Accurate position and orientation independent step counting algorithm for smartphones

2018 ◽  
Vol 10 (6) ◽  
pp. 481-495 ◽  
Author(s):  
Jungryul Seo ◽  
Teemu H. Laine
2010 ◽  
Vol 3 ◽  
pp. MEI.S3748 ◽  
Author(s):  
Tom Mikael Ahola

The Nokia Wrist–Attached Sensor Platform (NWSP) was developed at the Nokia Research Center during the NUADU project to facilitate research and demonstrations of use cases of wearable wireless sensors. A wrist–worn pedometer application was implemented as one of the demonstrations of the capabilities of the platform. In this paper the step counting algorithm is described and the performance is evaluated. The application is targeted for running exercise. However, the detection of steps during walking is also discussed.


2015 ◽  
Vol 9 (4) ◽  
pp. 211-224 ◽  
Author(s):  
Qingchi Zeng ◽  
Biao Zhou ◽  
Changqiang Jing ◽  
Nammoon Kim ◽  
Youngok Kim

2018 ◽  
Vol 5 ◽  
pp. 205566831880497 ◽  
Author(s):  
Arad Lajevardi-Khosh ◽  
Ben Tresco ◽  
Ami Stuart ◽  
Sarina Sinclair ◽  
Matt Ackerman ◽  
...  

Sensors ◽  
2018 ◽  
Vol 18 (1) ◽  
pp. 297 ◽  
Author(s):  
Xiaomin Kang ◽  
Baoqi Huang ◽  
Guodong Qi

2012 ◽  
Vol 479-481 ◽  
pp. 1931-1935
Author(s):  
Hai Tao Wang ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding

In the paper, both stereo vision and positional solution are used to measure the position and orientation of 6-DOF parallel robot. Firstly, moving platform is segmented from the background based on temporal and spatial union algorithm, rough position and orientation of moving platform are obtained by feature point extraction and match; secondly, rough length of supported leg is calculated according to rough position and orientation of moving platform; finally, accurate position and orientation of parallel robot are calculated using both actual length measured by sensors and positional solution. Experiment results demonstrate that the position and orientation calculated by the above algorithm have high accuracy.


2020 ◽  
Author(s):  
Runze Yang ◽  
Jian Song ◽  
Baoqi Huang ◽  
Wuyungerile Li ◽  
Guodong Qi

Abstract Step counting is not only the key component of pedometers (which is a fundamental service on smartphones), but is also closely related to a range of applications, including motion monitoring, behavior recognition, indoor positioning and navigation. Due to the limited battery capacity of current smartphones, it is of great value to reduce the energy consumption of such a popular service. Therefore, this paper focuses on the energy efficiency of step-counting algorithms. First of all, we formulate a theoretical error model based on the well-known auto-correlation coefficient step-counting (ACSC) algorithm, so as to analyze the factors affecting step-counting accuracy. And then, in light of this model and an adaptive sampling strategy, we propose a novel energy-efficient step-counting algorithm by adaptively substituting the computationally intensive auto-correlation with simple mean absolute deviation. On these grounds, an Android pedometer is implemented. Two individual experiments are carried out and verify both the theoretical error model and the proposed algorithm. It is shown that our algorithm outperforms two famous counterparts, i.e. the original ACSC algorithm and peak detection step-counting algorithm, in terms of both accuracy and energy efficiency.


Author(s):  
Sikha Bagui ◽  
Xingang Fang ◽  
Subhash Bagui ◽  
Jeremy Wyatt ◽  
Patrick Houghton ◽  
...  

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