Raw data generator software#
However, those publications did not focus on GGIR as a generic piece of scientific software that connects all these algorithms and adds a range of essential extra functionalities (e.g., time zone and daylight-saving time handling) to provide value to an entire research community far beyond the specificities of those studies. Previous publications related to GGIR focused on specific algorithms, such as sleep detection, and their validity. However, scientific software instruments need to be subjected to a peer review process as it is the case for other methodological components (e.g., algorithms and study protocols). The fast advances in wearable sensor technology over the last decades comes with the price of mandatory development of scientific software to ensure a good valorization of the newly available sensors ( Seinstra, Wallom, & Keahey, 2015). The individual algorithms that are embedded in GGIR have been described across a number of published papers. The GGIR software presented in this paper facilitates the processing and extraction of insightful physical activity and sleep variables of the data collected with these so called raw data accelerometers from three widely used sensor brands ( Wijndaele et al., 2015). Many physical activity and sleep researchers do not have the expertise necessary to process and analyze raw accelerometer data. However, this technological advance is counterbalanced by the large amount of data collected per measurement (typically 2♱0 8 data points per week of measurement) and the necessity to process the data to obtain meaningful variables that can be used in standard statistical analysis and software. The data recorded are typically expressed in gravitational acceleration ( g) because this is the reference point for acceleration value calibration, reflecting both the movement and gravitational component ( van Hees et al., 2013).
Raw data generator Offline#
However, following a general movement towards more transparent and open science, and thanks to technological evolution towards smaller, cheaper, and power efficient sensors, accelerometers now tend to store ‘raw’ data for offline processing and analysis. In the beginning, wearable movement sensors (i.e., accelerometers) typically performed onboard signal processing and only stored derived output to reduce battery consumption and memory requirements. Physical activity and sleep have traditionally been quantified with diaries and questionnaires, but wearable sensors have gained momentum since the 1990s. Human physical activity and sleep are popular areas of research because of their important role in health outcomes ( He, Zhang, Li, Dai, & Shi, 2017 Lee et al., 2012). The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research. GGIR has been used in over 90 peer-reviewed scientific publications to date. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R.
Raw data generator full#
The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. However, it has also shifted the need for considerable processing expertise to the researcher. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers.
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