A computerized multidimensional measurement of mental workload via handwriting analysis

Area of Science:

• Cognitive Psychology
• Human-Computer Interaction
• Biometrics

Background:

• Handwriting is a complex motor skill influenced by cognitive states.
• Understanding how mental workload affects fine motor control, like handwriting, is crucial for various applications.
• Previous research has explored cognitive load effects on performance, but handwriting-specific indicators require further investigation.

Purpose of the Study:

• To investigate the influence of varying mental workload levels on handwriting behavior.
• To identify distinct handwriting characteristics associated with low versus high mental workload.
• To develop a comprehensive profile of handwriting indicators for mental workload assessment.

Main Methods:

• Fifty-six participants completed handwriting tasks involving numerical progressions of differing difficulty.
• Handwriting data was captured using a computer-connected digitizer.
• Temporal, spatial, angular velocity, and pressure measures of handwriting were analyzed.

Main Results:

• Significant differences in temporal, spatial, and angular velocity handwriting measures were observed across different mental workload conditions.
• No significant differences were found in handwriting pressure measures.
• Data reduction techniques identified three handwriting clusters, with two effectively distinguishing between the three mental workload levels.

Conclusions:

• Handwriting behavior is demonstrably affected by mental workload.
• Individual handwriting measures provide unique insights into cognitive load.
• A combination of handwriting measures offers a comprehensive indicator of mental workload.