PHAROS—PHysical Assistant RObot System

Costa, Ângelo; Martínez-Martín, Ester; Cazorla, Miguel; Julián, Vicente

doi:10.3390/s18082633

Cited by 57 publications

(68 citation statements)

References 28 publications

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“…Another proposal is PHAROS [30,31], a socially assistive robot that monitors and evaluates the daily physical exercise done at the user's home (see Figure 1). For this, machine learning techniques (i.e., a convolutional neural network (CNN) together with a recurrent neural network (RNN)) are used to properly identify and evaluate the exercise performance.…”

Section: Older Adult Carementioning

confidence: 99%

Socially Assistive Robots for Older Adults and People with Autism: An Overview

2020

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Over one billion people in the world suffer from some form of disability. Nevertheless, according to the World Health Organization, people with disabilities are particularly vulnerable to deficiencies in services, such as health care, rehabilitation, support, and assistance. In this sense, recent technological developments can mitigate these deficiencies, offering less-expensive assistive systems to meet users’ needs. This paper reviews and summarizes the research efforts toward the development of these kinds of systems, focusing on two social groups: older adults and children with autism.

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Section: Older Adult Carementioning

confidence: 99%

Socially Assistive Robots for Older Adults and People with Autism: An Overview

2020

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“…All studies use interactive robots manufactured in Germany, Japan, and the United States, as shown in Figure 10. Only the PR2 robot is being used in the same country [56], while Care-O-bot3 is used on collaboration between Portugal and Spain [57], and Pepper is used in the UK [58] (see Table 7).…”

Section: Assistive Roboticsmentioning

confidence: 99%

Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

Sanchez-Comas

Synnes

Hallberg

2020

Sensors

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Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.

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“…One example of this is the PHAROS [15] project, whose goal is to use a friendly-looking robot to engage elderly people in playful activities, such as physical exercises or cognitive games. The aim is to maintain a conversation that subliminally engages the users to perform the system's suggestions.…”

Section: Cognitive Assistantsmentioning

confidence: 99%

A Low-Cost Cognitive Assistant

et al. 2020

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In this paper, we present in depth the hardware components of a low-cost cognitive assistant. The aim is to detect the performance and the emotional state that elderly people present when performing exercises. Physical and cognitive exercises are a proven way of keeping elderly people active, healthy, and happy. Our goal is to bring to people that are at their homes (or in unsupervised places) an assistant that motivates them to perform exercises and, concurrently, monitor them, observing their physical and emotional responses. We focus on the hardware parts and the deep learning models so that they can be reproduced by others. The platform is being tested at an elderly people care facility, and validation is in process.

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PHAROS—PHysical Assistant RObot System

Cited by 57 publications

References 28 publications

Socially Assistive Robots for Older Adults and People with Autism: An Overview

Socially Assistive Robots for Older Adults and People with Autism: An Overview

Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

A Low-Cost Cognitive Assistant

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