Ubicomp researchers have given the importance to the use of the technique LSA (Lag Sequential Analysis)[8, 7] to evaluate the impact of their systems. LSA is a technique for gathering quantitative data by observing users as they perform their normal activities. It is traditionally used in the field of developmental psychology to study the behavior of person to person interaction by measuring the number of times certain behaviors precede or follow a selected behavior; the behaviors are defined by the study evaluators. Datacan be captured live with paper and pencil or coded from video. Recent work of pervasive environments[2, 1] and social sciences [4, 6] shows that LSA is widely used for the analysis and evaluation, since it allows to capture the natural work of the user, also allows to obtain quantitative and statistical results from the data encoded.
An advantage of LSA is that it is conducted in the users environment, and it is conducted while the user performs his normal activities. With LSA, evaluators can generate statistics that capture aspects of observed behavior such as frequency and conditional probabilities of events. If video is used to capture the data, it can be recoded for different information as evaluation needs change, and it can be used for qualitative observational purposes. A significant disadvantage of LSA is cost; coding video for LSA is time consuming. There are many advantages for the use of LSA however there is a need to develop strategies to reduce the cost of its use .
This paper presents a tool that uses state of art computer vision techniques that allows the user to automate the process of encoding in LSA. Our aim is to augment LSA, reducing its burden, providing an specialized LSA. The tool presents a graphical interface for the user. How the tool works can be divided into four main steps: load video, select reference image, tracking process and classification. In the load video, the user manually selects a video. In the next main step, the user manually selects the image of the video where the behavior of interest is present. Then, a composite filter correlation  is used to process the reference image. At this point, the tool is ready to track the reference image in the video, this is done automatically by the tracking process; the TLD (Tracking Learning Detector)  tracking algorithm in computer vision is used for this process. When the TLD algorithm finds a possible match, the correlation peak threshold is used to determine a positive match between the processed reference image and possible image match.Finally, a time-stamp is registered at every positive match indicating that a behavior of interest occurred in that moment.
 Karina Caro. Exergames for children with motor skills problems. SIGACCESS Access. Comput.,(108):20–26, January 2014.
 S Consolvo, L Arnstein, and B R Franza. User study techniques in the design and evaluation of aubicomp environment. InIn proceedings of the 4th international conference on Ubiquitous Computing, 2002.
 Leopoldo. Gaxiola, Víctor Hugo Díaz-Ramírez, JuanJ. Tapia, Arnoldo Diaz-Ramirez, and Vitaly Kober.Robust face tracking with locally-adaptive correlation filtering. In Eduardo Bayro-Corrochano and Edwin Hancock, editors,Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, volume 8827 ofLecture Notes in Computer Science, pages 925–932. Springer International Publishing, 2014.
 Philip L. Gunter, Susan L. Jack, Richard E. Shores, Debra E. Carrell, and Julia Flowers. Lag sequentialanalysis as a tool for functional analysis of student disruptive behavior in classrooms.Journal ofEmotional and Behavioral Disorders, 1(3):138–148, 1993.
 Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas. Tracking-learning-detection.IEEE Trans. PatternAnal. Mach. Intell., 34(7):1409–1422, July 2012.
 Florian E. Klonek, Nale Lehmann-Willenbrock, and Simone Kauffeld. Dynamics of resistance to change:A sequential analysis of change agents in action.Journal of Change Management, 14(3):334–360, 2014.
 G P Sackett. Lag sequential analysis as a data reduction technique in social interaction research. InIn, pages 300–340. Brunner/Mazel, 1980.
 Gene P. Sackett, Richard Holm, Charles Crowley, and Allen Henkins. A fortran program for lag sequential analysis of contingency and cyclicity in behavioral interaction data.Behavior Research Methods and Instrumentation, 11(3):366–378, 1979.
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- Victor Lopez-Lopez