Identifying fall risk using machine learning algorithms

This invention describes a system that determines a person's risk of falling by analyzing data from weight-sensing pads or devices. It uses machine learning software, specifically a Hidden Markov Model, to process this weight data, identify different body postures, and calculate specialized balance scores called "punctuated equilibrium model" stability metrics. This information can then be used to alert the person or a healthcare provider about their fall risk.

Why it matters: Filed before widespread edge AI. Machine learning models are now more efficient for real-time sensor data processing, making such systems more practical.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedLoad sensors, machine learning algorithms, PEM metrics.

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