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Feature fusion using deep learning for smartphone based human activity recognition
2021
International Journal of Information Technology
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The performance accuracy of human activity recognition (HAR) models mainly depend on the features which are extracted from domain knowledge. ...
Identification of human physical activities is an active research area since long due to its application in personalized health and fitness monitoring. ...
[9] , proposed a recognition system for human activities using accelerometer sensors built in smartphones. ...
doi:10.1007/s41870-021-00719-6
pmid:34151135
pmcid:PMC8196919
fatcat:f4bf2y4esvg5pjwhi4pco3mbbm
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition
[article]
2020
arXiv
pre-print
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Smartphone sensors based human activity recognition is attracting increasing interests nowadays with the popularization of smartphones. ...
In view of the large differences when the smartphone is carried at different locations, a group based location recognition is first developed to pinpoint the location of the smartphone. ...
Smartphone-sensors Based Activity Recognition Using IndRNN. ...
arXiv:2011.00395v2
fatcat:efs2vvjy5vhc3biw6ol42ek3bu
Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition
2019
Sensors
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This paper presents a novel framework to classify and analyze human activities. A new convolutional neural network (CNN) strategy is applied to a single user movement recognition using a smartphone. ...
In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. ...
The second most related work is "Human activity recognition by smartphone" by Le Tuan [4] . ...
doi:10.3390/s19071556
fatcat:kvrhudjoh5glvjilgmslxypy7a
Smartphone-based Estimation of Sidewalk Surface Type via Deep Learning
2021
Sensors and materials
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During training, the model was pretrained with the human activity sensing consortium (HASC) dataset, a large benchmark for human activity recognition, as a source domain, and we applied fine-tuning (FT ...
We, therefore, propose a method of estimating the sidewalk surface type by applying a convolutional neural network (CNN) based on the VGG16 architecture to sensor data. ...
Sensor-based human activity recognition via deep learning Sensor-based human activity recognition is a research field similar to sensor-based sidewalk surface estimation. ...
doi:10.18494/sam.2021.2976
fatcat:ea7b4zjjdjatviohpatfm6sh24
The Construction of Online Course Learning Model of Ideological and Political Education for College Students from the Perspective of Machine Learning
2022
Security and Communication Networks
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In this study, a human behavior recognition system is proposed for monitoring the learning status of students in the course of ideological and political education using the signals of smartphone embedded ...
A convolution neural network (CNN) is used to automatically extract prominent patterns from the raw signals of smartphone embedded sensors followed by the classification of the seven student activities ...
In behavior recognition, the most widely used feature extraction methods are time-domain features, frequency-domain features, and time-frequency domain features. ...
doi:10.1155/2022/4674468
fatcat:zi2pazjbbbhd3lfxmiwh3whmjm
Human Activity Recognition using Multi-Head CNN followed by LSTM
[article]
2020
arXiv
pre-print
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2019
2019 15th International Conference on Emerging Technologies (ICET)
unpublished
doi:10.1109/icet48972.2019.8994412 fatcat:xygbw55cobcebgfozwy2z7wjja
doi:10.1109/icet48972.2019.8994412 fatcat:xygbw55cobcebgfozwy2z7wjja
This study presents a novel method to recognize human physical activities using CNN followed by LSTM. ...
So, to achieve high accuracy, we propose a multi-head CNN model comprising of three CNNs to extract features for the data acquired from different sensors and all three CNNs are then merged, which are followed ...
[7] proposed a method based on one-dimensional CNN for recognition of human activities using tri-axial accelerometer data collected through smartphones. ...
arXiv:2003.06327v1
fatcat:trdzdtspwvhwteoxeyzzftwy3y
Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation
2018
2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)
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We investigate the problem of making human activity recognition (AR) scalable-i.e., allowing AR classifiers trained in one context to be readily adapted to a different contextual domain. ...
Our model, called HDCNN, assumes that the relative distribution of weights in the different CNN layers will remain invariant, as long as the set of activities being monitored does not change. ...
In this paper, we propose a framework for scalable human activity recognition, based on a deep convolutional neural network (CNN) model. ...
doi:10.1109/percom.2018.8444585
dblp:conf/percom/KhanRM18
fatcat:eckofi5jonct7nebnxygunxb3a
New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition
2021
Sensors
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The activity data were collected using a smartphone with the help of an exclusively developed iOS application. ...
In addition to the time domain, raw data were represented via the Fourier and wavelet domains. ...
Introduction HAR (human activity recognition) is an active research field that focuses on identifying human activities from a visual or sensor input. ...
doi:10.3390/s21082814
pmid:33923706
pmcid:PMC8073736
fatcat:5wa7cglvtzewdim55h7uqf4vsa
Human Activity Recognition Using an Ensemble Learning Algorithm with Smartphone Sensor Data
2022
Electronics
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This study proposes an ensemble learning algorithm (ELA) to perform activity recognition using the signals recorded by smartphone sensors. ...
Human activity recognition (HAR) can monitor persons at risk of COVID-19 virus infection to manage their activity status. ...
An extra feature vector consisting of 561 parameters is generated from time-domain and frequency-domain based on the raw sensor data. ...
doi:10.3390/electronics11030322
fatcat:2otxvv5fpzeijl3ks4gyh6glpu
An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones
2021
Sensors
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With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human ...
In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. ...
Hasan et al. (2018) [18]
A Robust Human
Activity Recognition
System Using
Smartphone Sensors
and Deep Learning
Smartphone inertial
sensors-based
approach
Compares different
deep learning models ...
doi:10.3390/s21113845
fatcat:jrkcvtqckfd4dgazehigzg2e5e
Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation
2020
Sensors
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Real-time recognition experiments were performed in multiple scenes, a recognition model trained by the CNN network was deployed in a Huawei Mate20 smartphone, and the five most used pedestrian activities ...
In the procedure of recognition, we designed and trained deep learning models using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) networks based on Tensorflow framework. ...
Ignatov, A. et al. used CNN and accelerometer data to recognize human activities in real-time [26] . ...
doi:10.3390/s20092574
pmid:32366055
pmcid:PMC7248737
fatcat:ag56r6ot75cndbm5htzlr7j36u
Survey on Human Activity Recognition based on Acceleration Data
2019
International Journal of Advanced Computer Science and Applications
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The state of the art in human activity recognition based on accelerometer is surveyed. ...
Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. ...
CONCLUSION This paper surveys the state-of-the-art in human activity recognition based on measured acceleration components. ...
doi:10.14569/ijacsa.2019.0100311
fatcat:v475yhq52bd53k7usw3ifvjsne
Human Activity Recognition using Smartwatch and Smartphone: A Review on Methods, Applications, and Challenges
2022
Iraqi Journal of Science
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Embedded sensors in smartwatch and smartphone enabled applications to use sensors in activity recognition with challenges for example, support of elderly's daily life . ...
Most articles published on human activity recognition used a multi -sensors based methods where a number of sensors were tied on different positions on a human body which are not suitable for many users ...
can perform more than one activity at the same time, therefore, exploring a concurrent activity is also challenge. ...
doi:10.24996/ijs.2022.63.1.34
fatcat:dznj4ouzirea5l34mngmptmhle
machine and deep learning approaches for human activity recognition
2021
International Journal of Intelligent Computing and Information Sciences
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Human Activity Recognition (HAR) is a domain that has shown great interest in the past years and tills now. The main cause for this is that it can be used in various applications. ...
In addition to this, the survey will cover the types of actions or activities that are predicted. ...
[20] proposed a methodology based on machine learning for the recognition of human activities. ...
doi:10.21608/ijicis.2021.82008.1106
fatcat:lcfqdcvtdnhfdfx3jvlvzq6kum
Sound-based Transportation Mode Recognition with Smartphones
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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We propose a convolutional neural network based recognition pipeline, which operates on the shorttime Fourier transform (STFT) spectrogram of the sound in the log domain. ...
Smartphone-based identification of the mode of transportation of the user is important for context-aware services. ...
Acknowledgement: This work was supported by the HUAWEI Technologies within the project "Activity Sensing Technologies for Mobile Users". ...
doi:10.1109/icassp.2019.8682917
dblp:conf/icassp/0009R19
fatcat:osv4x6pw4rgfvlsvr5pgu2xwgi
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