Frequency based Classification of Activities using Accelerometer Data

TL;DR

This paper introduces a frequency-based method for classifying user activities like Rest, Walk, and Run using accelerometer data, which adapts to individual differences without needing threshold adjustments.

Contribution

It proposes a simple, automatic classification algorithm based on a single frequency parameter that accounts for individual variations in activity frequencies.

Findings

High accuracy in activity classification across different individuals.

No need for threshold tuning due to normalization step.

Automatic block-by-block classification process.

Abstract

This work presents, the classification of user activities such as Rest, Walk and Run, on the basis of frequency component present in the acceleration data in a wireless sensor network environment. As the frequencies of the above mentioned activities differ slightly for different person, so it gives a more accurate result. The algorithm uses just one parameter i.e. the frequency of the body acceleration data of the three axes for classifying the activities in a set of data. The algorithm includes a normalization step and hence there is no need to set a different value of threshold value for magnitude for different test person. The classification is automatic and done on a block by block basis.