A machine learning model to detect falls mimicking cardiac arrest-related collapse based on wrist-derived accelerometry: the DETECT-2 study

In wearable-based automated cardiac arrest detection technology, photoplethysmography (PPG) is the most commonly used sensor to detect the absence of pulsations. To minimize false-positive cardiac arrest alerts, accelerometry signals are often used for the detection of ongoing movement. We conducted the DETECT-2 study to develop an accelerometer-based machine learning model for the detection of cardiac arrest-related collapse, which is often a first manifestation of cardiac arrest.

Healthy volunteers simulated cardiac arrest-related collapses through sudden and soft falls without subsequent movement. Accelerometer signals were collected using the CardioWatch wristband; video recordings were made as a reference. An accelerometer-based gradient boosting model (GBM) for fall detection was trained (70%) and tested (30%). The primary endpoint was the sensitivity for the detection of falls; secondary endpoints were false-positive fall alerts. Nineteen participants performed 567 falls. In the training set (n =13; 388 falls), the sensitivity of the GBM was 99.2% (95% confidence interval [CI] 98–100%], with four false positives. In the test set (n = 6; 179 falls), sensitivity was 96.1% (95% CI 92–98%), with two false positives. For sudden falls (n = 120) and soft falls (n = 59), sensitivities were 100% (95% CI 96–100%) and 88.1% (95% CI 76–95%) in the test set (P < 0.001), respectively.

Using accelerometry data from the CardioWatch, sudden and soft falls that mimic cardiac arrest-related collapse can be accurately detected. The next step in the development of automated cardiac arrest detection is the integration of accelerometer signals into the existing PPG-based model, with the aim of reducing false positives and increasing sensitivity in everyday use.