Adult Facial Fatigue / Stress Index Analysis

Problem

Workplace fatigue and stress are often underreported or misjudged by subjective self-assessment. This skill turns visible adult facial cues—under-eye bag area, dark-circle grayscale, mouth-corner drop angle, and glabellar frown lines—into trackable numbers for personal state monitoring and workplace wellness.

How It Works

• Input: supports jpg/png images or mp4/avi/mov short videos up to 10MB, or a network URL that the API downloads automatically.
• Detection: performs front-face detection and alignment, estimates under-eye bag area, dark-circle darkness, mouth-corner drop, and glabellar lines, with overall skin dullness as a reference.
• Output: produces a 0-100 fatigue/stress index, labels states such as good, mild_fatigue, moderate_stress, or high_stress, and lists major contributing features plus directional advice on rest, hydration, or relaxation.
• History: uses --list to query cloud-stored reports, reads only from the API, and avoids bias from local memory or manual aggregation.

Boundaries and Notes

• Result nature: a single score is sensitive to lighting, makeup, filters, and expression; interpret it with continuous trends, and do not treat it as medical diagnosis.
• Privacy compliance: facial data is biometric data; obtain the person’s consent before use, and store or encrypt source images carefully.
• Runtime requirements: use only the skill’s built-in scripts, do not generate ad-hoc scripts, and render the historical report list from cloud API results as a Markdown table.