The Community for Technology Leaders
Green Image
Issue No. 05 - May (2018 vol. 51)
ISSN: 0018-9162
pp: 50-59
Thomas Plotz , Georgia Tech
Yu Guan , Newcastle University, UK
By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field's core application domains, including the ongoing transformation of human activity recognition technology through machine learning.
computer vision, image recognition, learning (artificial intelligence), mobile computing, natural language processing, speech recognition

T. Plotz and Y. Guan, "Deep Learning for Human Activity Recognition in Mobile Computing," in Computer, vol. 51, no. 5, pp. 50-59, 2018.
290 ms
(Ver 3.3 (11022016))