Transition-Aware Human Activity Recognition Using Smartphones
TLDR
Results show that TAHAR outperforms state-of-the-art baseline works and reveal the main advantages of the architecture.About:
This article is published in Neurocomputing.The article was published on 2016-01-01 and is currently open access. It has received 553 citations till now. The article focuses on the topics: Activity recognition & Systems architecture.read more
Figures
Figure 1: TAHAR System Architecture. 
Table 10: REALDISP Dataset Confusion Matrix. AL Implementation 
Table 9: REALDISP System error based on filtering stage and type of activity 
Table 3: List of measures for computing feature vectors. 
Table 6: SBHAR Dataset Confusion Matrices. Top: AL Implementation, Bottom: ATL Implementation. 
Table 5: SHBAR System error based on filtering stage and type of activity
Citations
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Journal ArticleDOI
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
TL;DR: A new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection is presented and shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used.
Journal ArticleDOI
LSTM-CNN Architecture for Human Activity Recognition
TL;DR: The results show that the proposed model has higher robustness and better activity detection capability than some of the reported results, and can not only adaptively extract activity features, but also has fewer parameters and higher accuracy.
Journal ArticleDOI
Deep Learning Models for Real-time Human Activity Recognition with Smartphones
TL;DR: A smartphone inertial accelerometer-based architecture for HAR is designed and a real-time human activity classification method based on a convolutional neural network (CNN) is proposed, which uses a CNN for local feature extraction on the UCI and Pamap2 datasets.
Journal ArticleDOI
Sensor-based and vision-based human activity recognition: A comprehensive survey
TL;DR: This survey analyzes the latest state-of-the-art research in HAR in recent years, introduces a classification of HAR methodologies, and shows advantages and weaknesses for methods in each category.
Posted Content
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
TL;DR: This study presents a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition and proposes a new taxonomy to structure the deep methods by challenges.
References
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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.
Journal ArticleDOI
A Survey on Human Activity Recognition using Wearable Sensors
Oscar D. Lara,Miguel A. Labrador +1 more
TL;DR: The state of the art in HAR based on wearable sensors is surveyed and a two-level taxonomy in accordance to the learning approach and the response time is proposed.
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A Survey on Human Activity Recognition using Wearable Sensors
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