Recognizing Mental Stress in Chess Players Using Vital Sign Data Christopher Eggert, Mentor: Óscar D. Lara Department of Computer Science and Engineering Abstract The identification of psychological stress can provide important feedback in performance-critical activities. We present a system for psychological stress detection using physiological sensors during a chess match. The sensors are inside an unobtrusive chest strap that can be worn by the player during a match. We investigate two approaches to stress detection: classification and clustering. By playing games on an Android phone, the system can apply machine learning techniques to the player’s vital sign data to give important feedback such as which moves caused the player to become stressed during a match. Feature Extraction Using 5 s, 10 s, and 15 s 50% overlap time windows, the following features are extracted: 1. Statistical features using from acceleration signals (X, time Y, Z):windows. Features extracted 5-second 50% overlap • Time- and frequency-domain features, extracted from acceleration signals. We evaluated two algorithms: Expectation Maximization and kmeans clustering. Both algorithms generated similar clusters. 2. Structural features from physiological signals (heart rate, respiration rate, skin temp, etc.) a. The coefficients of the polynomials that best fit the signal. The Least Squares Algorithm is applied, based on the Sum of Squared Error: Motivation • Affect recognition is a burgeoning area which uses computers to automatically detect emotions. Clustering is important because it doesn’t rely on outside information about when the player was stressed (unlike classification). This allows the system to report stress even when the player may not remember being stressed. b. Trend: c. Magnitude of change: Classification For classification, we used self-reports to determine when the player was stressed. Classification performance was evaluated using five algorithms: Neural Networks, J48, LogitBoost, Bayesian Networks, and Support Vector Machines. • Users gain objective feedback about when stress occurs and what potentially causes it. • Implementing stress recognition on a phone is convenient and efficient. System Overview Clustering Figure 5: Classification Algorithm Evaluations Measured attributes • Heart rate • Respiration rate • Breath amplitude • Skin temperature • Posture • 3D Acceleration • ECG amplitude Figure 1: Timer App Conclusions Clustering is a viable method for unsupervised detection of psychological stress, according to the assessments. This technique can be used to evaluate a situation and determine when the user was stressed, without being trained. This has numerous applications in performance enhancement and security situations. Figure 2: Chess App Also indicated is that, given user feedback, the LogitBoost classification algorithm with a 5-second time window outperforms the other evaluated algorithms and time windows, with a 93.56% classification accuracy. Vital Signs: • Heart Rate • Respiration Rate Figure 3: System Overview • Posture • collection Breathprotocol Amplitude Data • Approval the Institutional Review Board (IRB) at USF. • ECGof Amplitude • Data were collected from 8 individuals while performing all binary transitions between activities, in a • Galvanic Skin Response naturalistic fashion. Figure 4: Example of Clustering These evaluations show that a mobile phone-based system can use both supervised and unsupervised machine learning techniques to successfully recognize mental stress in chess players. UNIVERSITY OF SOUTH FLORIDA