Human activity recognition based on transformed accelerometer data from a mobile phone

Abstract

Summary Mobile phones are equipped with a rich set of sensors, such as accelerometers, magnetometers, gyroscopes, photometers, orientation sensors, and gravity sensors. These sensors can be used for human activity recognition in the ubiquitous computing domain. Most of reported studies consider acceleration signals that are collected from a known fixed device location and orientation. This paper describes how more accurate results of basic activity recognition can be achieved with transformed accelerometer data. Based on the rotation matrix (Euler Angle Conversion) derived from the orientation angles of gyroscopes and orientation sensors, we transform input signals into a reference coordinate system. The advantage of the transformation is that it allows activity classification and recognition to be carried out independent of the orientation of sensors. We consider five user activities: staying, walking, running, ascending stairs, and descending stairs, with a phone being placed in the subject's hand, or in pants pocket, or in a handbag. The results show that an overall orientation independent accuracy of 84.77% is achieved, which is a improvement of 17.26% over those classifications without input transformation. Copyright © 2014 John Wiley & Sons, Ltd.

A systematic review of smartphone-based human activity recognition methods for health research

“…This separation was typically performed using a high-pass Butterworth filter of low order (e.g., order 3) with a cutoff frequency below 1 Hz. Other approaches transformed tri-axial into bi-axial measurement with horizontal and vertical axes 49 , or projected the data from the device coordinate system into a fixed coordinate system (e.g., the coordinate system of a smartphone that lies flat on the ground) using a rotation matrix (Euler angle-based 66 or quaternion 47,67 ).…”

HHARNet: Taking inspiration from Inception and Dense Networks for Human Activity Recognition using Inertial Sensors

“…Event detection through transformed accelerometer information was achieved by the researchers in [14]. They convert input signals straight into a reference coordinate system based on the rotation matrix (Euler Angle Conversion) derived from gyroscope orientation angles and orientation detectors.…”

Security protocols in body sensor networks using visible light communications

“…The sensor data are transmitted to doctors' mobile phones. Using the data, doctors can make quicker and better decisions.Example (Activity recognition ). In pervasive and mobile computing, activities can be recognized using BSNs.…”