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Tag Archives: datasets
Internalizing and externalizing behavior 2023 Best
This study is to investigate whether head start program would affect child’s internalizing and externalizing behavior (teacher reported behavior problem) longitudinally.
Internalizing and externalizing behavior
Instruction: (This study is to investigate whether a child participated in Head start program before kindergarten or not would affect children’s internalizing and externalizing behavior (teacher reported behavior problem) longitudinally, and the age of child who started head start program would affect children’s internalizing and externalizing behavior longitudinally) Please see file named outline, variables and other resources. 1. title: Head Start Program and the trajectory of children’s internalizing and externalizing behavior problems: A latent growth model analysis 2. Method a. Datasets and Participants.
Internalizing and externalizing behavior
I. The Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K:1998) data set followed a nationally representative sample of children who were in kindergarten in fall 1998 and who continued to be followed to their early school years (kindergarten through Grade 5). The study was conducted by the National Center for Education Statistics (NCES) within the Institute of Education Sciences (IES) of the U.S. Department of Education. This data was merged with the ECLS-K Base Year Head Start data which contains data that were collected to verify the participation of children in the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 sample who were identified by their parents and/or schools as having attended Head Start the year prior to kindergarten.
Internalizing and externalizing behavior
First, it was determined whether the program that was identified was a Head Start program. Second, it was determined whether the child attended the program. ii. The current analyses used data collected during fall and spring of both kindergarten and first grade. The primary outcome, children’s internalizing and externalizing behavior was measured with direct assessments in fall and spring of each year. The analytic sample is restricted to English-speaking children; the # children who did not pass a language screener and whose home language was not English were not included in the analyses.
Internalizing and externalizing behavior
b. Measures i. Behavior Problems ii. Head Start program iii. Age of Starting Head Start Program (Start age) iv. Covariates (control variables) c. Analytical Approach i. Latent Growth model. Please read variables and lit review 1. Please see preacher_2010. This explains what each variable plays a role in the model (e.g., control variables) . I need explanation of how these variables work in this model. Please explain why those are selected as control variables. And it needs explanation of what each control variable means in this study. https://youtu.be/DKVW_tiDUIg
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Electromyography feature extraction. 2022 Best
This paper explores Electromyography feature extraction. Processing of EMG signals in order to collect specific Features (specifically IEMG, RMS, WL, WAMP, ENERGY) in the Matlab environment, as shown in the attached paper.
Electromyography feature extraction.
Processing of EMG signals in order to collect specific Features (specifically IEMG, RMS, WL, WAMP, ENERGY) in the Matlab environment, as shown in the attached paper. Signals must be drawn from datasets that already exist online. The code corresponding to each Feature already exists. There are two goals at work: 1. To create a dataset that will contain the processed EMG signals 2. a dataset that will contain the features. The order is as follows: Raw EMG Signal -> Preprocessing and Data Segmentation -> Feature Extraction. The ideal case would be to find data both from healthy individuals, when and from individuals with mobility problems or individuals with spinal cord injury.
Electromyography feature extraction.
There is no limitation to a muscle unit, i.e. the signals can be from both hand and leg movements. The data should concern muscle activity of people with and without a spinal cord problem. Data can be pulled from github, kaggle and other sources like researchgate. I am sending you indicative links: 1. https://www.kaggle.com/datasets/nccvector/electromyography-emg-dataset 2. https://github.com/Suguru55/Wearable_Sensor_Long-term_sEMG_Dataset The data is not required to be from a specific muscle unit, i.e. one dataset could have data from arm muscles and another from thigh muscles.
Electromyography feature extraction.
However, muscle activity data should also be found from people with motor/muscle problems and the same procedure as the rest of the data should be followed. The data should be entered into matlab, preprocessing should be done [indicatively the signal was band pass filtered using 2nd order Butterworth filter with a cut off frequency of 20-500 Hz and further rectified before segmentation] and a dataset should be created with all processed signals. https://youtu.be/ucwtJ28Yia0
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