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This paper deals with the automatic classification of customers on the basis of their movements around a sports shop center. We start by collecting coordinates from customers while they visit the store. Consequently, any costumer's path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. A guess about the trajectory is constructed, and a number of parameters are calculated before performing a Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. We can also monitor the state of the shop, identify different situations that appear during limited periods of time, and predict peaks in customer traffic.
RESUMEN
BACKGROUND: An emergency waiting room is a place where conflicts often arise. Nervous relatives in a hostile, unknown environment force security and medical staff to be ready to deal with some awkward situations. Additionally, it has been said that the medical interview is the first diagnostic and therapeutic tool, involving both intellectual and emotional skills on the part of the doctor. At the same time, it seems that there is something mysterious about interviewing that cannot be formalized or taught. In this context, virtual conversational characters (VCCs) are progressively present in most e-learning environments. OBJECTIVE: In this study, we propose and develop a modular architecture for a VCC-based behavior simulator to be used as a tool for conflict avoidance training. Our behavior simulators are now being used in hospital environments, where training exercises must be easily designed and tested. METHODS: We define training exercises as labeled, directed graphs that help an instructor in the design of complex training situations. In order to increase the perception of talking to a real person, the simulator must deal with a huge number of sentences that a VCC must understand and react to. These sentences are grouped into sets identified with a common label. Labels are then used to trigger changes in the active node of the graph that encodes the current state of the training exercise. As a consequence, we need to be able to map every sentence said by the human user into the set it belongs to, in a fast and robust way. In this work, we discuss two different existing string metrics, and compare them to one that we use to assess a designed exercise. RESULTS: Based on the similarities found between different sets, the proposed metric provided valuable information about ill-defined exercises. We also described the environment in which our programs are being used and illustrated it with an example. CONCLUSIONS: Initially designed as a tool for training emergency room staff, our software could be of use in many other areas within the same environment. We are currently exploring the possibility of using it in speech therapy situations.
RESUMEN
Se realiza una evaluación de la técnica de enzimoinmunoanálisis de micropartículas (MEIA), para diagnóstico de toxoplasmosis, mediante estudio de 322 sueros correspondientes a pacientes sin infección, y con diferentes etapas de ella. Se seleccionaron estos sueros según los resultados obtenidos previamente mediante la aplicación de inmunofluorescencia indirecta, e inmunofluorescencia indirecta para IgM. Se aplicó MEIA IgM y MEIA IgM, procesándose mediante un sistema auto-analizador de inmunoensayo totalmente automatizado. Se obtuvo para MEIA IgG: sensibilidad 95,12 por ciento, especificidad 98,25 por ciento, predictibilidad positiva 97,50 por ciento y predictibilidad negativa 96,57 por ciento. Para MEIA IgM se obtuvo: sensibilidad 100 por ciento, especificidad 91,13 por ciento, predictibilidad positiva 56,14 por ciento y predictibilidad negativa 100 por ciento. Se concluye que la técnica MEIA, es un método sensible y específico, pero la baja predictibilidad positiva de la investigación de la IgM, no permite afirmar por sí sola la actividad de la infección, debiendo ser confirmada por otras técnicas, como la inmunofluorescencia IgM