Identification of Optimal Data Augmentation Techniques for Multimodal Time-Series Sensory Data: A Framework

• Published in: Information (Basel) Vol. 15; no. 6; p. 343
• Main Authors: Ashfaq, Nazish, Khan, Muhammad Hassan, Nisar, Muhammad Adeel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Activities of daily living; Algorithms; Analysis; Classification; Data augmentation; Datasets; Deep learning; ensemble deep network; Exercise; Heart rate; human activity recognition; Literature reviews; Machine learning; Methods; Motion sensors; multimodal time series data; Neural networks; Sensors; Smartphones; Time series; Wearable technology; Germany

Recently, the research community has shown significant interest in the continuous temporal data obtained from motion sensors in wearable devices. These data are useful for classifying and analysing different human activities in many application areas such as healthcare, sports and surveillance. The...