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

By Mohammad Mehedi Hassan, Md. Zia Uddin, Amr Mohamed, Ahmad S. Almogren. Future generations computer systems, 2018

575 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5864d83374d89ef2a032246af44ed18cab9c58e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. M. Jamal Deen
2. David A. Patterson
3. Parisa Rashidi
4. Vijay Janappa Reddi
5. Joaquín B. Ordieres Meré
6. Meiqing Wang
7. Maximilian Lam
8. Xiaochen Zheng

In Computer Vision and Pattern Recognition

1. Colby R. Banbury
2. M. Jamal Deen
3. David A. Patterson
4. Jae-sun Seo
5. Parisa Rashidi
6. Graziano Pravadelli
7. Vijay Janappa Reddi
8. Joaquín B. Ordieres Meré

Citations in the corpus

• Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “Researchers have proposed various algorithms to extract features from sensor signals and to perform activity classification using machine-learned models like K Nearest Neighbor (KNN) and SVM [123, 149, 168, 193, 254, 265, 270, 290, 313, 319].”
• Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “Other used models are the Naïve Bayes (NB) [4], [42], [94], [122], [136], [142], [159], [169], [171], [184], [185], [210], the Dynamic Bayesian Network (DBN) [101], [103], [166], Hidden Markov Models (HMM) [68], [69], [151], [179], [182], [208], Extreme Learning Machine (ELM) [153], [154], Principal”
• Smartphone Sensors for Health Monitoring and Diagnosis (2019, methodology): “Finally, an appropriate classification model such as support vector machine (SVM) [137,138,155,158,159], naive Bayes (NB) [136,149,155,156], k-means clustering [138,149], logistic regression [134,155,156], k-nearest neighbor (KNN) [133,136,155,156,158], neural network (NN) [140,141,143,151,152] or a”
• Benchmarking TinyML Systems: Challenges and Direction (2020, methodology): “Other deployed TinyML applications, like activity recognition from IMU data (Hassan et al., 2018), rely on low feature dimensionality to fit within the tight constraints of the platforms.”
• Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0 (2018, methodology): “A variety of machine learning algorithms have been used to process human activity data in the big data environment [15–20].”