Design of Multiple Spatial Context Detection Method Considering Elongated Top-Bounded Spaces Based on GPS Signal-To-Noise Ratio and Fuzzy Inference

Tabata, Kenichi; Nakajima, Madoka; Kohtake, Naohiko

doi:10.3390/ijgi9120717

Cited by 2 publications

(1 citation statement)

References 22 publications

(34 reference statements)

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“…Studies using GPS data have used algorithms relying on the GPS device's Signal to Noise Ratio (SNR), which is a measure of the magnitude of the signal received by the GPS tracker, as one of the key variables to classify time spent in indoor or outdoor environments [4,5]. The Personal Activity Location Measurement System (PALMS) algorithm also uses SNR to differentiate indoor and outdoor points [6][7][8][9][10]. Lam, et al [11] compared time spent outdoors through PALMS using a 250 SNR threshold and the in-vehicle trip detection against images captured from automated cameras, which resulted in 80.9% overall accuracy.…”

Section: Introductionmentioning

confidence: 99%

Quantifying time spent outdoors: A versatile method using any type of global positioning system (GPS) and accelerometer devices

Liu,

Chambers,

Clevenger

et al. 2024

PLoS ONE

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Spending time outdoors is associated with increased time spent in physical activity, lower chronic disease risk, and wellbeing. Many studies rely on self-reported measures, which are prone to recall bias. Other methods rely on features and functions only available in some GPS devices. Thus, a reliable and versatile method to objectively quantify time spent outdoors is needed. This study sought to develop a versatile method to classify indoor and outdoor (I/O) GPS data that can be widely applied using most types of GPS and accelerometer devices. To develop and test the method, five university students wore an accelerometer (ActiGraph wGT3X-BT) and a GPS device (Canmore GT-730FL-S) on an elastic belt at the right hip for two hours in June 2022 and logged their activity mode, setting, and start time via activity diaries. GPS trackers were set to collect data every 5 seconds. A rule-based point cluster-based method was developed to identify indoor, outdoor, and in-vehicle time. Point clusters were detected using an application called GPSAS_Destinations and classification were done in R using accelerometer lux, building footprint, and park location data. Classification results were compared with the submitted activity diaries for validation. A total of 7,006 points for all participants were used for I/O classification analyses. The overall I/O GPS classification accuracy rate was 89.58% (Kappa = 0.78), indicating good classification accuracy. This method provides reliable I/O clarification results and can be widely applied using most types of GPS and accelerometer devices.

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