Identifying active travel behaviors in challenging environments using GPS, accelerometers and machine learning algorithms

Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper we present a supervised machine learning method for transportation mode prediction from GPS and accelerometer data. Methods: We collected a dataset of about 150 hours of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-minute windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Conclusions: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel

Context-specific outdoor time and physical activity among school-children across gender and age: Using accelerometers and GPS to advance methods

Introduction: Being outdoors has a positive influence on health among children. Evidence in this area is limited and many studies have used self-reported measures. Objective context-specific assessment of physical activity patterns and correlates, such as outdoor time, may progress this field.Aims: To employ novel objective measures to assess age and gender differences in context-specific outdoor weekday behavior patterns among school-children (outdoor time and outdoor MVPA) and to investigate associations between context-specific outdoor time and MVPA.Methods: A total of 170 children had at least one weekday of nine hours combined accelerometer and GPS data and were included in the analyses. The data were processed using the Personal Activity and Location Measurement System and a purpose-built PostgreSQL database resulting in context-specific measures for outdoor time, outdoor MVPA and overall daily MVPA. In addition, four domains (leisure, school, transport and home) and 11 subdomains (e.g. urban green space, sports facilities) were created and assessed. Multilevel analyses provided results on age and gender differences and the association between outdoor time and MVPA.Results: Girls compared to boys had fewer outdoors minutes (pConclusion:A new methodology to assess context-specific outdoor time and physical activity patterns has been developed and can be expanded to other populations. Different context-specific patterns were found for gender and age, suggesting different strategies may be needed to promote physical activit

Dynamic accuracy of GPS receivers for use in health research: a novel method to assess GPS accuracy in real-world settings

The emergence of portable global positioning system (GPS) receivers over the last 10 years has provided researchers with a means to objectively assess spatial position in free-living conditions. However, the use of GPS in free-living conditions is not without challenges and the aim of this study was to test the dynamic accuracy of a portable GPS device under real-world environmental conditions, for four modes of transport, and using three data collection intervals.We selected four routes on different bearings, passing through a variation of environmental conditions in the City of Copenhagen, Denmark, to test the dynamic accuracy of the Qstarz BT-Q1000XT GPS device. Each route consisted of a walk, bicycle and vehicle lane in each direction. The actual width of each walking, cycling and vehicle lane was digitized as accurately as possible using ultra-high-resolution aerial photographs as background. For each trip we calculated the percentage that actually fell within the lane polygon, and within the 2.5, 5 and 10 meter buffers respectively, as well as the mean and median error in meters.Our results showed that 49.6% of all ≈68,000 GPS points fell within 2.5 meters of the expected location, 78.7% fell within 10 meters and the median error was 2.9 m. The median error during walking trips was 3.9 m, 2.0 m for bicycle trips, 1.5 m for bus and 0.5 m for car. The different area types showed considerable variation in the median error: 0.7 m in open areas, 2.6 m in half-open areas and 5.2 m in urban canyons. The dynamic spatial accuracy of the tested device is not perfect, but we feel that it is within acceptable limits for larger population studies. Longer recording periods, for a larger population are likely to reduce the potentially negative effects of measurement inaccuracy. Furthermore, special care should be taken when the environment in which the study takes place could compromise the GPS signal.<br/

Potential applications of image-guided radiotherapy for brain metastases and glioblastoma to improve patient quality of life

Treatment of glioblastoma multiforme (GBM) and brain metastasis remains a challenge because of the poor survival and the potential for brain damage following radiation. Despite concurrent chemotherapy and radiation dose escalation, local recurrence remains the predominant pattern of failure in GBM most likely secondary to repopulation of cancer stem cells. Even though radiotherapy is highly effective for local control of radio-resistant tumors such as melanoma and renal cell cancer, systemic disease progression is the cause of death in most patients with brain metastasis. Preservation of quality of life of cancer survivors is the main issue for patients with brain metastasis. Image-guided radiotherapy (IGRT) by virtue of precise radiation dose delivery may reduce treatment time of patients with GBM without excessive toxicity and potentially improve neurocognitive function with preservation of local control in patients with brain metastasis. Future prospective trials for primary brain tumors or brain metastasis should include IGRT to assess its efficacy to improve patient quality of life