Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data

Condition(s) studied

Investigational drug(s) / intervention(s)

EMG Analysis Software: Surface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.

Study summary

The study assesses a machine learning model developed to predict fall risk among stroke patients using multi-sensor signals. This prospective, multicenter, open-label, sponsor-initiated confirmatory trial aims to validate the safety and efficacy of the model which utilizes electromyography (EMG) signals to categorize patients into high-risk or low-risk fall categories. The innovative approach hopes to offer a predictive tool that enhances preventative strategies in clinical settings, potentially reducing fall-related injuries in stroke survivors.

Eligibility

Primary outcome measure(s)

• Sensitivity of the Machine Learning Model — At the time of the single visit
  The primary outcome measure is the sensitivity of the machine learning model, which refers to its ability to correctly identify patients who are at high risk of falls. Sensitivity is defined as the proportion of actual positives that are correctly identified.

Trial sites (1)

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