# Smartwatch-based detection of loss of pulse

## TL;DR

This paper introduces a smartwatch-based framework and dataset to detect loss of pulse with high accuracy and low false alarms.

## Contribution

The novel contribution is the first multimodal dataset combining PPG, IMU, and ground truth data for pulse loss detection.

## Key findings

- The framework achieves 97.1% detection accuracy using machine learning models.
- The false detection rate is 1.0 per hour with leave-one-subject-out cross-validation.
- The dataset includes data from 20 individuals with controlled pulse loss in a clinical setting.

## Abstract

We present a framework and a novel dataset for detecting loss of pulse cases using a smartwatch. Our methodology for gathering data involves inducing controlled pulse loss in the forearm within a clinical setting under medical supervision. To the best of our knowledge, this is the first multimodal dataset comprising photoplethysmogram (PPG) signals, inertial measurements (IMUs) that can be obtained from a commercially available smartwatch, and continuous recordings as ground truth, collected from 20 individuals. This dataset serves as a valuable resource for the research community to advance and verify techniques for detecting pulse loss and differentiate between the non-usage of smartwatches to prevent false alarms. Our presented framework consists of extracting several features from signals and employing machine learning models designed to differentiate between the emergency loss of pulse cases with the non-usage of smartwatches. The presented framework achieves a detection accuracy of up to 97.1%, with a false detection rate of 1.0 in an hour when evaluated using leave-one-subject-out cross-validation.