Про один підхід до ідентифікації особи за контуром профілю носа

У статті розглядається підхід для ідентифікації особи за контуром профілю носа. Для знаходження простору ознак пропонується використовувати апроксимацію за допомогою B-сплайнів. Алгоритмічна реалізація підходу використовує моделювання структури бази даних у вигляді n- вимірного куба.В статье рассматривается подход для идентификации личности по контуру носа. Для нахождения пространства признаков предлагается использовать аппроксимацию с помощью B-сплайнов. Алгоритмическая реализация подхода использует моделирование структуры базы данных в виде n-мерного куба.An approach of human identification by the nose contour profile is researched in the paper. B-splines approximation is suggested for determining the feature space. Algorithmic implementation of the approach uses database structure in the form of n-dimensional cube

Rasch Analysis of the International Quality of Life Basic Data Set Version 2.0

Objective: To examine the internal construct validity of the International Spinal Cord Injury Quality of Life Basic Data Set Version 2.0 (QoL-BDS V2.0) and compare this with the internal construct validity of the original version of the QoL-BDS. Design: International cross-sectional psychometric study. Setting: Spinal rehabilitation units, clinics, and community. Participants: The study involved 5 sites and 4 countries, 2 of whose primary language is not English. Each site included a consecutive sample of inpatients with spinal cord injury or disease (SCI/D) and a convenience sample of individuals with SCI/D living in the community (N=565). Main outcome measures: The QoL-BDS V2.0 consists of the 3 original items on satisfaction with life as a whole, physical health, psychological health of the QoL-BDS, and an additional item on satisfaction with social life. All 4 items are answered on a 0-10 numeric rating scale. Rasch analysis was performed on versions 1.0 and 2.0 of the QoL-BDS to examine the ordering of the items' response options, item scaling, reliability, item fit, local item independence, differential item functioning, and unidimensionality. Results: The sample included 565 participants with 57% outpatients and 43% inpatients. Mean age was 51.4 years; 71% were male; 65% had a traumatic injury, 40% had tetraplegia, and 67% were wheelchair users. Item thresholds were collapsed for ordering, and subsequent analyses showed good internal construct validity for the QoL-BDS V2.0 with a person separation reliability of 0.76 and Cronbach α of 0.81. Infit and outfit statistics ranged 0.62-0.91. No local dependencies and multidimensionality were found. Differential item functioning was observed only for country and inpatients vs outpatients but not for other participants' characteristics. Differences in internal construct validity between the 3-item and 4-item versions were minimal. Conclusions: The results of this Rasch analysis support the internal construct validity of the QoL-BDS V2.0

Budget impact analysis of robotic exoskeleton use for locomotor training following spinal cord injury in four SCI Model Systems

Background We know little about the budget impact of integrating robotic exoskeleton over-ground training into therapy services for locomotor training. The purpose of this study was to estimate the budget impact of adding robotic exoskeleton over-ground training to existing locomotor training strategies in the rehabilitation of people with spinal cord injury. Methods A Budget Impact Analysis (BIA) was conducted using data provided by four Spinal Cord Injury (SCI) Model Systems rehabilitation hospitals. Hospitals provided estimates of therapy utilization and costs about people with spinal cord injury who participated in locomotor training in the calendar year 2017. Interventions were standard of care walking training including body-weight supported treadmill training, overground training, stationary robotic systems (i.e., treadmill-based robotic gait orthoses), and overground robotic exoskeleton training. The main outcome measures included device costs, training costs for personnel to use the device, human capital costs of locomotor training, device demand, and the number of training sessions per person with SCI. Results Robotic exoskeletons for over-ground training decreased hospital costs associated with delivering locomotor training in the base case analysis. This analysis assumed no difference in intervention effectiveness across locomotor training strategies. Providing robotic exoskeleton overground training for 10% of locomotor training sessions over the course of the year (range 226–397 sessions) results in decreased annual locomotor training costs (i.e., net savings) between $1114 to$4784 per annum. The base case shows small savings that are sensitive to parameters of the BIA model which were tested in one-way sensitivity analyses, scenarios analyses, and probability sensitivity analyses. The base case scenario was more sensitive to clinical utilization parameters (e.g., how often devices sit idle and the substitution of high cost training) than device-specific parameters (e.g., robotic exoskeleton device cost or device life). Probabilistic sensitivity analysis simultaneously considered human capital cost, device cost, and locomotor device substitution. With probabilistic sensitivity analysis, the introduction of a robotic exoskeleton only remained cost saving for one facility. Conclusions Providing robotic exoskeleton for over-ground training was associated with lower costs for the locomotor training of people with SCI in the base case analyses. The analysis was sensitive to parameter assumptions