Dataset Information

Kids motor performances datasets.

ABSTRACT: These datasets described the data of the Motor Performance Index for 7 years old kids in Malaysia based on Malaysia's physical fitness test SEGAK. This database has been designed and created with data analysis to create the index from the factor and variable of the test and the test was conducted in the majority of the national primary school in Malaysia. Gender, state of origin, and residential location of the school were the factors used to categorize the participant of the test. The factor of age, weight, height, body mass index (BMI), power, flexibility, coordination, and speed were used for the measurement to relate with the participant's physical fitness. Kids Motor Performances Index data can be reused for talent identification in sport talent scout and to create a baseline for kid's biology growth specifically in gross motor skills and cognitive growth measurement.

SUBMITTER: Maliki ABHM

PROVIDER: S-EPMC7744643 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Kids motor performances datasets.

Maliki Ahmad Bisyri Husin Musawi ABHM Abdullah Mohamad Razali MR Nadzmi Ahmad A Zainoddin Mohamad Amirur Rafiqi MAR Puspitasari Intan Meily IM Jibril Nur Faizatul Amira NFA Nawi Nur Amirah NA Mat-Rasid Siti Musliha SM Musa Rabiu Muazu RM Suhaili Zarizal Z Kamarudin Noor Aishah NA Ali Syed Kamaruzaman Syed SKS

Data in brief 20201130

These datasets described the data of the Motor Performance Index for 7 years old kids in Malaysia based on Malaysia's physical fitness test SEGAK. This database has been designed and created with data analysis to create the index from the factor and variable of the test and the test was conducted in the majority of the national primary school in Malaysia. Gender, state of origin, and residential location of the school were the factors used to categorize the participant of the test. The factor of ...[more]

PMID: 33354597

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Project description:The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.

Dataset Information

Kids motor performances datasets.

Publications

Kids motor performances datasets.

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