Search results for: Anzah H. Niazi
chapter
This paper proposes a multi-level meta-classifier for identifying human activities based on accelerometer data. The training data consists of 77 subjects performing a combination of 23 different activities and monitored using a single hip-worn triaxial accelerometer. Time and frequency based features were extracted from two-second windows of raw accelerometer data and a subset of the features, together...
chapter
Human activity recognition (HAR) has many important applications in health care. While machine learning-based techniques have been applied for wearable sensor-based HAR, very few researchers have comprehensively studied the effects of various factors on the accuracy and robustness of activity classification. This paper presents a detailed empirical study of machine learning-based HAR schemes. The...
