Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
| Autor: | Diaz-Martinez Jorge Luis, Suarez-Brieva Eydy del Carmen, Butt Shariq Aziz, Molina_Estren Diego, Urina-Triana Miguel, Oñate-Bowen Alvaro Agustín, De-La-Hoz-Franco Emiro, García-Restrepo Johanna, Ariza-Colpas Paola Patricia |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Activity Daily Living
Activities of daily living Computer science business.industry Process (engineering) ADL Human Activity Recognition Machine learning computer.software_genre Automation Field (computer science) Activity recognition Machine Learning Identification (information) Software Home automation HAR General Earth and Planetary Sciences Artificial intelligence business computer General Environmental Science |
| Zdroj: | Procedia Computer Science Vol. 191 (2021) Repositorio Digital USB Universidad Simón Bolívar instacron:Universidad Simón Bolívar REDICUC-Repositorio CUC Corporación Universidad de la Costa instacron:Corporación Universidad de la Costa FNC/MobiSPC |
| Popis: | AI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and therefore enrich the quality of life while preserving their privacy. An SH system is usually equipped with a collection of software interrelated with hardware components to monitor the living space by capturing the behavior of the resident and their occupations. By doing so, the system can report risks, situations, and act on behalf of the resident to their satisfaction. This research article shows the experimentation carried out with the human activity recognition dataset, CASAS Kyoto, through preprocessing and cleaning processes of the data, showing the Via Regression classifier as an excellent option to process this type of data with an accuracy 99.7% effective |
| Databáze: | OpenAIRE |
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