Human centered computing for the development of assistive environments

Maglogiannis, I.

doi:10.1145/2674396.2674453

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…In 6.96% of the selected papers, the focus of the devices is Cognitive support [17,22,37,37,41,42,45,51]. Therapy support is considered in 4.35% of the articles [2,40,45,45,74].…”

Section: Rq1 -What Are the Usage Models Between Patients And Technology?mentioning

confidence: 99%

Technologies for monitoring patients with Alzheimer’s disease: A systematic mapping study and taxonomy

Machado,

Tavares,

Barbosa

2024

AIS

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Alzheimer’s Disease (AD) is an incurable disease and a type of dementia. About 55 million people around the world have AD. However, technologies have been used to assist in the healthcare of AD, supporting physicians in the palliative care of patients. This article presents a systematic mapping study (SMS) to identify articles that use technologies to monitor patients with AD in order to show an overview of the literature, identifying gaps and research opportunities in this field. The scientific contribution of this work is to identify monitoring technologies related to AD and highlight current trends on the subject. The paper uses the term technologies as hardware infrastructure and devices or systems without considering software technologies. In addition, this article proposes a taxonomy for the domain of technologies applied to AD patients. The SMS study was conducted in six databases, including articles from 1997 to 2021. An initial search resulted in 7,781 articles. After applying filter criteria, throwing automatic selection on databases, and manual assortment, 171 articles were selected. Subsequently, a second search was performed to reduce the list of articles and filter by the specific search objective of articles focused on technologies for monitoring with tracking, resulting in 74 works. The main results obtained are: (1) a relevant number of articles (43.42%) reported solutions used in sensor-based devices; (2) several works (33.33%) have the interaction focus on Position/Distance/Proximity/Location sensor type; (3) another group of articles has a secondary focus on Emergency help (18.97%). The results indicated the need for technologies to help caregivers monitor patients, in addition to evidence of research opportunities in palliative care and support for the daily activities of AD patients.

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“…In 6.96% of the selected papers, the focus of the devices is Cognitive support [17,22,37,37,41,42,45,51]. Therapy support is considered in 4.35% of the articles [2,40,45,45,74].…”

Section: Rq1 -What Are the Usage Models Between Patients And Technology?mentioning

confidence: 99%

Technologies for monitoring patients with Alzheimer’s disease: A systematic mapping study and taxonomy

Machado,

Tavares,

Barbosa

2024

AIS

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“…In this section we describe environments that could benefit from our methodology. As an example, the STHENOS project [18] already focussed on the development of a methodology and an affective computing system for the recognition of physiological states and biological activities in assistive environments. Kanjo et al [12] provide a good introduction to, and review of, the different approaches and modalities for emotion recognition in pervasive environments.…”

Section: Applications: Smart and Assistive Environmentsmentioning

confidence: 99%

Automatic Classification and Shift Detection of Facial Expressions in Event-Aware Smart Environments

Bernin

Muller

Ghose

et al. 2018

Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference

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Affective application developers often face a challenge in integrating the output of facial expression recognition (FER) software in interactive systems: although many algorithms have been proposed for FER, integrating the results of these algorithms into applications remains difficult. Due to interand within-subject variations further post-processing is needed. Our work addresses this problem by introducing and comparing three post-processing classification algorithms for FER output applied to an event-based interaction scheme to pinpoint the affective context within a time window. Our comparison is based on earlier published experiments with an interactive cycling simulation in which participants were provoked with game elements and their facial expression responses were analysed by all three algorithms with a human observer as reference. The three post-processing algorithms we investigate are mean fixed-window, matched filter, and Bayesian changepoint detection. In addition, we introduce a novel method for detecting fast transition of facial expressions, which we call emotional shift. The proposed detection pattern is suitable for affective applications especially in smart environments, wherever users' reactions can be tied to events.

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Home Supporting Smart Systems for Elderly People

Boumpa

Kakarountas

2020

EAI/Springer Innovations in Communication and Computing

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Human centered computing for the development of assistive environments

Cited by 4 publications

References 26 publications

Technologies for monitoring patients with Alzheimer’s disease: A systematic mapping study and taxonomy

Technologies for monitoring patients with Alzheimer’s disease: A systematic mapping study and taxonomy

Automatic Classification and Shift Detection of Facial Expressions in Event-Aware Smart Environments

Home Supporting Smart Systems for Elderly People

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