Social activity modelling and multimodal coaching for active aging

Dimitrov, Yordan; Gospodinova, Zlatka; Wheeler, Richard; Žnidaršič, Martin; Ženko, Bernard; Veleva, Vera; Miteva, Nadejda

doi:10.1145/3316782.3322761

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…Council of coaches [ 11 ] also focused on natural language interaction but included the concept of embodied conversational social characters. Supporting Active Ageing through Multimodal coaching (SAAM) [ 12 ] stresses the participation of the older adult social circles in the coaching process and social activity to combat isolation. Virtual Coaching Activities for Rehabilitation in Elderly (vCare) [ 13 ] considers tele-rehabilitation and frailty aspects supported by gamification and advanced vital sign monitoring.…”

Section: Introductionmentioning

confidence: 99%

User Centered Virtual Coaching for Older Adults at Home Using SMART Goal Plans and I-Change Model

Beristáin

Álvarez

Hors-Fraile³

et al. 2021

IJERPH

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Preventive care and telemedicine are expected to play an important role in reducing the impact of an increasingly aging global population while increasing the number of healthy years. Virtual coaching is a promising research area to support this process. This paper presents a user-centered virtual coach for older adults at home to promote active and healthy aging and independent living. It supports behavior change processes for improving on cognitive, physical, social interaction and nutrition areas using specific, measurable, achievable, relevant, and time-limited (SMART) goal plans, following the I-Change behavioral change model. Older adults select and personalize which goal plans to join from a catalog designed by domain experts. Intervention delivery adapts to user preferences and minimizes intrusiveness in the user’s daily living using a combination of a deterministic algorithm and incremental machine learning model. The home becomes an augmented reality environment, using a combination of projectors, cameras, microphones and support sensors, where common objects are used for projection and sensed. Older adults interact with this virtual coach in their home in a natural way using speech and body gestures on projected user interfaces with common objects at home. This paper presents the concept from the older adult and the caregiver perspectives. Then, it focuses on the older adult view, describing the tools and processes available to foster a positive behavior change process, including a discussion about the limitations of the current implementation.

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Section: Introductionmentioning

confidence: 99%

User Centered Virtual Coaching for Older Adults at Home Using SMART Goal Plans and I-Change Model

Beristáin

Álvarez

Hors-Fraile³

et al. 2021

IJERPH

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“…vCare 4 embeds clinical profiles and the pathways to drive the behavior of the virtual coach at home and provide wellelaborated services for tele-rehabilitation in neurology and cardiology (64). SAAM 5 supports the aging population living at home, with a novel and practical emphasis on ambient sensing and learning of user needs and preferences, and effective coaching by leveraging the user's social support networks (65). EMPATHIC 6 proposes multimodal face analytics, adaptive spoken dialogue systems and natural language interfaces along with non-intrusive technologies to extract physiological markers of emotional states in real-time, in order to support dependent aging persons and their caregivers (66).…”

Section: Discussionmentioning

confidence: 99%

“…SAAM 5 supports the aging population living at home, with a novel and practical emphasis on ambient sensing and learning of user needs and preferences, and effective coaching by leveraging the user's social support networks ( 65 ).…”

Section: Discussionmentioning

confidence: 99%

A Review of Virtual Coaching Systems in Healthcare: Closing the Loop With Real-Time Feedback

Tsiouris

Tsakanikas

Gatsios

et al. 2020

Front. Digit. Health

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This review focuses on virtual coaching systems that were designed to enhance healthcare interventions, combining the available sensing and system-user interaction technologies. In total, more than 1,200 research papers have been retrieved and evaluated for the purposes of this review, which were obtained from three online databases (i.e.,PubMed, Scopus and IEEE Xplore) using an extensive set of search keywords. After applying exclusion criteria, the remaining 41 research papers were used to evaluate the status of virtual coaching systems over the past 10 years and assess current and future trends in this field. The results suggest that in home coaching systems were mainly focused in promoting physical activity and a healthier lifestyle, while a wider range of medical domains was considered in systems that were evaluated in lab environment. In home patient monitoring with IoT devices and sensors was mostly limited to activity trackers, pedometers and heart rate monitoring. Real-time evaluations and personalized patient feedback was also found to be rather lacking in home coaching systems and this is the most alarming find of this analysis. Feasibility studies in controlled environment and an ongoing active research on Horizon 2020 funded projects, show that the future trends in this field are aiming to close the loop with automated patient monitoring, real-time evaluations and more precise interventions.

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“…This includes the detection of emotions and attitudes which can, among other things, influence communication effectiveness both in dialogue and in presentations. Methods developed in these fields have found applications in the analysis of clinician-patient communication [26], education [22], entertainment [4] and cognitive health monitoring [6]. In the SAAM project [6], we are employing Ambient Assisted Living (AAL) technologies to analyse activities and health status, and provide personalised multimodal coaching to elderly persons living on their own or in assisted care settings.…”

Section: Introductionmentioning

confidence: 99%

“…Methods developed in these fields have found applications in the analysis of clinician-patient communication [26], education [22], entertainment [4] and cognitive health monitoring [6]. In the SAAM project [6], we are employing Ambient Assisted Living (AAL) technologies to analyse activities and health status, and provide personalised multimodal coaching to elderly persons living on their own or in assisted care settings. Such activities and status include mobility, sleep, social activity, air quality, cardiovascular health, diet [15] and attitudes [10].…”

Section: Introductionmentioning

confidence: 99%

Extracting Audio-Visual Features for Emotion Recognition Through Active Feature Selection

Haider

Pollak

Albert

et al. 2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Research in automatic emotion recognition has seldom addressed the issue of computational resource utilisation. With the advent of ambient technology, which employs a variety of low-power, resource constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions aim at maintaining the user's independence as unobtrusively as possible. In this context, efforts are being made to model human social signals such as affects using low-cost technologies, which can aid health monitoring. This paper presents an Active Feature Selection (AFS) method using self-organized maps neural networks for emotion recognition in the wild. The AFS is used for feature subsets selection from three different feature sets: 62 out of 88 features were selected for eGeMAPs, 21 out of 988 for emobase, and 140 out of 2832 for LBPTOP features. The results show that the features subsets selected by AFS provide better results than the entire feature set and PCA dimensionality reduction method. The best improvement is observed on emobase features, followed by eGeMAPs. For visual features, nearly the same results are obtained with a significant reduction in dimensionality (only 5% of the full feature set is required for the same level of accuracy). The weighted score fusion results in an improvement, leading to 43.40% and 40.12% accuracies on the EmotiW 2018 validation and test datasets respectively.

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Social activity modelling and multimodal coaching for active aging

Cited by 10 publications

References 22 publications

User Centered Virtual Coaching for Older Adults at Home Using SMART Goal Plans and I-Change Model

User Centered Virtual Coaching for Older Adults at Home Using SMART Goal Plans and I-Change Model

A Review of Virtual Coaching Systems in Healthcare: Closing the Loop With Real-Time Feedback

Extracting Audio-Visual Features for Emotion Recognition Through Active Feature Selection

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