9786206740117 - Efficient and Intelligent Human Activity Monitoring and Recognition: Human Activity Recognition de Thakur, Dipanwita; Biswas, Suparna (7 resultados)

Condición: New.

Taschenbuch. Condición: Neu. Efficient and Intelligent Human Activity Monitoring and Recognition | Human Activity Recognition | Dipanwita Thakur (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786206740117 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 404 pp. Englisch.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces the readers to a comprehensive idea to implement machine learning-based physical activity recognition frameworks. This book covers the challenges and their respective solutions of machine learning-based human activity monitoring and recognition frameworks. A novel feature selection method, modified guided regularized random forest, is introduced to accurately select the most relevant and important features to address the ¿curse-of-dimensionality¿ and ¿overfitting¿ issues. Ensemble learning, Random projection-based ELM, feature fusion, and deep learning frameworks with attention mechanisms are explored for human activity recognition in the rest of the chapters. The importance of transitional activities is also discussed concerning hemiplegia gait analysis and the concept of online change point detection segmentation method is also introduced. Finally, the book ends with a flexible activity recognition and real-time monitoring system (Flexi-HAMR), which can efficiently monitor and recognize activities using online, real-time data streams and also update the model dynamically for any new activity such as Parkinsonian gait for early disease prediction.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 404 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book introduces the readers to a comprehensive idea to implement machine learning-based physical activity recognition frameworks. This book covers the challenges and their respective solutions of machine learning-based human activity monitoring and recognition frameworks. A novel feature selection method, modified guided regularized random forest, is introduced to accurately select the most relevant and important features to address the 'curse-of-dimensionality' and 'overfitting' issues. Ensemble learning, Random projection-based ELM, feature fusion, and deep learning frameworks with attention mechanisms are explored for human activity recognition in the rest of the chapters. The importance of transitional activities is also discussed concerning hemiplegia gait analysis and the concept of online change point detection segmentation method is also introduced. Finally, the book ends with a flexible activity recognition and real-time monitoring system (Flexi-HAMR), which can efficiently monitor and recognize activities using online, real-time data streams and also update the model dynamically for any new activity such as Parkinsonian gait for early disease prediction.