Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes

Brondeel, Ruben; Pannier, Bruno; Chaix, Basile

doi:10.1249/mss.0000000000000704

Cited by 40 publications

(42 citation statements)

References 35 publications

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“…Previous studies indicated that utilizing GPS devices is a practical method to accurately estimate humans' locomotion speed [26][27][28][29][30]. While adding GPS data (i.e., speed) to accelerometer data increases transport mode detection performance when differentiating between active and passive modes of transport [24,[31][32][33], these studies rarely included different types of walking or cycling activities or different sub-types of the stationary class such as sitting, standing and lying. Although studies have included GPS speed to improve PA type detection for more fine-grained activities [5,13,34], they have a number of limitations that still have to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

Allahbakhshi

Conrow

Naimi

et al. 2020

Sensors

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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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Section: Introductionmentioning

confidence: 99%

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

Allahbakhshi

Conrow

Naimi

et al. 2020

Sensors

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“…As an empirical illustration, the present study relied on GPS tracking and on a mobility survey to precisely assess places visited over 7 days. 8,13,14 The resulting ability to disentangle truly residential from nonresidential effects allowed us to demonstrate the existence of a major generator of confounding that we refer to as the "residential" effect fallacy (we use quotes to put into question the residential nature of the underlying effect), quantify its magnitude, and correct for it. As an illustration, we focus on the well-known hypothesis that the residential accessibility to services fosters transport walking.…”

Section: Introductionmentioning

confidence: 99%

The “Residential” Effect Fallacy in Neighborhood and Health Studies

et al. 2017

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“…Numerous studies have relied on accelerometers to derive objective measures of physical activity (12,13). However, studies were less successful in linking transport behavior with physical activity because identifying trips with their exact start and end times is required to perform this linkage (5,14,15). Unfortunately, study designs including trip recognition and accelerometer data collection often result in datasets with very precise measures but with limited sample sizes.…”

Section: Introductionmentioning

confidence: 99%

Associations of socioeconomic status with transport-related physical activity: combining a household travel survey and accelerometer data using random forests

Brondeel

Pannier²,

Chaix

2016

Journal of Transport & Health

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International audienceBackgroundSocioeconomic disparities in active transport have been documented in household travel surveys. However, active transport in these studies was operationalized with self-reported measures, which poorly approximate physical activity. Unfortunately, objective accelerometer data are very expensive to obtain in large-scale travel studies.PurposeTo benefit from a large sample and objective physical activity data, this study linked a cross-sectional household travel survey with accelerometer data from a small sample to investigate the association between socioeconomic disadvantage and the daily level of transport-related moderate-to-vigorous physical activity (T-MVPA) in an adult population (35–83 years).MethodsAccelerometer data for participants’ trips over 7 days from the RECORD GPS Study (7138 trips, 229 participants) were combined with information on participants’ trips over 1 day from the Global Transport Survey (Enquête Globale Transport, EGT) (82084 trips, 21332 participants). Trip-level T-MVPA data from the RECORD sample were used to train a random forests prediction model, enabling the prediction of T-MVPA for each participant׳s trip from EGT. The associations between socioeconomic indicators and daily T-MVPA were analyzed with negative binomial regression models.ResultsAn average time of 18.9 min (95% confidence interval: 18.6–19.2) of T-MVPA was found for these 35–83 year old adults. The education level had a positive association with T-MVPA. Household income had a negative association with T-MVPA, especially for those people without a motorized vehicle.ConclusionsThis study developed a methodology exporting precise sensor-based knowledge to a large survey sample to shed light on population-level socioeconomic disparities in transport-related physical activity

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Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes

Cited by 40 publications

References 35 publications

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

The “Residential” Effect Fallacy in Neighborhood and Health Studies

Associations of socioeconomic status with transport-related physical activity: combining a household travel survey and accelerometer data using random forests

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