## 3 Methodology
### Experiment Study Design  
For this research's investigation, a hardware-in-the-loop simulation experiment involving human participants was designed. The experiment was conducted with the aid of a computer-based UAV simulator, augmented with external hardware components: a single-board computer, a web camera, a microphone, a speaker, and two multi-colour (white and yellow) variable LED (Light Emitting Diode) lighting systems, which were laid out as shown in Figure 1a. Unlike typical pure simulation experiments, the hardware-in-the-loop experiment enabled practical interaction with the real world components, making the results more generalisable to practical real world applications. \begin{table}
\begin{tabular}{l l c} \hline \hline
**S/N** & **Small Multirotor UAV** & **Noise Levels (dB)** \\ \hline
1 & DJI Phantom 2 & 75.8 \\
2 & DJI Phantom 3 Pro & 76.3 \\
3 & DJI Phantom 4 pro & 76.9 \\
4 & DJI Inspire 2 & 79.8 \\
5 & Hover Cam & 72.1 \\
6 & DJI Mavic Pro & \(\sim\)65 \\
7 & DJI Mavic Pro Platinum & \(\sim\)60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Noise levels generated by some small multirotor unmanned aerial vehicles (UAVs) [22]. The study participants were mostly sited in front of a three-screen UAV simulation computer workstation, during which the participant were asked to perform a series of task. The first task measured the effect of varying noise levels from 55 dB to 85 dB in steps of 5 dB, being generated from a Bose Sound link Mini speaker system, playing a pre-recorded loop of a multirotor UAV propeller-rotor noise sound. The second task observed how varying the ambient lighting conditions from 10 Lux to 1400 Lux, for each white, yellow, and mixed lighting condition, against the backdrop of a green, blue, and white solid background surface, affects the quality of the finger gesture recognition. The gesture was processed on an Odroid XU4 single board computer and captured via a webcam device, in the same way it would be setup on a practical UAV. By processing both the speech and gesture control input on a linux based single board computer, such as the Odroid XU4, the mSVG control technique is made portable and easy to plug into other commercial UAV flight controller systems like the PixHawk, having the single board computer convert the high level nCA Tier I-III [2] or higher commands to lower levels that the flight controller can handle. The experiment participants were asked to repeat a series of 12 speech commands made from a vocabulary of 12 words. The commands were \"_go forward, go backward, step left, step right, hover, land, go forward half metre, go backward one metre, hover one metre, step left half metre, step right one metre, and stop_\". The first six speech commands were selected because they were essential UAV navigation commands that specify fundamental horizontal motion in the \(x\) and \(y\) coordinate directions at a fixed \(z\) altitude/vertical coordinate. The seventh to eleventh commands were randomly selected commands used to emulate practical \(x\), \(y\), and \(y\) coordinate navigation were short distance (less than 3 m) modifiers were applied. The twelfth command was selected based on practical consideration for a fail-safe/emergency/urgent command phrase that halts any current motion action. These commands are issued at quiet lab conditions of 55 dB, and then repeated for 60 dB, 65 dB, 70 dB, 75 dB, 80 dB, and 85 dB noisy lab conditions. The results are presented and discussed under Sections 4 and 5. For the second part of the experiment on how varying ambient lighting conditions and changing background affects gesture recognition quality, the procedure was to divide the experiment into a sequence of nine lighting stages (LS1-LS9). The default room lighting was turned off, all window blinds closed, and the main lighting source was the LED lamp with two colour LEDs--white (5500 K) and yellow (3500 K), as shown in Figure 1b.