The Staga-Dataset: Stop and Trip Annotated GPS and Accelerometer Data of Everyday Life

Spang, Robert P.; Pieper, K.; Oesterle, B.; Brauer, Max; Haeger, Christine; Mümken, Sandra Angelika; Gellert, Paul; Voigt-Antons, J.-N.

doi:10.5194/isprs-archives-xlviii-4-w1-2022-443-2022

Cited by 5 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To evaluate the performance of our classification system, we needed an accurately labelled dataset containing real GPS traces and annotations about the stops and trips of the person recording the data. The STAGA-Dataset was created to evaluate the classification performance of stop/trip detection algorithms (Spang et al, 2022). This dataset provides smartphone recorded GPS data (0.1Hz), including accelerometer readings (1Hz).…”

Section: Datasetmentioning

confidence: 99%

Making Sense of the Noise: Integrating Multiple Analyses for Stop and Trip Classification

Spang

Pieper

Oesterle

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Mobility research is mainly concerned with understanding mobility on a higher level, including environmental factors, e.g., measuring the time out of home or tracking revisited places. This requires preprocessing the raw data obtained from GPS sensors, like clustering significant locations and distinguishing these from periods on the go. We introduce a new stop and trip detection algorithm to transform a list of position records into intervals of dwelling and transit. The system is based on geometrical analyses of the signal noise: Imperfect GPS data tends to scatter around an actual dwell position in a star-like pattern, and this imperfection is what we leverage for our classification. The system contains four independent classification methods, comparing different aspects of the geometrical properties of a given trajectory. If available, accelerometer readings can be used to improve the system’s accuracy further. To evaluate the classifier’s performance, we recorded a large dataset containing gold-standard labels and compared the classification results of our system with the results of Scikit Mobility and Moving Pandas. Our Stop Go Classifier outperforms the traditional distance/time-threshold-based systems. The described system is available as free software.

show abstract

Section: Datasetmentioning

confidence: 99%

Making Sense of the Noise: Integrating Multiple Analyses for Stop and Trip Classification

Spang

Pieper

Oesterle

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The test data set [32], used to evaluate the classification data set, was recorded using the described GPS.Rec2.0 app. The data set contains GPS and acceleration records as well as stop/trip annotations.…”

Section: Code Availabilitymentioning

confidence: 99%

Smartphone Global Positioning System–Based System to Assess Mobility in Health Research: Development, Accuracy, and Usability Study

Spang¹,

Haeger²,

Mümken³

et al. 2023

JMIR Rehabil Assist Technol

View full text Add to dashboard Cite

Background As global positioning system (GPS) measurement is getting more precise and affordable, health researchers can now objectively measure mobility using GPS sensors. Available systems, however, often lack data security and means of adaptation and often rely on a permanent internet connection. Objective To overcome these issues, we aimed to develop and test an easy-to-use, easy-to-adapt, and offline working app using smartphone sensors (GPS and accelerometry) for the quantification of mobility parameters. Methods An Android app, a server backend, and a specialized analysis pipeline have been developed (development substudy). Parameters of mobility by the study team members were extracted from the recorded GPS data using existing and newly developed algorithms. Test measurements were performed with participants to complete accuracy and reliability tests (accuracy substudy). Usability was examined by interviewing community-dwelling older adults after 1 week of device use, followed by an iterative app design process (usability substudy). Results The study protocol and the software toolchain worked reliably and accurately, even under suboptimal conditions, such as narrow streets and rural areas. The developed algorithms had high accuracy (97.4% correctness, F1-score=0.975) in distinguishing dwelling periods from moving intervals. The accuracy of the stop/trip classification is fundamental to second-order analyses such as the time out of home, as they rely on a precise discrimination between the 2 classes. The usability of the app and the study protocol was piloted with older adults, which showed low barriers and easy implementation into daily routines. Conclusions Based on accuracy analyses and users’ experience with the proposed system for GPS assessments, the developed algorithm showed great potential for app-based estimation of mobility in diverse health research contexts, including mobility patterns of community-dwelling older adults living in rural areas. International Registered Report Identifier (IRRID) RR2-10.1186/s12877-021-02739-0

show abstract

“…No estudo de [Spang et al 2022b], um conjunto de dados de trajetórias GPS com anotac ¸ões de pontos de parada foi registrado por meio de um aplicativo durante 126 dias. Os usuários do aplicativo, que eram voluntários com idades entre 25 e 35 anos, trabalhavam em tempo integral de forma presencial e residiam principalmente em áreas urbanas da Europa.…”

Section: Aquisic ¸ãO Dos Dados Ou Conjunto De Dadosunclassified

Uma análise comparativa de técnicas de detecção de pontos de parada em ambientes urbanos

Oliveira,

Celes,

Oliveira

et al. 2023

Anais Da XI Escola Regional De Computação Do Ceará, Maranhão E Piauí (ERCEMAPI 2023)

View full text Add to dashboard Cite

Este artigo apresenta um framework para a criação de conjuntos de dados de referência (ground-truth) destinados à detecção automatizada de pontos de parada. O framework utiliza dados do OpenStreetMap e o SUMO (Simulation of Urban MObility) como fontes de informação essenciais. Além disso, são implementados e comparados métodos amplamente discutidos na literatura para a detecção de pontos de parada, utilizando conjuntos de dados gerados por meio desse framework. Os resultados da análise confirmam a confiabilidade dos métodos estudados. O estudo também introduz novos algoritmos à análise, que demonstram ser promissores na detecção de pontos de parada, além de identificar áreas para melhorias futuras. Destacam-se a necessidade de explorar análises adicionais que considerem métodos alternativos de aquisição de dados e avaliem seus impactos na detecção de pontos de parada.

show abstract

The Staga-Dataset: Stop and Trip Annotated GPS and Accelerometer Data of Everyday Life

Cited by 5 publications

References 5 publications

Making Sense of the Noise: Integrating Multiple Analyses for Stop and Trip Classification

Making Sense of the Noise: Integrating Multiple Analyses for Stop and Trip Classification

Smartphone Global Positioning System–Based System to Assess Mobility in Health Research: Development, Accuracy, and Usability Study

Uma análise comparativa de técnicas de detecção de pontos de parada em ambientes urbanos

Contact Info

Product

Resources

About