Fall Detection
Fall detection algorithm based on multi-frame temporal analysis precisely distinguishes falls from daily actions such as squatting, sitting, and bending by continuously capturing dynamic changes of human skeletal keypoints over time. The algorithm not only focuses on single-frame posture but also deeply analyzes the center of gravity descent speed, body tilt angle change rate, and post-fall stationary state, achieving millisecond-level response and high-confidence judgment for fall events.
Algorithm Introduction
Fall detection algorithm based on multi-frame temporal analysis precisely distinguishes falls from daily actions such as squatting, sitting, and bending by continuously capturing dynamic changes of human skeletal keypoints over time. The algorithm not only focuses on single-frame posture but also deeply analyzes the center of gravity descent speed, body tilt angle change rate, and post-fall stationary state, achieving millisecond-level response and high-confidence judgment for fall events.
Applications
Elderly living alone intelligent monitoring systems, nursing home safety early warning platforms, home security cameras, bathroom/emergency assistance monitoring.
Core Advantages
Extremely Low False Alarm Rate (Strong Anti-Interference)
Proprietary temporal action discrimination mechanism effectively filters easily confused postures such as tying shoelaces while squatting, sitting on sofas, and picking up objects while bending over, thoroughly solving the pain point of traditional algorithms that alarm on any movement, triggering alerts only when confirmed rapid fall with loss of balance and abnormal stationary state.
Alert Sensitivity
For sudden falls (such as slips and fainting), the algorithm can complete recognition and push alerts within moments of the action occurring (<1 second), not missing the golden rescue window.
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