A Survey on Ambient-Assisted Living Tools for Older Adults

By Parisa Rashidi, Alex Mihailidis. IEEE journal of biomedical and health informatics, 2013

1,099 citations recorded by the source corpus.

In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Demography

View Paper: https://www.semanticscholar.org/paper/48327aaf21902c09a92b90b1122f5bf2de62f56e

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Citations in the corpus

Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2016, background): “Legislation: Legislation and policy for certain aspects of telemedicine are not yet available [194,200], albeit they are a prerequisite for the development of the system described herein.”
High-Accuracy Localization for Assisted Living: 5G systems will turn multipath channels from foe to friend (2016, background): “Among the many location sensing methods proposed [3]–[9], active or passive radiolocalization are most promising, because radio transceivers can be integrated in existing devices like smartphones and built at small form factors with low power consumption.”
Evaluation of a skeleton-based method for human activity recognition on a large-scale RGB-D dataset (2016, methodology): “Tests have been executed considering C = 1 for the SVM, different levels of the temporal pyramid (L = [2, 3, 4]), and the same number of clusters for each action of the dataset, setting K = K1 = K2 = · · · = KM .”
Seven HCI Grand Challenges (2019, background): “Privacy and confidentiality should be safeguarded, while technology should not replace human care, resulting in older adults ’ isolation (Rashidi & Mihailidis, 2013), nor should it lead to abuse and violation of human rights ( e.g . excessive control by caregivers).”
A Human Activity Recognition System Using Skeleton Data from RGBD Sensors (2016, background): “In the last years, several tools have been proposed to improve quality of life of elderly people, from the remote control of health conditions to the improvement of safety [1].”
Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013, positive): “Preprocessing in the healthcare domain involves (1) filter unusual data to remove artifacts and (2) remove high frequency noise [1,13].”
Ambient Assisted Living Healthcare Frameworks, Platforms, Standards, and Quality Attributes (2014, background): “presents a general technical survey of ambient assisted living platforms, systems, algorithms and standards [16].”