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• W3000856666 abstract "This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types." @default.
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• W3000856666 date "2020-01-21" @default.
• W3000856666 modified "2023-09-28" @default.
• W3000856666 title "Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection" @default.
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• W3000856666 doi "https://doi.org/10.3390/s20030588" @default.
• W3000856666 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7038120" @default.
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