Anxiety Behavior Recognition: Hand-Rubbing, Nail-Biting, Pacing
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About this skill
Problem to Solve
In home or office footage from a fixed camera, hand-rubbing, nail-biting, and pacing are often brief and repetitive. Manual review is impractical, and giving a clinical diagnosis is out of scope. This skill performs visual behavior statistics only: it detects these actions, outputs frequency, duration, and a 0-100 index for self-awareness and counseling support.
How It Works
It accepts local mp4/avi/mov files or video URLs, requiring visibility of the upper body/hands and leg path. It uses human detection, hand-keypoint estimation, action-event recognition, and trajectory analysis to separate hand_rubbing, nail_biting, and pacing, then reports dominant behavior, four severity levels, 7-14 day baseline comparison, and an event timeline. Historical reports must be queried through the cloud API with --list, not assembled from local memory.
Boundaries
It does not output anxiety disorder diagnoses, scale scores, or medication advice. Brief hand-rubbing, eating, or moving to fetch objects can cause false positives, so multi-window averaging is recommended. Use requires informed consent, with preference for anonymized statistical indicators. If a high level appears alongside symptoms like chest tightness or palpitations, include a mental-health hotline and recommend medical care.
Use Cases
- Counseling clinic uses two weeks of fixed-camera sessions to compare hand-rubbing and nail-biting frequency after intervention.
- Family caregiver reviews adolescent living-room footage to check pacing and nail-biting event timelines before suggesting relaxation.
- School counselor compiles self-study room video statistics on hand-rubbing and pacing trends for individual check-ins.
- App engineer connects fixed-camera video to the API and verifies event fields and index output format.
Best For
- Counselors who want to track changes in client hand-rubbing, nail-biting, and pacing frequency after intervention with an archivable index report.
- Family caregivers of adolescents who need daily living-room camera event timelines to decide when to suggest relaxation exercises.
- School counselors who need to compile pacing and hand-rubbing frequency trends from fixed-camera footage for individual check-ins.
- Mental-health app engineers who need to verify the three behavior event types, index fields, and report link format returned by the video API.
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