[u' @article{azkune_combining_2015, title = {Combining {Users}\u2019 {Activity} {Survey} and {Simulators} to {Evaluate} {Human} {Activity} {Recognition} {Systems}}, volume = {15}, copyright = {http://creativecommons.org/licenses/by/3.0/}, url = {http://www.mdpi.com/1424-8220/15/4/8192}, doi = {10.3390/s150408192}, abstract = {Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.}, language = {en}, number = {4}, urldate = {2015-04-13}, journal = {Sensors}, author = {Azkune, Gorka and Almeida, Aitor and L\xf3pez-de-Ipi\xf1a, Diego and Chen, Liming}, month = apr, year = {2015}, note = {00000}, keywords = {AI for health, Activity Recognition, Artificial Intelligence, Data analysis, Q1, Synthetic Dataset Generator, activity survey, evaluation methodology, intelligent environments, jcr2.048, machine learning}, pages = {8192--8213}, } ']