Frequency Domain Approach for Activity Classification using Accelerometer

TL;DR

This paper presents a frequency domain method for classifying activities like rest, walking, and running using accelerometer data, achieving high accuracy for rest and movement detection and moderate accuracy for walking and running differentiation.

Contribution

It introduces a combined time and frequency analysis approach for activity classification using MEMS accelerometers in wireless sensor networks, with specific focus on SMA and median frequency features.

Findings

Rest and movement classification accuracy: 100%

Walk and run classification accuracy: 81.25%

Effective use of SMA and median frequency features

Abstract

Activity classification was performed using MEMS accelerometer and wireless sensor node for wireless sensor network environment. Three axes MEMS accelerometer measures body's acceleration and transmits measured data with the help of sensor node to base station attached to PC. On the PC, real time accelerometer data is processed for movement classifications. In this paper, Rest, walking and running are the classified activities of the person. Both time and frequency analysis was performed to classify running and walking. The classification of rest and movement is done using Signal magnitude area (SMA). The classification accuracy for rest and movement is 100%. For the classification of walk and Run two parameters i.e. SMA and Median frequency were used. The classification accuracy for walk and running was detected as 81.25% in the experiments performed by the test persons.