A Study on Human Activity Recognition Using Accelerometer Data from Smartphones
Reads0
Chats0
TLDR
In this paper, a low-pass filter was designed to isolate the component of gravity acceleration from that of body acceleration in the raw data, and five classifiers were tested using various statistical features.About:
This article is published in Procedia Computer Science.The article was published on 2014-01-01 and is currently open access. It has received 373 citations till now. The article focuses on the topics: Accelerometer & Activity recognition.read more
Citations
More filters
Journal ArticleDOI
Physical Human Activity Recognition Using Wearable Sensors.
Ferhat Attal,Samer Mohammed,Mariam Dedabrishvili,Faicel Chamroukhi,Latifa Oukhellou,Yacine Amirat +5 more
TL;DR: A review of different classification techniques used to recognize human activities from wearable inertial sensor data shows that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms.
Proceedings ArticleDOI
A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer
Yuqing Chen,Yang Xue +1 more
TL;DR: This paper constructs a CNN model and modify the convolution kernel to adapt the characteristics of tri-axial acceleration signals, and shows that the CNN works well, which can reach an average accuracy of 93.8% without any feature extraction methods.
Journal ArticleDOI
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Henry Friday Nweke,Henry Friday Nweke,Ying Wah Teh,Ghulam Mujtaba,Ghulam Mujtaba,Mohammed Ali Al-Garadi +5 more
TL;DR: The focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices.
Journal ArticleDOI
On the use of ensemble of classifiers for accelerometer-based activity recognition
TL;DR: The power of ensemble of classifiers approach for accelerometer-based activity recognition is explored and a novel activity prediction model based on machine learning classifiers is built and provides better performance than MLP-based recognition approach suggested in previous study.
Journal ArticleDOI
A survey on wearable sensor modality centred human activity recognition in health care
TL;DR: This survey aims to provide a more comprehensive introduction to Sensor-based human activity recognition (HAR) in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods.
References
More filters
Book ChapterDOI
Activity recognition from user-annotated acceleration data
Ling Bao,Stephen S. Intille +1 more
TL;DR: This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves, and suggests that multiple accelerometers aid in recognition.
Journal ArticleDOI
Activity recognition using cell phone accelerometers
TL;DR: This work describes and evaluates a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing, and has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity.
Journal ArticleDOI
A survey on vision-based human action recognition
TL;DR: A detailed overview of current advances in vision-based human action recognition is provided, including a discussion of limitations of the state of the art and outline promising directions of research.
Proceedings Article
Activity recognition from accelerometer data
TL;DR: This paper reports on the efforts to recognize user activity from accelerometer data and performance of base-level and meta-level classifiers, and Plurality Voting is found to perform consistently well across different settings.
Journal ArticleDOI
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers
TL;DR: How human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose, is discussed.