Deep Activity Recognition Models with Triaxial Accelerometers

• Main Authors: Abu Alsheikh, Mohammad, Selim, Ahmed, Niyato, Dusit, Doyle, Linda, Lin, Shaowei, Tan, Hwee-Pink
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2017
• Subjects: Learning; Human-Computer Interaction
• Online Access: https://hdl.handle.net/10356/80702; http://hdl.handle.net/10220/42197; https://arxiv.org/abs/1511.04664
• Institution: Nanyang Technological University

Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data. We consider the problem...