# Motion Sickness in Mixed-Reality Situational Awareness System
## 5 Results  
### SSQ Normalization Using MSQ  
Motion sickness is a variable and often perplexing phenomenon in VR experiences. The individual susceptibility to VR-induced motion sickness can greatly differ among users. The literature has pointed toward a potential solution--a weak correlation between pre-test MSQ scores and subsequent SSQ scores [17]. This revelation opens the possibility of using MSQ scores to normalize SSQ values and establish a more uniform measure of motion sickness severity. To delve into this correlation, our study initially compiled a wide spectrum of SSQ data, reflecting the diverse reactions people exhibit. Employing linear regression on each participant's SSQ scores, we quantified the spread of the generated lines to derive a baseline score. To bridge the MSQ-SSQ relationship, a series of scaling functions were introduced as \(F(x,m,s)\). Here, \(x\) signified the raw SSQ input to be scaled, m denoted the corresponding MSQ score for that specific participant, and s was a parameter introduced to optimize the function. Manipulating the s variable and the fundamental function \(F(x,m,s)\) led to the identification of the best fit. The most effective function, derived from this analysis of the raw data, can be expressed as: \(F(x,m,s)=x/(1+m\cdot s)\). The best \(s\) value on all of the data was found to be 1.50, but this varied slightly as newer tests were recorded. Sample best fitting lines can be seen in Figure 5. The first plot depicts the error of the application of the function \(F(x,m,s)\) on all recorded data over varying values of \(s\). The \(x\)-axis depicts varying values of \(s\) and the \(y\)-axis depicts the standard deviation for the data at each time step after scaling. The four lines on the plot show where the smallest standard deviation was present for 5, 10, 15, and 60 min after scaling. Figure 5: The function parameter s for SSQ normalization using MSQ. This was a form of focused fitting where the standard deviation of values near the X amount of minutes was used as the fitting error with a varying MSQ coefficient. Each of the lines represents all user data scaled by different MSQ coefficient values (\(x\)-axis) and then linearly interpolated to obtain the value at X minutes; all of these values were aggregated and the standard deviation was used as the fit error (\(y\)-axis). The values 5, 10, and 15 were chosen as they were the most frequent SSQ sample point values in the dataset and thus the most reliable. The 60 min represents our main target duration but is mostly interpolated. The function \(F(x,m,s)\) was generated in a similar manner. Several functions including linear, polynomial, exponential, and combinations of these were tested. The proposed function of \(F(x,m,s)=x/(1+m\cdot s)\) outperformed the the rest as more complex functions tended to over-fit on a specific time range very quickly and changed drastically when new test results were added for evaluation. In the context of long-term tests, a distinct pattern emerged. Figure 6 only contains recorded data that exceeded 15 min in test time as this subset proved to be more stable for SSQ curve prediction. Fluctuations in the best fit suggested an insufficiency of data, hinting at the challenges of conducting longer-duration tests. Here, the coefficient value ranged from 3.45 to 5.40, with coefficients exceeding 1.00 yielding more stable MSQ scaling results compared to those based on data from all experiment durations. This phenomenon can be attributed to the dominance of 15 min samples, which seemingly lacked sufficient time to allow motion sickness effects to stabilize. The best \(s\) value on the long data was 4.95. Both the initial 1.50 and 4.95 were tested for data normalization and the value 4.95 was found to have a smaller standard deviation on the unaltered data set compared to 1.50. As such, the coefficient of 4.95 was used for MSQ scaling, but this remains to be fully explored.