Evidence map›Paper›PMID 39730411›Full record

ArticleScientific reports2024

Development of a wearable activity tracker based on BBC micro:bit and its performance analysis for detecting bachata dance steps.

Kemal Avci

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

1 author.

Kemal AvciDepartment of Electrical and Electronics Engineering, Izmir Democracy University, 35140, Izmir, Turkey. kemal.avci@idu.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rising popularity of wearable activity tracking devices can be attributed to their capacity for gathering and analysing ambient data, which finds utility across numerous applications. In this study, a wearable activity tracking device is developed using the BBC micro:bit development board to identify basic bachata dance steps. Initially, a pair of smart ankle bracelets is crafted, employing the BBC micro:bit board equipped with a built-in accelerometer sensor and a Bluetooth module for transmitting accelerometer data to smartphones. Subsequently, a dataset encompassing six core bachata dance steps synchronized to four beats is created from ten participants to examine the performance of the system. A metric using squared Euclidean distance is applied for the accelerometer raw data to facilitate and standardize the automatic detection of the steps by the system. A user interface, built with Python and Tkinter library, is developed to enable automatic step detection using the accelerometer dataset. The results demonstrated a system accuracy rate of 79.2%.

Indexed as

AccelerometryWearable Electronic DevicesAdultDancingFemaleFitness TrackersHumansMaleSmartphoneAccelerometerBachata dance stepsBBC:microbit boardWearable activity tracker

Identifiers

PMID39730411
PMCPMC11680584

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.