Stress Detection For Keystroke Dynamics
Abstract
Background. Stress can profoundly affect human behavior. Critical-infrastructure operators (e.g., at nuclear power plants) may make more errors when overstressed; malicious insiders may experience stress while engaging in rogue behavior; and chronic stress has deleterious effects on mental and physical health. If stress could be detected unobtrusively, without requiring special equipment, remedies to these situations could be undertaken. In this study a common computer keyboard and everyday typing are the primary instruments for detecting stress. Aim. The goal of this dissertation is to detect stress via keystroke dynamics the analysis of a users typing rhythms and to detect the changes to those rhythms concomitant with stress. Additionally, we pinpoint markers for stress (e.g., a 10% increase in typing speed), analogous to the antigens used as markers for blood type. We seek markers that are universal across all typists, as well as markers that apply only to groups or clusters of typists, or even only to individual typists. Data. Five types of data were collected from 116 subjects: (1) demographic data, which can reveal factors (e.g., gender) that influence subjects reactions to stress; (2) psychological data, which capture a subjects general susceptibility to stress and anxiety, as well as his/her current stress state; (3) physiological data (e.g., heart-rate variability and blood pressure) that permit an objective and independent assessment of a subjects stress level; (4) self-report data, consisting of subjective self-reports regarding the subjects stress, anxiety, and workload levels; and (5) typing data from subjects, in both neutral and stressed states, measured in terms of keystroke timings hold and latency times and typographical errors. Differences in typing rhythms between neutral and stressed states were examined to seek specific markers for stress.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2018
- Accession Number
- AD1167960
Entities
People
- Shing-hon Lau
Organizations
- Carnegie Mellon University