Dynamic Programmable Clock Frequency Using Machine Learning Algorithms to Reduce Power Consumption in Wearables

Abstract

In today’s world, wearable devices have become a key device in monitoring all internal activities of human being with the help of sensors. Humans are provided with a wristband device fabricated with various sensors to detect overall body condition in every day-to-day activity. Most of the wearable devices can be used to monitor the beats per minute (BPM) by heart rate sensor (pressure sensor), sleep and body movement activity by three-axis accelerometer sensor, the respiratory rate by biosensors and electrocardiogram (ECG) by sensors (electrodes). It is well known that soon in the near future, all sensors related to human activity and health monitoring play a major role in human beings; especially, for old age people, the wearable devices with well-packed sensor will soon be reaching out the market. The most wearable devices are able to provide almost all sensors in a single package, but fail to work if the power goes off shortly. The proposed model makes an attempt to reduce the power consumption in the wearable device by introducing the programmable clock frequency with machine learning concept. To reduce the power consumption in wearable devices, a programmable clock frequency of 1 GHz has been designed and monitored on a monthly basis. The proposed work uses different frequencies based on different activities of wearable device with generated data set of machine learning algorithms and is analysed using PYNQ boards. The proposed work concludes based on the multilinear-based regression model to check the power consumed for the given parameters observed that step count consumes 96% power during the complete analysis.