Key result
A wearable device algorithm combining accelerometer and pulse oximeter data was developed to distinguish resting from loss of consciousness to prevent drowning.
Why the study?
To develop an algorithm for a wearable device to detect pre-drowning symptoms and alert rescue staff to prevent drowning in swimming pools.
Does a wearable pulse oximeter with an accelerometer algorithm detect pre-drowning symptoms in swimming pools?
Does a wearable pulse oximeter with an accelerometer algorithm detect pre-drowning symptoms in swimming pools?
A novel algorithm combining pulse oximetry and accelerometry in a wearable device may help detect pre-drowning symptoms by distinguishing between resting and unconsciousness.
May enable wearable drowning alerts; leaves open prospective validation before use.
The purpose of this research was to develop an algorithm for a wearable device that would prevent people from drowning in swimming pools. The device should detect pre-drowning symptoms and alert the rescue staff. The proposed detection method is based on analyzing real-time data collected from a set of sensors, including a pulse oximeter. The pulse oximetry technique is used for measuring the heart rate and oxygen saturation in the subject's blood. It is an optical method; subsequently, the measurements obtained this way are highly sensitive to interference from the subject's motion. To eliminate noise caused by the subject's movement, accelerometer data were used in the system. If the acceleration sensor does not detect movement, a biosensor is activated, and an analysis of selected physiological parameters is performed. Such a setup of the algorithm allows the device to distinguish situations in which the person rests and does not move from situations in which the examined person has lost consciousness and has begun to drown.
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Kałamajska et al. (2022) studied Drowning. Wearable pulse oximeter and accelerometer algorithm was evaluated on Detection of pre-drowning symptoms. A wearable device algorithm combining accelerometer and pulse oximeter data was developed to distinguish resting from loss of consciousness to prevent drowning.
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