FocusGuard uses a combination of machine learning algorithms and sensor data to detect minor distractions and interruptions.

It tracks environmental cues like sound spikes from notifications, changes in typing patterns, or face orientation perceived by the webcam to determine distraction events.

These detected events are then categorized and visualized on a dashboard that provides a timeline of disruptions.

Users receive actionable insights on how these interruptions correlate with productivity dips, enabling them to adjust their workflow strategies without the need for intrusive tracking methods.

FocusGuard offers a granular view of focus disruption that current tools fail to capture, giving users the ability to precisely identify and mitigate micro-distractions.

This not only enhances time management but substantially boosts productivity without overhauling existing routines.

The technology required for FocusGuard, while complex, builds on existing capabilities like machine learning for pattern recognition and sensor-based data collection.

Feasibility is high given the prevalence of devices with sensors capable of capturing necessary data (e.g., microphones, webcams).

However, challenges may include ensuring user privacy and system integration across diverse hardware.