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.

Self-powered 5G NB-IoT system for remote monitoring applications

Nano Energy ◽  
2021 ◽  
pp. 106140
Author(s):  
Guosheng Hu ◽  
Zhiran Yi ◽  
Lijun Lu ◽  
Yang Huang ◽  
Yueqi Zhai ◽  
...  
Keyword(s):  
Remote Monitoring ◽  
Monitoring Applications ◽  
Self Powered

scholarly journals A Combined-activity Recognition Method with Accelerometers

2016 ◽  
Vol 24 (3) ◽  
pp. 512-521 ◽  
Author(s):  
Kazuya Murao ◽  
Tsutomu Terada
Keyword(s):  
Activity Recognition ◽  
Recognition Method

scholarly journals User Activity Recognition through Software Sensors

2017 ◽  
pp. 243-265 ◽  
Author(s):  
Stephan Reiff-Marganiec ◽  
Kamran Taj Pathan ◽  
Yi Hong
Keyword(s):  
Activity Recognition ◽  
Software Sensors ◽  
User Activity

scholarly journals User activity recognition system to improve the performance of environmental control interfaces: a pilot study with patients

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Arturo Bertomeu-Motos ◽  
Santiago Ezquerro ◽  
Juan A. Barios ◽  
Luis D. Lledó ◽  
Sergio Domingo ◽  
...  
Keyword(s):  
Pilot Study ◽  
Activity Recognition ◽  
Environmental Control ◽  
Recognition System ◽  
User Activity
Sign in / Sign up

Export Citation Format

Share Document