Video-Based Fall Detection Analysis

The Problem

In home care, senior living, or assisted-living scenarios, a fall can happen faster than routine checks can catch. Raw video records events but does not by itself tell whether a person sat down, lay down, or actually fell, nor does it trigger a timely alert. This skill addresses that gap: it evaluates a short video clip to determine whether a fall occurred in the target area and returns a structured alert decision.

How It Works

The skill is built around short-video input and a cloud analysis workflow. Users can provide a local file path or a public video URL, and the skill calls the API through scripts/fall_detection_video_analysis.py to analyze human presence, posture changes, and motion trajectories. Its core capabilities include:

• Person and keypoint detection: extracts human pose information from video frames
• Temporal action reasoning: distinguishes falls from similar actions such as sitting or lying down
• Structured output and alerting: returns fields such as whether a fall was detected, confidence, and whether an alarm is needed
• Historical report lookup: retrieves the report list from the cloud API with --list and renders it as a Markdown table

Before analysis, the workflow typically validates the video and obtains an open-id for saving and querying report records. The result is intended for safety alerts, not as the sole medical or monitoring conclusion.

Boundaries

This skill works best with short, single-person, unobstructed monitoring clips. Recommended videos are under 5 seconds, with a detection distance of 3–5 meters and a fully visible body. Supported formats include mp4/avi/mov, up to 10MB. The output is a safety reference and should be confirmed by a person on site when an alarm is triggered.