# A Gamified AI-Driven System for Depression Monitoring and Management
## 4 Results: Simulation-Based Evaluation of Depression Detection Models
### Synthetic Dataset Structure  
The dataset included the following main categories of features:  
* Behavioral features: game accuracy, average response latency, error rate, and interaction frequency, representing engagement and performance under cognitive load.
* Cognitive features: baseline cognitive score, working memory load, and attention span indicators were sampled from normal distributions informed by psychological literature.
* Emotional states: self-reported mood, stress level, and energy level, generated dynamically across sessions to simulate fluctuating affective profiles.
* Contextual features: temperature, humidity, and time of day correlated probabilistically with mood and behavioral responses based on findings from environmental psychology studies.
* Temporal dynamics: each synthetic user had 10-30 sessions over time to introduce variability in longitudinal patterns (such as fatigue, recovery trends, or mood swings).  
The synthetic data schema emulated real-world variability by combining continuous, ordinal, and binary variables. Gaussian noise and mild correlations (r = 0.3-0.6) were injected between key variables (poor sleep and mood or between temperature and stress) to reflect plausible, ecologically valid relationships observed in prior literature. These features were selected based on their relevance to depressive symptomatology. For example, response latency and game accuracy are linked to psychomotor slowing in depression, while mood variability, stress, and low energy are core affective indicators. Contextual variables like temperature and time of day affect circadian rhythms and mood regulation. Together, these multimodal inputs support realistic simulation of depression-related behavioral and cognitive states. Table 1 summarizes the structure of the synthetic dataset.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Category** & **Feature Name** & **Data Type** & **Generation Method** \\ \hline \multirow{4}{*}{Behavioral} & Game Accuracy & Continuous & Sampled from a normal distribution \\  & Response Latency & Continuous & Sampled from normal a distribution \\  & Error Rate & Continuous & Derived from gameplay \\  & Interaction Frequency & Count & Poisson distribution \\ \hline \multirow{4}{*}{Cognitive} & Baseline cognitive score & Continuous & Sampled from a normal distribution \\  & Working Memory Load & Ordinal & Mapped from the cognitive scale \\  & Attention Span & Ordinal & Mapped from EMA \\ \hline \multirow{4}{*}{Emotional} & Mood Score & Continuous & Fluctuates per session \\  & Stress Level & Ordinal & Dependent on temperature and performance \\  & Energy Level & Ordinal & Correlated with stress and activity \\ \hline \multirow{4}{*}{Contextual} & Temperature & Continuous & Realistic seasonal values \\  & Humidity & Continuous & Realistic seasonal values \\  & Time of Day & Categorical & Random morning/afternoon/evening split \\ \hline Temporal & Session Index (1–30) & Integer & Sequential with session-specific noise \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of the synthetic dataset categorized by feature, data type, and generation method.