Review



readysteady mobile app using the data from the built-in accelerometer of the ipod device  (APDM Wearable Technologies)

 
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    Structured Review

    APDM Wearable Technologies readysteady mobile app using the data from the built-in accelerometer of the ipod device
    Summary of hypotheses, endpoints, and instruments/comparisons that will be used to obtain endpoints.
    Readysteady Mobile App Using The Data From The Built In Accelerometer Of The Ipod Device, supplied by APDM Wearable Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/accelerometer+data/pmc07083754-0-81-91?v=APDM+Wearable+Technologies
    Average 90 stars, based on 1 article reviews
    readysteady mobile app using the data from the built-in accelerometer of the ipod device - by Bioz Stars, 2026-07
    90/100 stars

    Images

    1) Product Images from "ReadySteady intervention to promote physical activity in older adults with Parkinson's disease: Study design and methods"

    Article Title: ReadySteady intervention to promote physical activity in older adults with Parkinson's disease: Study design and methods

    Journal: Contemporary Clinical Trials Communications

    doi: 10.1016/j.conctc.2019.100513

    Summary of hypotheses, endpoints, and instruments/comparisons that will be used to obtain endpoints.
    Figure Legend Snippet: Summary of hypotheses, endpoints, and instruments/comparisons that will be used to obtain endpoints.

    Techniques Used: Comparison, Activity Assay



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    Image Search Results


    Devices used in the study: A wearable camera was used to provide visual confirmation of human behavior performed in the wild. We utilized the smartwatch tri-axial accelerometer and gyroscope to estimate energy expenditure using our proposed algorithm and compared it against multiple algorithms , , , , – using the hip-worn ActiGraph.

    Journal: Scientific Reports

    Article Title: Developing and comparing a new BMI inclusive energy expenditure algorithm on wrist-worn wearables

    doi: 10.1038/s41598-025-99963-0

    Figure Lengend Snippet: Devices used in the study: A wearable camera was used to provide visual confirmation of human behavior performed in the wild. We utilized the smartwatch tri-axial accelerometer and gyroscope to estimate energy expenditure using our proposed algorithm and compared it against multiple algorithms , , , , – using the hip-worn ActiGraph.

    Article Snippet: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K_m$$\end{document} motion estimates of accelerometer data segments are calculated by the Panasonic equation and aggregated based on the selected window size (Eq.).

    Techniques: