Implementation of a Wearable Device to Monitor and Track Status of Elderly People

Automatization when monitoring elderly patients can improve their living conditions and even, reduce the consequences of some events, e.g. falls, what can be fatal in some cases for such people.

For this purpose, we propose a wrist-wearable low-power device able to monitor body physiological signals as temperature, heart rate, oxygen saturation, and monitor daily activities and on this basis to detect falls. The device is also able to transmit and receive data with an Android App using a Bluetooth Low-Energy interface.

The wearable use an Xtensa microprocessor as an engine, sensor MAX30205 to measure the temperature, sensor MAX30102 to heart rate and oxygen saturation, and sensor LSM6DS3 for detect falls and monitor daily activity. The signal produced by the MAX30102 sensor is processed for preparation of the decision signal based on signal data fusion received from other sensors. For counting the beats, we pass the signal through an IIR bandpass filter and then use a novel, made by ourselves, peak detection algorithm. For detecting the oxygen saturation some approaches have been followed (in time and frequency domains) to obtain the attributes for the further functional analyses of the monitoring process

The results show the monitoring and tracking functionalities of this wearable; however, it could be improved in some aspects covered by test data collected in real conditions for learning stages.

As the conclusion, the viability of this implementation has been more than proved given the fact that some providers are interested in deploying such wearable in elderly home residences. A low-cost and low-power device IoT system can be integrated into the existing care workflow and may reduce patient fall risk being an elusive patient safety challenge based on the technological aids and tools