شماره ركورد كنفرانس :
4767
عنوان مقاله :
Feature-Based Elderly Behavior Detection and Prediction in Smart Homes
عنوان به زبان ديگر :
Feature-Based Elderly Behavior Detection and Prediction in Smart Homes
پديدآورندگان :
Erfanmanesh Malihe m.erfanmanesh@shirazu.ac.ir Department of Computer Science and Engineering and IT Shiraz University, Shiraz, Iran , Tahayori Hooman tahayori@shirazu.ac.ir Department of Computer Science and Engineering and IT Shiraz University, Shiraz, Iran , Visconti Andrea Universita degli Studi di Milano Milano, Italy
كليدواژه :
activity recognition , machine learning , classification , behavior prediction , smart home
عنوان كنفرانس :
اولين كنفرانس ملي فناوري ها و سيستم هاي محاسباتي مراقبت از سلامت
چكيده فارسي :
One of the capabilities of smart home in the healthcare domain is helping elderly to live independently. This demands for detecting and monitoring their normal activities of daily living (ADLs). By considering changes in the occurrence of these normal activities we can decide about declining the health status of the elderly. Hence, the possibility of preventive care for some elderly people would be partly provided. In this paper, we propose a method for detection and prediction of elderly activities by extracting several features from available information. Obtained results reveal that the proposed features are more effective for detection and prediction of elderly behavior.
چكيده لاتين :
One of the capabilities of smart home in the healthcare domain is helping elderly to live independently. This demands for detecting and monitoring their normal activities of daily living (ADLs). By considering changes in the occurrence of these normal activities we can decide about declining the health status of the elderly. Hence, the possibility of preventive care for some elderly people would be partly provided. In this paper, we propose a method for detection and prediction of elderly activities by extracting several features from available information. Obtained results reveal that the proposed features are more effective for detection and prediction of elderly behavior.
