MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns

2016 ◽  
Vol 25 ◽  
pp. 104-124 ◽  
Author(s):  
Georgios Meditskos ◽  
Stamatia Dasiopoulou ◽  
Ioannis Kompatsiaris
Keyword(s):  
Activity Recognition ◽  
Activity Patterns ◽  
Context Aware

scholarly journals Smartphone-Based Activity Recognition in a Pedestrian Navigation Context

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3243
Author(s):  
Robert Jackermeier ◽  
Bernd Ludwig
Keyword(s):  
Activity Recognition ◽  
Recognition Performance ◽  
Activity Patterns ◽  
Detailed Knowledge ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
User Activity ◽  
Device Placement ◽  
Locomotion Behavior ◽  
Current Context

In smartphone-based pedestrian navigation systems, detailed knowledge about user activity and device placement is a key information. Landmarks such as staircases or elevators can help the system in determining the user position when located inside buildings, and navigation instructions can be adapted to the current context in order to provide more meaningful assistance. Typically, most human activity recognition (HAR) approaches distinguish between general activities such as walking, standing or sitting. In this work, we investigate more specific activities that are tailored towards the use-case of pedestrian navigation, including different kinds of stationary and locomotion behavior. We first collect a dataset of 28 combinations of device placements and activities, in total consisting of over 6 h of data from three sensors. We then use LSTM-based machine learning (ML) methods to successfully train hierarchical classifiers that can distinguish between these placements and activities. Test results show that the accuracy of device placement classification (97.2%) is on par with a state-of-the-art benchmark in this dataset while being less resource-intensive on mobile devices. Activity recognition performance highly depends on the classification task and ranges from 62.6% to 98.7%, once again performing close to the benchmark. Finally, we demonstrate in a case study how to apply the hierarchical classifiers to experimental and naturalistic datasets in order to analyze activity patterns during the course of a typical navigation session and to investigate the correlation between user activity and device placement, thereby gaining insights into real-world navigation behavior.

scholarly journals Context-Aware Human Activity Recognition in Industrial Processes

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 134
Author(s):  
Friedrich Niemann ◽  
Stefan Lüdtke ◽  
Christian Bartelt ◽  
Michael ten Hompel
Keyword(s):  
Activity Recognition ◽  
Time Management ◽  
Recognition Performance ◽  
Context Information ◽  
Context Aware ◽  
Recognition Model ◽  
Movement Data ◽  
Information Activity ◽  
The Subject ◽  
Motion Capturing

The automatic, sensor-based assessment of human activities is highly relevant for production and logistics, to optimise the economics and ergonomics of these processes. One challenge for accurate activity recognition in these domains is the context-dependence of activities: Similar movements can correspond to different activities, depending on, e.g., the object handled or the location of the subject. In this paper, we propose to explicitly make use of such context information in an activity recognition model. Our first contribution is a publicly available, semantically annotated motion capturing dataset of subjects performing order picking and packaging activities, where context information is recorded explicitly. The second contribution is an activity recognition model that integrates movement data and context information. We empirically show that by using context information, activity recognition performance increases substantially. Additionally, we analyse which of the pieces of context information is most relevant for activity recognition. The insights provided by this paper can help others to design appropriate sensor set-ups in real warehouses for time management.

scholarly journals Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

2019 ◽  
Vol 11 (21) ◽  
pp. 2531 ◽  
Author(s):  
Zhiqiang Gao ◽  
Dawei Liu ◽  
Kaizhu Huang ◽  
Yi Huang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Learning Strategy ◽  
Recognition Performance ◽  
Body Position ◽  
Human Activity Recognition ◽  
Motion Sensors ◽  
Context Aware ◽  
User Identification ◽  
Additional Information

Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of 0.9174 in terms of F 1 -score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by 5.1 % and 9.6 % respectively.

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