Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm

Monitoring a patient’s vital signs is considered one of the most challenging problems
in telehealth systems, especially when patients reside in remote locations. Companies now use
IoT devices such as wearable devices to participate in telehealth systems. However, the steady
adoption of wearables can result in a significant increase in the volume of data being collected and
transmitted. As these devices run on limited battery power, they can run out of power quickly due
to the high processing requirements of the device for data collection and transmission. Given the
importance of medical data, it is imperative that all transmitted data adhere to strict integrity and
availability requirements. Reducing the volume of healthcare data and the frequency of transmission
can improve a device’s battery life via an inference algorithm. Furthermore, this approach creates
issues for improving transmission metrics related to accuracy and efficiency, which are traded-off
against each other, with increasing accuracy reducing efficiency. This paper demonstrates that
machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares
algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst
maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the
metrics. The results showed that the DLSA provides the best performance, with an efficiency of
3.33 times for reduced sample data size and an accuracy of 95.6%, with similar accuracies observed
in seven different sampling cases adopted for testing that demonstrate improved efficiency. This
proposed method significantly improve both metrics using ML without sacrificing one metric over
the other compared to existing methods with high efficiency.