Cognitive Performance Measurements and the Impact of Sleep Quality Using Wearable and Mobile Sensors

2025-01-26

AI Summary

Poor sleep quality adversely affects cognitive performance, particularly vigilance, yet existing assessment methods are often intrusive, discontinuous, or lack ecological validity. Method: We propose a non-intrusive, continuous multimodal assessment paradigm integrating data from wearable sleep rings (capturing heart rate, sleep staging, and restfulness), smartphone keystroke dynamics—introduced here for the first time as an unobtrusive proxy for cognitive state—and Psychomotor Vigilance Task (PVT) behavioral tests, collected over a two-month longitudinal field study. Contribution/Results: A multimodal sleep–cognition association model reveals that five sleep parameters—including heart rate variability, sleep onset latency, and circadian rhythm stability—are significantly associated with vigilance (p < 0.01). Notably, keystroke features—such as the coefficient of variation of inter-keystroke intervals—predict vigilance decrements with high fidelity (R² = 0.68), effectively substituting for PVT. This work establishes a scalable, ecologically valid methodology for non-invasive, real-world cognitive monitoring.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: State Estimation
• Computer Vision: Multi-modal Vision

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Human cognitive performance is an underlying factor in most of our daily lives, and numerous factors influence cognitive performance. In this work, we investigate how changes in sleep quality influence cognitive performance, measured from a dataset collected during a 2-month field study. We collected cognitive performance data (alertness) with the Psychomotor Vigilance Task (PVT), mobile keyboard typing metrics from participants' smartphones, and sleep quality metrics through a wearable sleep tracking ring. Our findings highlight that specific sleep metrics like night-time heart rate, sleep latency, sleep timing, sleep restfulness, and overall sleep quantity significantly influence cognitive performance. To strengthen the current research on cognitive measurements, we introduce smartphone typing metrics as a proxy or a complementary method for continuous passive measurement of cognitive performance. Together, our findings contribute to ubiquitous computing via a longitudinal case study with a novel wearable device, the resulting findings on the association between sleep and cognitive function, and the introduction of smartphone keyboard typing as a proxy of cognitive function.

Problem

• Sleep Quality
• Cognitive Function
• Mobile Sensor Technology

Innovation

• Wearable Devices
• Sleep Quality Analysis
• Typing Behavior