In this paper we propose a novel approach for validating a simulation model for a passengers' airport terminal. The validation approach is based on a "historical data" and "model-to-model" validation approach, and the novelty is represented by the fact that the model used as comparison uses historical data from different data sources and technologies. The proposed validation approach , which is presented as part of the IMHOTEP project, implements various data fusion and data analytics methods to generate the passenger "Activity-Travel-Diary", which is the model that is then compared with the results from the simulation model. The data used for developing the "Activity-Travel-Diary" comes from different sources and technologies such as: passengers data (personal mobile phone, apps), airport data (airport Wi-Fi, GPS, scanning facilities), and flight Information (flight schedules, gate allocation etc.). The simulation model is based on an agent-based simulation paradigm and includes all the passengers flows and operations within a terminal airport. The proposed validation approach is implemented in a real-life case study, Palma de Mallorca Airport, and preliminary results of the validation (calibration) process of the simulation model are presented.
Objective: To determine content validity of the Muscle Power Sprint Test (MPST) and construct validity and reliability of the MPST, 10x5 Meter Sprint Test (10x5MST), slalom test and one stroke push test (1SPT) in wheelchair-using youth with spina bifida (SB). Design: Clinimetric study Setting: Rehabilitation centers, SB outpatient services, private practices Participants: A convenience sample of 53 children (5-19 years, 32 boys / 21 girls) with SB who use a manual wheelchair. Participants were recruited in the Netherlands through rehabilitation centers, SB outpatient services, pediatric physical therapists and the BOSK (Association of and by parents of children, adolescents and adults with a disability). Interventions: Not applicable. Main Outcome Measures: Construct validity of the the MPST was determined by comparing results with the arm-cranking Wingate Anaerobic test (WAnT) using paired t-tests and Pearson Correlation Coefficients, while content validity was assessed using time based criteria for anaerobic testing . Construct validity of the 10x5MST, slalom test and 1SPT was analyzed by hypothesis testing using Pearson Correlation Coefficients and Multiple Regression. For reliability, Intra Class Correlation coefficients (ICC) and smallest detectable changes (SDC) were calculated. Results: For the MPST, mean exercise time of four sprints was 28.1 sec. (±6.6 sec.). Correlations between the MPST and WAnT were high (r>0.72, p<0.01). Excellent correlations were found between the 10x5MST and slalom test (r=0.93, p<0.01), while correlations between the10x5MST or slalom test and MPST and 1SPT were moderate (r=-0.56- -0.70; r=0.56, p<0.01). The 1SPT was explained for 38% by wheelchair mass (Beta -0.489) and total upper muscle strength (Beta 0.420). All ICCs were excellent (ICC>0.95) but the SDCs varied widely. Conclusions: The MPST, 10x5MST and slalom test are valid and reliable tests in wheelchair-using youth with SB for measuring respectively anaerobic performance or agility. For the 1SPT, both validity and reliability are questionable.
Due to a lack of transparency in both algorithm and validation methodology, it is diffcult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (+/- 10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.
