Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs

Abstract

Although human activity recognition (HAR) has been studied extensively in the past decade, HAR on smartphones is a relatively new area. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. Realizing this goal is challenging, however. Firstly, these devices are low on resources, which limits the number of sensors that can be utilized. Secondly, to achieve optimum performance efficient feature extraction, feature selection and classification methods are required. This work implements a smartphone-based HAR scheme in accordance with these requirements. Time domain features are extracted from only three smartphone sensors, and a nonlinear discriminatory approach is employed to recognize 15 activities with a high accuracy. This approach not only selects the most relevant features from each sensor for each activity but it also takes into account the differences resulting from carrying a phone at different positions. Evaluations are performed in both offline and online settings. Our comparison results show that the proposed system outperforms some previous mobile phone-based HAR systems.

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

“…Khan et al [13] implemented a smartphone-based HAR scheme in accordance with these requirements. Time domain features were extracted from only three smartphone sensors, and a nonlinear discriminatory approach was employed to recognize 15 activities with a high accuracy.…”

Device-Free Indoor Activity Recognition System

“…In general, traditional HAR systems have been considered as device-based approaches such as vision-based [1], body-worn sensors [2], and smartphone interior sensors [3]. However, both vision-based and sensor-based activity recognition systems have certain drawbacks.…”

Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

“…With the advances in wireless communications and Micro-Electro-Mechanical System (MEMS) sensor technologies on mobile devices (e.g., accelerometer, gyroscope, magnetometer), collecting a vast amount of information about the user is feasible in an automatic way; however, it is still difficult to organize and aggregate such information into a coherent, expressive and semantically-rich representation of the user's physical activity [1][2][3][4]. In other words, there is a gap between low-level sensor readings and their high-level activity descriptions.…”

“…Recent popularity of mobile devices such as smartphones has resulted in considerable research directed towards the recognition and monitoring of dynamic activity patterns using the low-cost sensors [3]. In this research, a comparison has been conducted on using different sensor data and pattern recognition methods to identify the most efficient components of an activity recognition system for smartphones.…”