Evidence map›Paper›PMID 35813443›Full record

ArticleComputational and mathematical methods in medicine2022

Wearable Running Training Data Acquisition System Based on Intelligent Computer Technology.

Ruirui Zhang, Zhao Liu, Zhansheng Chang, Yongqing Cao, Dan Liu

Abstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ruirui ZhangSchool of Computer Science and Engineering, Cangzhou Normal University, Cangzhou 061001, China.
Zhao LiuSchool of Computer Science and Engineering, Cangzhou Normal University, Cangzhou 061001, China.ORCID https://orcid.org/0000-0003-0686-5382
Zhansheng ChangSchool of Computer Science and Engineering, Cangzhou Normal University, Cangzhou 061001, China.ORCID https://orcid.org/0000-0001-6061-7385
Yongqing CaoSchool of Computer Science and Engineering, Cangzhou Normal University, Cangzhou 061001, China.ORCID https://orcid.org/0000-0001-7139-5650
Dan LiuSchool of Computer Science and Engineering, Cangzhou Normal University, Cangzhou 061001, China.ORCID https://orcid.org/0000-0002-6986-4195

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Because the instantaneous motion of running training changes rapidly, the acquisition accuracy is low. Therefore, a wearable running training data acquisition system based on intelligent computer technology is designed. In the hardware design of the system, by setting the registers of the main control chip, inertial sensor, and magnetic sensor, the acceleration and angular velocity information of motion exist in the data format of binary complement. Bluetooth realizes the transparent transmission of data between hardware and software. The system software uses LDA recognition algorithm to decompose the dynamic data transmitted by Bluetooth into multiple static data and collect the final motion data through training. The experimental results show that the designed system has good quality and high accuracy in motion detection and counting.

Indexed as

RunningWearable Electronic DevicesAccelerationComputersHumansTechnology

Identifiers

PMID35813443
PMCPMC9270115

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.