the patient laying on the floor for a given period of time. It can be seen from Figure 7 that the true position of each patient is inferred accurately. However, the next closest reference position is sitting with an RSSI of 0.5 dB. Figure 5: System performance for standing over a stipulated timeframe. Figure 6: System performance for sitting position over a stipulated timeframe. Figure 8 illustrates the measured results when a patient lies on the bed for a timeframe of 4 s. The system accurately detects the correct patient position. However, the next nearest position inferred is sitting on the chair with an RSSI of 1.5 dBm whilst the farthest is standing as standing followed by walking. Furthermore, another case scenario of concurrent position, that is, when targets make different physical movements concurrently over time.