This repository acts as a supplement to my work for CS 725 - Independent Study
This project works on players' physiological responses during League of Legends matches, specifically focusing on how intense gaming moments, such as kills, influence players' stress levels. By measuring Galvanic Skin Response (GSR) and heart rate variations, we find patterns that might indicate heightened emotional or cognitive stress during gameplay.
The main goal is to determine if and how significant gaming events correlate with measurable changes in physiological metrics, potentially affecting player performance and overall gaming experience.
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Data Collection: Data is sourced from augmented match files, including each player's time-stamped kill events and corresponding physiological data (GSR and heart rate).
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Data Processing:
- Segmentation:
- Signal Processing:
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Statistical Analysis:
- Peak Detection: Using the
scipy.signal.find_peaksmethod, heart rate peaks, and GSR dips are identified for both kill and baseline segments. - t-Test: To assess statistical significance, a two-sample t-test is applied to compare the mean counts of GSR dips and HR peaks between kill and baseline segments.
- Peak Detection: Using the
In the game League of Legends, defeating opponents, often referred to as securing a "kill," involves a combination of strategy, skill, and timing. There isn't an instant-kill mechanic as every champion (character) in the game has a health bar that needs to be depleted for them to be defeated. Here's a simplified explanation of how a player can kill an opponent in the game:
- Understanding Champion Abilities: Each champion in League of Legends comes with a unique set of abilities, which include basic attacks and four special skills (three basic abilities and one ultimate ability).
- Effective Use of Combos: Skilled players learn to chain their champion’s abilities in quick succession to maximize damage.
- Confidence and Decision-Making: Effective players gauge when they're in a strong position to secure a kill, understanding their own strength relative to the opponent's.
- Execution: Once everything is lined up—the player knows their champion’s combos, understands both champions' strengths and weaknesses, and senses an opportunity—they execute their plan.
Confidence plays a key role here; a player who hesitates often misses the narrow window of opportunity to take down the opponent.
This is how we tried visualizing Galvanic Skin Response Data (GSR) and Heart Rate Sensor Data (HR) from the data available at https://github.com/smerdov/eSports_Sensors_Dataset
Galvanic Skin Response Data (GSR):
- Galvanic Skin Response (GSR) is a measure of the electrical conductance of the skin, which is influenced by changes in sweat gland activity.
- GSR sensors are used to detect variations in skin conductance, which can be an indicator of emotional or cognitive arousal.
- GSR sensors capture these changes and provide data that can be analyzed to understand the physiological responses of players during gameplay. By examining GSR data, we can gain insights into the level of stress or arousal experienced by players during different gaming events, such as kills in League of Legends matches.
Heart Rate Sensor (HR):
- Heart Rate (HR) is a measure of the number of times the heart beats per minute.
- HR sensors are used to monitor the heart rate of players during gameplay.
- Changes in heart rate can indicate physiological arousal, stress, or excitement.
- By analyzing HR data, we can gain insights into the intensity of players' emotional and cognitive responses during gaming events, such as kills in League of Legends matches.
- Understanding the relationship between heart rate and gaming events can help us understand the impact of these events on player performance and overall gaming experience.
Here, below as we can see, the first graph suggests the kill and assist events by players and if we eyeball it we can notice two things:
- GSR Dips before kills, suggesting that the player was in a aroused/excited state before the kill (supporting the claim of League of Legends Gameplay)
- Heart Rate peaks after the kill/assist events, suggesting that the player had a moment of excitement/ satisfaction.
There are other metrics which have been recorded and processed by the original contributors (Engagement, Excitement, Stress, Relaxation, Interest and Focus)
- If you see their correspondance with the kill and assist events, eyeballing the hypothesis becomes easy and leans towards what we are proposing - the gamers were focused and excited into the gameplay more within the threshold window of the kill/assist events.
Tonic Component Graph: By examining the trend in the tonic component, you can assess how the participant's baseline arousal level changes over time. An increasing trend might suggest growing tension or stress, while a decreasing trend could indicate relaxation.
Phasic Component Graph: Looking at the peaks in the phasic component can help you identify moments of acute response. The presence, frequency, and magnitude of these peaks can be linked back to specific events, offering insights into the participant's reactions.
- Peak Detection: Using the
scipy.signal.find_peaksmethod, heart rate peaks, and GSR dips are identified for both kill and baseline segments. - t-Test: To assess statistical significance, a two-sample t-test is applied to compare the mean counts of GSR dips and HR peaks between kill and baseline segments.
The p-value is a statistical tool that helps us determine whether the differences we observe in our data are significant or if they could be just due to random chance. This concept is often used in hypothesis testing to draw conclusions about populations based on sample data.
For instance, consider we have test scores from two different groups of students. One group was taught using a new method (Group A), and the other group was taught using a traditional method (Group B).
Group A (New Method) The scores for Group A are as follows: 90, 92, 95, 88, 96. The new method seems to be effective as the scores are relatively high.
Group B (Traditional Method) The scores for Group B are as follows: 85, 87, 84, 88, 86. These scores are slightly lower compared to Group A.
Upon initial observation, it appears that Group A has performed better. However, the question arises - is Group A genuinely more intelligent, or was it just luck? This is where the p-value comes into play. By calculating the p-value, we can determine if the difference in scores between the two groups is statistically significant or if it could be due to random chance.
"Deciding the threshold to scan for dips in GSR and peaks in HR was a key factor"
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Earlier as a default approach, we moved to consider the window to be as 10 seconds before and 10 seconds after a kill event
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The results as we can guess were not good - suggesting that most parameters didn't have a significant p value to be considered
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But we had a hunch using the method of how the game League of Legends is actually played, a player is quite confident by using the combination of powerups and understand whether the kill would happen or no. And this usually happens earlier that the actual kill event takes place
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We slowly moved the threshold before the kill event farther and finally consider the thresholds as:
threshold_before = 35 # seconds before the kill event
threshold_after = 15 # seconds after the kill event
The following graph visualizes the above dataframes which consist of the kill events with the physiological sensors' data
For each player and match, the analysis generates:
- Average counts of GSR dips and HR peaks during kills vs. baseline periods.
- P-values from the t-tests to determine if the differences observed are statistically significant.
Results are aggregated across all matches and stored in a comprehensive data frame, allowing for easy access and further analysis.
The findings can provide insights into players' attention & engagement levels and help develop strategies to enhance gaming performance. Additionally, understanding physiological responses can aid in the design of more engaging and balanced game environments.