Human Activity Recognition Using Smartphone Sensors

2016 ◽  
pp. 41-47 ◽  
Author(s):  
Marcin D. Bugdol ◽  
Andrzej W. Mitas ◽  
Marcin Grzegorzek ◽  
Robert Meyer ◽  
Christoph Wilhelm
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Smartphone Sensors

Weighted Support Tensor Machines for Human Activity Recognition with Smartphone Sensors

2021 ◽  
pp. 1-1
Author(s):  
Zhenchao Ma ◽  
Laurence Tianruo Yang ◽  
Man Lin ◽  
Qingchen Zhang ◽  
Cheng Dai
Keyword(s):  
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

Robust Human Activity Recognition Using Smartphone Sensors via CT-PCA and Online SVM

2017 ◽  
Vol 13 (6) ◽  
pp. 3070-3080 ◽  
Author(s):  
Zhenghua Chen ◽  
Qingchang Zhu ◽  
Yeng Chai Soh ◽  
Le Zhang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Smartphone Sensors

scholarly journals SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 245
Author(s):  
Aiiad Albeshri
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Prediction Accuracy ◽  
Elderly Care ◽  
Human Activity Recognition ◽  
Training Data ◽  
Smart Surveillance ◽  
Smartphone Sensors ◽  
Self Learning ◽  
User Activities

Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost.

Sign in / Sign up

Export Citation Format

Share Document