404,679 research outputs found

    Reliability and Precision of the Nana Protocol to Assess Body Composition Using Dual Energy X-ray Absorptiometry

    Get PDF
    The Nana positioning protocol is widely used to position participants to minimize technical error when undertaking body composition scanning and analysis with a Dual energy X-Ray absorptiometry (DXA) machine. Once biological and technical errors are accounted for, the only variation in test–retest results is from statistical fluctuation or machine error. Therefore, the aim of this study is to assess the test–retest reliability of the Nana positioning protocol and establish the smallest real difference percentage (SRD%). A gender-balanced group of 30 participants (15 males, 15 females) underwent two scans in succession using the Nana positioning protocol, with repositioning between scans. Percentage change in mean with typical error, Intraclass Correlation Coefficients (ICC), and standard error measurement percentage (SEM%) were used to identify the test–retest reliability and error rate of these protocols. Additionally, SRD% was calculated to assess the point at which clinically important changes occurred in a participant. The reliabilities of the whole body and regional scans were excellent. Percentage change in mean ranged between 0.00% and 0.23%. High reproducibility of the Nana positioning protocol was evident through an ICC ranging between 0.966–1.000. Additionally, typical error, SEM%, and SRD% were all low. Interestingly, fat mass was associated with the largest fluctuations observed to be associated with any of the parameters assessed. When all sources of biological and technical errors have been accounted for, the Nana positioning protocol has excellent test–retest reliability and produces low SEM% and SRD%

    Managing big data experiments on smartphones

    Get PDF
    The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

    Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

    Get PDF
    The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
    • …
    corecore