Robust Face Tracking with Suppressed False Positives in Smart Home Environment

2008 ◽  
pp. 34-41
Author(s):  
Jun-Hyeong Do ◽  
Zeungnam Bien
Keyword(s):  
Home Environment ◽  
Smart Home ◽  
False Positives ◽  
Face Tracking ◽  
Smart Home Environment

IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

2019 ◽  
Vol 36 (1) ◽  
pp. 203-224 ◽  
Author(s):  
Mario A. Paredes‐Valverde ◽  
Giner Alor‐Hernández ◽  
Jorge L. García‐Alcaráz ◽  
María del Pilar Salas‐Zárate ◽  
Luis O. Colombo‐Mendoza ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Energy Saving ◽  
Home Environment ◽  
Smart Home ◽  
Electrical Energy ◽  
Smart Home Environment

Cognitive Radio for Smart Home Environment

2015 ◽  
Author(s):  
T. Sudha ◽  
Kiran Selvan ◽  
V. Anand ◽  
Krishna Anilkumar ◽  
V. G. Kavya ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Home Environment ◽  
Smart Home ◽  
Smart Home Environment

A Context-Aware Information Model for Elderly Homecare Services in a Smart Home

2009 ◽  
Author(s):  
Feng Zhou ◽  
Jianxin Roger Jiao ◽  
Songlin Chen ◽  
Daqing Zhang
Keyword(s):  
Home Environment ◽  
Smart Home ◽  
Rough Set Theory ◽  
Research Problem ◽  
Information Model ◽  
Context Aware ◽  
Sensor Platform ◽  
Smart Home Environment ◽  
Manual Assistance

One of the critical situations facing the society across the globe is the problem of elderly homecare services (EHS) due to the aggravation of the society coupled with diseases and limited social resources. This problem has been typically dealt with by manual assistance from caregivers and/or family members. The emerging Ambience Intelligence (AmI) technology suggests itself to be of great potential for EHS applications, owing to its strength in constructing a pervasive computing environment that is sensitive and responsive to the presence of human users. The key challenge of AmI implementation lies in context awareness, namely how to align with the specific decision making scenarios of particular EHS applications. This paper proposes a context-aware information model in a smart home to tackle the EHS problem. Mainly, rough set theory is applied to construct user activity models for recognizing various activities of daily living (ADLs) based on the sensor platform constructed in a smart home environment. Subsequently, issues of case comprehension and homecare services are also discussed. A case study in the smart home environment is presented. Initial findings from the case study suggest the importance of the research problem, as well as the feasibility and potential of the proposed framework.

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