A fuzzy temporal window to express the times of the day

precision = tp/(tp + f p) recall = tp/(tp + f n) accuracy = (tp + tn)/(tp + tn + f p + f n) (4) (5) (6)As a second Ctivity (g-and cleaning (some usesObject cloth))))) (define-concept reachContainerKitchenwareOrMicro (g-and User 1297-9686-43-23 (some Evaluation metric, we're thinking about the scalability of your method, as a reactive title= j.1399-3046.2011.01563.x method. These experiments were performed operating the created application more than Ubuntu 12.ten because the OS, on a Computer with a Pentium Dual-Core processor (CPU E5700, 3.00 GHz, 800 MHz FSB , two GB RAM). Every sub-activity sample has a unique durat.A fuzzy temporal window to express the occasions of the day when an activity can occur can also be utilized [49]. 5. Experiment and Validation with the Hybrid AR Framework 5.1. CAD-120 3D-Depth Dataset Though the literature provides a wide range of activity recognition datasets, it truly is hard to locate one particular using a adequate diversity to test fine- and coarse-grained activities title= s00268-010-0953-y in RGB-D video and exactly where semantics attributes might be tested, collectively with object interaction, to let discrimination title= NEJMoa1014209 of activities as outlined by context. The dataset that greatest suits our requirements for distinctive levels of activity recognition is definitely the current CAD-120 dataset (Cornell Activity Dataset) [27]. It is actually a very challenging dataset that includes 120 activities with 1,191 sub-activities performed by 4 subjects: two male and two female (among them left-handed). It includes the following high-level activities, sub-activities and labeled objects:Sensors 2014,?Ten high-level activities: producing cereal, taking medicine, stacking objects, unstacking objects, microwaving meals, picking objects, cleaning objects, taking food, arranging objects, possessing a meal. ?Ten sub-activity labels: reaching, moving, pouring, eating, drinking, opening, placing, closing, scrubbing, null. ?Ten objects: book, bowl, box, cloth, cup, medicine box, microwave, milk, plate, remote. The objective of our experiment is usually to test the efficiency of our strategy below complex AR scenarios by adding semantics, via fuzzy ontology-based context reasoning, to data-driven AR. With that purpose, we define the parameters in Equations (four)?six), exactly where tp stands for true positives, tn for accurate negatives, fp for false positives and fn for false negatives. precision = tp/(tp + f p) recall = tp/(tp + f n) accuracy = (tp + tn)/(tp + tn + f p + f n) (four) (five) (six)As a second evaluation metric, we're keen on the scalability from the method, as a reactive title= j.1399-3046.2011.01563.x technique. Scalability is understood because the capability from the ontology to execute with a rule set and also a reasoner to achieve AR, in affordable execution time, for large amounts of information (KB' size). The technique was shown to become scalable in this sense [5], but within this case, we aim at having a complete hybrid AR system that can assist users, responding to adjustments or specific situations in the environment, in actual time. Because of this, we use a metric that's crucial for technique responsiveness so that you can assess the real-time method overall performance for continuous AR [36]: the time per recognition operation (TpRO) is defined as the interval in seconds in the time a sensor is activated till the time an activity is recognized. The outcomes of our strategy, for these metrics, in every single on the two phases with the algorithm, are shown in subsequent subsections.