The EnrichMe Project

Salatino, Claudia; Gower, Valerio; Ghrissi, Meftah; Tapus, Adriana; Wieczorowska‐Tobis, Katarzyna; Suwalska, Aleksandra; Barattini, Paolo; Rosso, Roberto; Munaro, Giulia; Bellotto, Nicola; Heuvel, H. van den

doi:10.1007/978-3-319-41264-1_45

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…The study was carried out within the frame of the international ENRICHME project (ENabling Robot and assisted living environment for Independent Care and Health Monitoring of the Elderly). The aim of the project was to test the support of a robot for persons with mild cognitive impairment (MCI, greater cognitive decline than expected for an individual’s age and education level but does not interfere notably with activities of daily life [ 26 ]) in their living environment [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Could Robots Help Older People with Age-Related Nutritional Problems? Opinions of Potential Users

Łukasik

Tobis

Wieczorowska‐Tobis

et al. 2018

IJERPH

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Background: The rapid development of new technologies has recently raised interest in the use of assistive robots in the care of older people. The success or failure of robots’ implementation is mediated by their acceptance by users. The aim of the study was to answer the question of how both older people and caregivers perceive the possibility of using an assistive robot for nutritional support. Methods: Opinions of 252 respondents (126 older—aged 65 and above; 126 younger ones—aged below 65) from five European countries were collected using the Users’ Needs, Requirements, and Abilities Questionnaire. Results: The majority of participants positively assessed the possibility of using a robot to improve the diet of older people by giving advice on healthy eating or monitoring the amount of food consumed by the owner. An age-related difference was observed, as older people less frequently accepted the reminding of meal times or drinking liquids than younger participants. Four key categories related to the robot’s role in the diet of older people were identified: matching the needs, robot’s characteristics, ethical issues and technical/financial issues. Conclusions: Our preliminary research has shown a positive approach to robots in the context of their nutrition-related aspects. An indication has been made of the need to include older people and other stakeholders in the process of designing these functionalities and modeling human–robot interactions based on communication theories.

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Section: Methodsmentioning

confidence: 99%

Could Robots Help Older People with Age-Related Nutritional Problems? Opinions of Potential Users

Łukasik

Tobis

Wieczorowska‐Tobis

et al. 2018

IJERPH

Self Cite

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“…It does not facilitate the interaction including new activities at runtime without the contribution of developers. ENRICHME project 2 is another EU project financed through the H2020 Framework Program [14,15]. The project aims to face the cognitive decline of cognitive capacities in elderly people.…”

Section: Related Workmentioning

confidence: 99%

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Lanza

Seidita

Chella

2020

Journal of Biomedical Informatics

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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“…For example, wearables have been explored as a cheaper, less intrusive, and more portable alternative [9][10][11][12][13][14][15][16] with improved results over METs. Large-scale home monitoring systems [17][18][19] have started to provide enough data to investigate computer vision approaches [20][21][22], which are cheap, much less intrusive, and more accurate. This provides the opportunity to extend the monitoring of energy consumption from stationary work environments [23][24][25], where variation between different people cannot be accurately captured by self-reporting.…”

Section: Introductionmentioning

confidence: 99%

Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning

Perrett¹,

Masullo²,

Damen³

et al. 2022

JMIR Form Res

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Background Calorimetry is both expensive and obtrusive but provides the only way to accurately measure energy expenditure in daily living activities of any specific person, as different people can use different amounts of energy despite performing the same actions in the same manner. Deep learning video analysis techniques have traditionally required a lot of data to train; however, recent advances in few-shot learning, where only a few training examples are necessary, have made developing personalized models without a calorimeter a possibility. Objective The primary aim of this study is to determine which activities are most well suited to calibrate a vision-based personalized deep learning calorie estimation system for daily living activities. Methods The SPHERE (Sensor Platform for Healthcare in a Residential Environment) Calorie data set is used, which features 10 participants performing 11 daily living activities totaling 4.5 hours of footage. Calorimeter and video data are available for all recordings. A deep learning method is used to regress calorie predictions from video. Results Models are personalized with 32 seconds from all 11 actions in the data set, and mean square error (MSE) is taken against a calorimeter ground truth. The best single action for calibration is wipe (1.40 MSE). The best pair of actions are sweep and sit (1.09 MSE). This compares favorably to using a whole 30-minute sequence containing 11 actions to calibrate (1.06 MSE). Conclusions A vision-based deep learning energy expenditure estimation system for a wide range of daily living activities can be calibrated to a specific person with footage and calorimeter data from 32 seconds of sweeping and 32 seconds of sitting.

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The EnrichMe Project

Cited by 11 publications

References 10 publications

Could Robots Help Older People with Age-Related Nutritional Problems? Opinions of Potential Users

Could Robots Help Older People with Age-Related Nutritional Problems? Opinions of Potential Users

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning

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