Research Article
Design and Implementation of Data Collection & Analysis Tool for Healthcare Parameter Monitoring using Inverse Low Pass Filter
@ARTICLE{10.4108/eai.30-10-2018.160460, author={S. K. Chaurasia and S.R.N Reddy}, title={Design and Implementation of Data Collection \& Analysis Tool for Healthcare Parameter Monitoring using Inverse Low Pass Filter}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={4}, number={16}, publisher={EAI}, journal_a={PHAT}, year={2018}, month={10}, keywords={Activity Detection, Context Detection, Gravity Removal, Inverse Low Pass Filters, Machine Learning}, doi={10.4108/eai.30-10-2018.160460} }- S. K. Chaurasia
S.R.N Reddy
Year: 2018
Design and Implementation of Data Collection & Analysis Tool for Healthcare Parameter Monitoring using Inverse Low Pass Filter
PHAT
EAI
DOI: 10.4108/eai.30-10-2018.160460
Abstract
BACKGROUND: Health of an individual can be determined by monitoring his daily activities. Human Activity Recognition (HAR) is a process, used to record these activities. The first step of HAR involves collection of Data from various Sensors, which is challenging due to the requirement of interfacing of hardware Sensors. Researchers either do hardware interfacing themselves or just analyse already available dataset. In the literature, many measures are proposed to calculate the human activities by using the Accelerometer data. But, different activities recorded by Accelerometer are not accurate due to presence of gravity component. Also there is requirement of identification of appropriate Machine learning algorithm for HAR.
OBJECTIVE: To facilitate the researchers in hassle free data collection for monitoring HAR and finding optimal cutoff frequency to remove gravity component present in accelerometer data. Also detecting suitable machine learning model for HAR.
METHODS: An application has been developed using Inverse low pass filter with an optimal threshold value to compute cut-off frequency for the removal of gravity component from accelerometer data. 8 different machine learning models are trained and tested for examining suitability of models on the collected data at server. Accuracy and execution time are considered as validation parameters.
RESULTS: Gravity Component is removed and data gathered by using 0.6 alpha is providing 0.05 mean acceleration in static state. Among Various ML models k Nearest Neighbor is providing 93.17 percent of accuracy with 0.187486 sec of execution time.
CONCLUSION: Removal of Gravity component is optimised by our method and out of various ML model kNN is identified as suitable model for HAR.
Copyright © 2018 S. K. Chaurasia et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.