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

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.Peer reviewe

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

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.Peer reviewe

An optimization approach for equitable bicycle share station siting

Bicycle share systems are becoming an increasingly popular feature of many urban areas across the United States. While these systems aim to increase transit mode options as well as overall bicycle ridership, bike share programs also face challenges and criticisms related to density and inequitable distribution of services. Key factors in the success of bicycle share include high station density as well as services that reach a variety of neighborhoods, though many current systems do not reach low-income areas. Equitable station distribution therefore appears to be a complex problem to address. We propose utilizing spatial analytics, including GIS and spatial optimization, to help site bicycle share stations across an urban region. Specifically we seek to apply a covering model to assess how many bicycle stations are needed, and where they should be located, so no user would have to travel too far for access. The city of Phoenix, Arizona, is used as a case study to illustrate the coverage and access tradeoffs possible through different investment strategies. Accordingly, for a given investment level, the set of stations is identified that provides the best access to the designated bike path network for the greatest number of potential users. Further, tradeoff options that differentially favor either network or population coverage are possible, and can be identified and evaluated through the proposed analytical framework

Transport changes and COVID‐19: From present impacts to future possibilities

Changes in people's movement and travel behaviour have been apparent in many places during the COVID-19 pandemic, with differences seen at a range of spatial scales. These changes, occurring as a result of the COVID-19 ‘natural experiment’, have afforded us an opportunity to reimagine how we might move in our day-to-day travels, offering a hopeful glimpse of possibilities for future policy and planning around transport. The nature and scale of changes in movement and transport resulting from the pandemic have shown we can shift travel behaviour with strong policy responses, which is especially important in the concurrent climate change crisis

A conceptual framework for developing dashboards for big mobility data

Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets

Using accelerometer and GPS data for real-life physical activity type detection

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

Differences in Active Travel Between Immigrants in an Active and Less Active Mobility Culture

Despite growing investments in active travel infrastructure in many developed countries, walking and cycling rates often remain low. In addition to changes in the built environment, life experiences, place-specific urban mobility policies, and social and cultural norms with regard to active travel mode use are also found to be important factors for encouraging walking and cycling. Many researchers have examined immigrants’ travel behaviour to study the influence of social and cultural norms and place-specific factors on mode choice and travel decisions. However, knowledge of the differences in walking and cycling behaviour between various sub-groups of immigrants remains limited. By means of a multiple linear regression model, this study investigates differences in walking and cycling behaviours between immigrants in a less active travel culture, namely New Zealand, and an active travel culture, the Netherlands. The findings show that immigrants in both contexts walk and cycle more than the wider populations. Analysis results demonstrate that socio-demographic characteristics, car and bicycle access, and trip purpose all have a significant effect on active travel behaviour. Furthermore, on average, Dutch born-and-raised immigrants in New Zealand cycle more days per month than professional immigrants in the Netherlands and tend to use a much wider range of transport modes, particularly sharing services. These findings suggest that past experiences with particular travel modes and socialisation factors likely play a major role in active travel behaviour, thereby stressing the need for more research on the role of cultural and social norms in travel decision-making processes

MOBIlity assessment with modern TEChnology in older patients' real-life by the General Practitioner: The MOBITEC-GP study protocol

Background: Mobility limitations in older adults are associated with poor clinical outcomes including higher mortality and disability rates. A decline in mobility (including physical function and life-space) is detectable and should be discovered as early as possible, as it can still be stabilized or even reversed in early stages by targeted interventions. General practitioners (GPs) would be in the ideal position to monitor the mobility of their older patients. However, easy-to-use and valid instruments for GPs to conduct mobility assessment in the real-life practice setting are missing. Modern technologies such as the global positioning system (GPS) and inertial measurement units (IMUs) - nowadays embedded in every smartphone - could facilitate monitoring of different aspects of mobility in the GP's practice. Methods: This project's aim is to provide GPs with a novel smartphone application that allows them to quantify their older patients' mobility. The project consists of three parts: development of the GPS- and IMU-based application, evaluation of its validity and reliability (Study 1), and evaluation of its applicability and acceptance (Study 2). In Study 1, participants (target N = 72, aged 65+, ≥2 chronic diseases) will perform a battery of walking tests (varying distances; varying levels of standardization). Besides videotaping and timing (gold standard), a high-end GPS device, a medium-accuracy GPS/IMU logger and three different smartphone models will be used to determine mobility parameters such as gait speed. Furthermore, participants will wear the medium-accuracy GPS/IMU logger and a smartphone for a week to determine their life-space mobility. Participants will be re-assessed after 1 week. In Study 2, participants (target N = 60, aged 65+, ≥2 chronic diseases) will be instructed on how to use the application by themselves. Participants will perform mobility assessments independently at their own homes. Aggregated test results will also be presented to GPs. Acceptance of the application will be assessed among patients and GPs. The application will then be finalized and publicly released. Discussion: If successful, the MOBITEC-GP application will offer health care providers the opportunity to follow their patients' mobility over time and to recognize impending needs (e.g. for targeted exercise) within pre-clinical stages of decline

Using accelerometer and GPS data for real-life physical activity type detection

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