Deep recurrent neural network for mobile human activity recognition with high throughput
TLDR
A method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN) is proposed, and various architectures and its combination to find the best parameter values are investigated.Abstract:
In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The “high throughput” refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 ms, while the best traditional method required 11.031 ms which includes 11.027 ms for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN.read more
Citations
More filters
Journal ArticleDOI
Deep learning for sensor-based activity recognition: A survey
TL;DR: The recent advance of deep learning based sensor-based activity recognition is surveyed from three aspects: sensor modality, deep model, and application and detailed insights on existing work are presented and grand challenges for future research are proposed.
Journal ArticleDOI
Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
TL;DR: A user-independent deep learning-based approach for online human activity classification using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series is presented.
Journal ArticleDOI
Sensor-based and vision-based human activity recognition: A comprehensive survey
TL;DR: This survey analyzes the latest state-of-the-art research in HAR in recent years, introduces a classification of HAR methodologies, and shows advantages and weaknesses for methods in each category.
Journal ArticleDOI
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Henry Friday Nweke,Henry Friday Nweke,Ying Wah Teh,Ghulam Mujtaba,Ghulam Mujtaba,Mohammed Ali Al-Garadi +5 more
TL;DR: The focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices.
Journal ArticleDOI
IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment
Valentina Bianchi,Marco Bassoli,Gianfranco Lombardo,Paolo Fornacciari,Monica Mordonini,Ilaria De Munari +5 more
TL;DR: An innovative HAR system, exploiting the potential of wearable devices integrated with the skills of deep learning techniques, is presented with the aim of recognizing the most common daily activities of a person at home.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Related Papers (5)
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Human activity recognition with smartphone sensors using deep learning neural networks
Charissa Ann Ronao,Sung-Bae Cho +1 more