Keeping the Resident in the Loop: Adapting the Smart Home to the User

By Parisa Rashidi, Diane Joyce Cook. 2009

465 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Transportation, Signal Processing

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Citations in the corpus

Federated Multi-Task Learning (2017, background): “Examples of potential applications include: learning sentiment, semantic location, or activities of mobile phone users; predicting health events like low blood sugar or heart attack risk from wearable devices; or detecting burglaries within smart homes [3, 37, 40].”
Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation (2013, background): “During that time, researchers began to recognize the importance of applying and improving methodologies for behavior monitoring and decision making also in the fields of assistance services [72], [73] and power management [64].”
Learning under Concept Drift: an Overview (2010, background): “‘Smart’ home systems [126] or intelligent household appliances [34] also need to be adaptive to changing environment and user needs.”
Remote health monitoring of elderly through wearable sensors (2019, methodology): “In addition, some projects have focused on the monitoring patients suffering from a chronic disease, such as CASAS project [39], which use the smart-homes environment to monitor patients suffering from dementia, which is similar to ENABLE project [1] with the goal of giving them more autonomy in the”
Smart homes and their users: a systematic analysis and key challenges (2014, background): “Much of the technologically oriented literature on smart homes presents their beneﬁts for end-users as both obvious and manifold: comfort, security, scheduling tasks, convenience through automation, energy management and efﬁ-ciency; and for speciﬁc end-users, health and assisted living [20, 63].”
Learning Setting-Generalized Activity Models for Smart Spaces (2010, methodology): “We evaluate these methods using datasets from the CASAS Smart Home project [2].”
An Overview of Concept Drift Applications (2016, background): “Intelligent systems, or smart home systems [64] aim to develop intelligent household appliances [2].”
Tackling Climate Change with Machine Learning (2019, background): “t how they operate based on whether a building or room is occupied, thereby improving both occupant comfort and energy use [265]. MLcanhelpthesesystemsdynamically adapt tochanges inoccupancy patterns [266]. Moreover, occupancy detection itself represents an opportunity for ML algorithms, ranging fro”
Practical trigger-action programming in the smart home (2014, background): “Rashidi and Cook [16] suggest using machine learning over end-user programming, while others [9] argue in favor of control using a mobile phone.”
A Survey on Ambient Intelligence in Health Care (2013, unknown): “Methods for activity discovery build on a rich history of discovery research, including methods for mining frequent sequences [1,17], mining frequent patterns using regular expressions [47], constraint-based mining [90], and frequent-periodic pattern mining [91], [92].”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, methodology): “care applications, sensor networks need to be combined with technologies such as activity recognition [58], [59], behavioral pattern discovery [60], [61], anomaly detection [62] and”
Robust Federated Learning in a Heterogeneous Environment (2019, background): “Since the paradigm of Federated Learning presented by [1, 3], several recent works focus on diﬀerent applications of the problem, such as in deep learning [2], predicting health events from wearable devices, and detecting burglaries in smart homes [17, 18].”