Clustering Time-Series Data Generated by Smart Devices for Human Activity Recognition

2019 ◽  
pp. 708-716
Author(s):  
R. Jothi
Keyword(s):  
Time Series ◽  
Activity Recognition ◽  
Human Activity ◽  
Time Series Data ◽  
Human Activity Recognition ◽  
Series Data ◽  
Smart Devices

Assessment of Deep Learning Models for Human Activity Recognition on Multi-variate Time Series Data and Non-targeted Adversarial Attack

2021 ◽  
pp. 129-159
Author(s):  
Mahbuba Tasmin ◽  
Sharif Uddin Ruman ◽  
Taoseef Ishtiak ◽  
Arif-ur-Rahman Chowdhury Suhan ◽  
Redwan Hasif ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Time Series Data ◽  
Human Activity Recognition ◽  
Series Data ◽  
Learning Models ◽  
Adversarial Attack

scholarly journals Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4713
Author(s):  
Mariem Abid ◽  
Amal Khabou ◽  
Youssef Ouakrim ◽  
Hugo Watel ◽  
Safouene Chemcki ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Time Series Data ◽  
Recognition Rate ◽  
Feature Learning ◽  
Human Movement ◽  
Data Representation ◽  
Human Activity Recognition ◽  
Series Data ◽  
Wearable Sensor

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using an efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yielded an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down.

scholarly journals Deep Dilation on Multimodality Time Series for Human Activity Recognition

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 53381-53396 ◽  
Author(s):  
Rui Xi ◽  
Ming Li ◽  
Mengshu Hou ◽  
Mingsheng Fu ◽  
Hong Qu ◽  
...  
Keyword(s):  
Time Series ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

Imaging and fusing time series for wearable sensor-based human activity recognition

2020 ◽  
Vol 53 ◽  
pp. 80-87 ◽  
Author(s):  
Zhen Qin ◽  
Yibo Zhang ◽  
Shuyu Meng ◽  
Zhiguang Qin ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Time Series ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Wearable Sensor

scholarly journals Machine Learning for Activity Recognition from Movement Time Series Data

2017 ◽  
Author(s):  
Young-Nam Kim ◽  
Jin-Hee Park ◽  
Gyu-Jin Jang ◽  
Hye-Yeon Yu ◽  
Moon-Hyun Kim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Activity Recognition ◽  
Time Series Data ◽  
Movement Time ◽  
Series Data
Sign in / Sign up

Export Citation Format

Share Document