Elderly Fall Detection Analysis

Problem

In home care for elderly people, falls often occur in bathrooms, living rooms, and bedrooms. Existing approaches may rely on wearable devices or manual review of surveillance footage, which can slow response and raise privacy concerns. This skill targets that problem: it analyzes surveillance images or videos to detect human presence, infer posture, identify falls, and provide a rapid alert clue when an abnormal event appears.

How It Works

• Inputs: local image/video paths or network URLs; supported formats include jpg, jpeg, png, mp4, avi, and mov. Short continuous video clips are recommended, with a maximum size of 10MB.
• Core flow: prepare monitoring input -> the system automatically handles identity association internally -> run fall detection analysis -> return a structured report.
• Outputs: detection or monitoring results, risk notes, recommendations, and a report link.
• History query: use --list to query cloud-stored historical reports, optionally filtering by start date. History must be read from the cloud API rather than assembled from local memory or manual summaries.
• Deployment fit: cameras should cover the main activity areas of the elderly user, with sufficient lighting and minimal large shadows. Fixed-angle cameras are better suited for bathrooms, living rooms, bedrooms, and similar spaces.
• Limitations: the result is a safety warning reference only and cannot replace human confirmation. When an alarm occurs, verify by phone contact and start emergency response immediately.