Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Evaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition
Ist Teil von
Computer Information Systems and Industrial Management, p.253-269
Ort / Verlag
Cham: Springer Nature Switzerland
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
One of the technical aspects that contribute to improving the quality of life for older adults is the automation of physical spaces using sensors and actuators, which facilitates the performance of their daily activities. The interaction between individuals and their environment enables the detection of abnormal patterns that may arise from a decline in their cognitive abilities. In this study, we evaluate the CASAS Kyoto dataset from WSU University, which provides information on the daily living activities of individuals within an indoor environment. We developed a model to predict activities such as Cleaning, Cooking, Eating, Washing hands, and Phone Call. A novel approach is proposed, which involves preprocessing and segmenting the dataset using sliding windows. Furthermore, we conducted experiments with various classifiers to determine the optimal choice for the model. The final model utilizes the regression classification technique and is trained on a reduced dataset containing only 5 features. It achieves outstanding results, with a Recall of 99.80% and a ROC area of 100%.