ArticleSensors (Basel, Switzerland)2020
Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Objectively measuring the association between the built environment and physical activity: a systematic review and reporting framework.The international journal of behavioral nutrition and physical activity · 2022Pooled it
- Feasibility and Preliminary Efficacy of a Foot-Ankle Exercise Program Aiming to Improve Foot-Ankle Functionality and Gait Biomechanics in People with Diabetic Neuropathy: A Randomized Controlled Trial.Sensors (Basel, Switzerland) · 2020Trial
- Effects of a device-measured physical activity intervention on visual outcomes, including contrast sensitivity, and glycaemic control in individuals with abnormal glucose tolerance: a pilot randomised trial post-CarMeR study.Pilot and feasibility studies · 2026Article
- Identification of a Person in a Trajectory Based on Wearable Sensor Data Analysis.Sensors (Basel, Switzerland) · 2024Article
- Multi-dimensional task recognition for human-robot teaming: literature review.Frontiers in robotics and AI · 2023Review
- Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke.Frontiers in physiology · 2022Article
- Charting everyday activities in later life: Study protocol of the mobility, activity, and social interactions study (MOASIS).Frontiers in psychology · 2022Article
- Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units.Sensors (Basel, Switzerland) · 2021Article
- DST-Predict: Predicting Individual Mobility Patterns From Mobile Phone GPS Data.IEEE access : practical innovations, open solutions · 2021Article
- Assessing the Transferability of Physical Activity Type Detection Models: Influence of Age Group Is Underappreciated.Frontiers in physiology · 2021Article
- Spanish Costaleros' Physical Activity and Their Quality of Life.Sensors (Basel, Switzerland) · 2020Article
- Objective Measures to Assess Active Commuting Physical Activity to School in Young People: A Systematic Review Protocol and Practical Considerations.International journal of environmental research and public health · 2020Article
- Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis.Sensors (Basel, Switzerland) · 2020Article
- Data Analytics and Applications of the Wearable Sensors in Healthcare: An Overview.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.