A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices

IEEE Journal of Biomedical and Health Informatics

Volumn 21, Issue 1, 2017, Pages 56-64

  Ravi, Daniele ^a   Wong, Charence ^a   Lo, Benny ^a   Yang, Guang Zhong ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

ActiveMiles; deep learning; Human Activity Recognition (HAR); Internet of Things (IoT); low power devices; wearable

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA ANALYTICS; INTERNET OF THINGS; LEARNING SYSTEMS; MHEALTH; WEARABLE SENSORS;

ACTIVEMILES; HUMAN ACTIVITY RECOGNITION; INTERNET OF THINGS (IOT); LOW-POWER DEVICES; WEARABLE;

DEEP LEARNING;

ARTICLE; CONVOLUTIONAL NEURAL NETWORK; DEEP BELIEF NETWORK; DEEP LEARNING APPROACH; ELECTROCARDIOGRAPHY; ELECTROMYOGRAPHY; GAIT; HEALTH CARE; LEARNING ALGORITHM; MACHINE LEARNING; MEASUREMENT ACCURACY; MOBILE PHONE; PHYSICAL ACTIVITY; RESTRICTED BOLTZMANN MACHINE; SPORT; SUPPORT VECTOR MACHINE; WELLBEING; AMBULATORY MONITORING; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DEVICES; HUMAN; HUMAN ACTIVITIES; PROCEDURES; SIGNAL PROCESSING;

HUMAN ACTIVITIES; HUMANS; MACHINE LEARNING; MONITORING, AMBULATORY; NEURAL NETWORKS (COMPUTER); SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 85014926848     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2016.2633287     Document Type: Article

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