Human activity recognition with smartphone sensors using deep learning neural networks

2016 ◽  
Vol 59 ◽  
pp. 235-244 ◽  
Author(s):  
Charissa Ann Ronao ◽  
Sung-Bae Cho
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Smartphone Sensors

EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications

2020 ◽  
Vol 79 (41-42) ◽  
pp. 31663-31690
Author(s):  
Debadyuti Mukherjee ◽  
Riktim Mondal ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Debotosh Bhattacharjee
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Approach ◽  
Healthcare Applications ◽  
Smartphone Sensors

scholarly journals A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5707 ◽  
Author(s):  
Saedeh Abbaspour ◽  
Faranak Fotouhi ◽  
Ali Sedaghatbaf ◽  
Hossein Fotouhi ◽  
Maryam Vahabi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Cognitive Disorders ◽  
Human Activity Recognition ◽  
Hybrid Models ◽  
Wide Range ◽  
And Performance ◽  
High Level

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity.

A robust human activity recognition system using smartphone sensors and deep learning

2018 ◽  
Vol 81 ◽  
pp. 307-313 ◽  
Author(s):  
Mohammed Mehedi Hassan ◽  
Md. Zia Uddin ◽  
Amr Mohamed ◽  
Ahmad Almogren
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Smartphone Sensors

Towards Environment-Independent Human Activity Recognition using Deep Learning and Enhanced CSI

2020 ◽  
Author(s):  
Zhenguo Shi ◽  
J. Andrew Zhang ◽  
Richard Xu ◽  
Qingqing Cheng ◽  
Andre Pearce
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Deep Learning Shared Bandpass Filters for Resource-Constrained Human Activity Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 39089-39097
Author(s):  
Morgan Stuart ◽  
Milos Manic
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Bandpass Filters ◽  
Human Activity Recognition ◽  
Resource Constrained
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