IoT Security Approaches in Social Robots for Ambient Assisted Living Scenarios

Vulpe, Alexandru; Paikan, Ali; Crăciunescu, Răzvan; Ziafati, Pouyan; Kyriazakos, Sofoklis; Hemmer, Adrien; Badonnel, Rémi

doi:10.1109/wpmc48795.2019.9096127

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…For instance, this is the case in the proposal by Bui and Chong [44], where the user communicates with the robot to access and control devices/information appliances in the smart home. Similar use cases are identified in [40][41][42]. But there are, of course, many other roles or functionalities that such robots can take on.…”

Section: Expected Servicesmentioning

confidence: 67%

“…One example is the previously mentioned Misty-II robot (Figure 2 (Left)). Other examples are the popular Nao [37][38][39] or LuxAI's QTRobot [40][41][42]. Figure 3 shows the external aspect of these two platforms.…”

Section: Robotic Platformsmentioning

confidence: 99%

“…Relatives living far away can use an intelligent remote control to keep an eye on things in the home or support them in certain activities via telepresence. In other proposals, the robot acts as a virtual therapist, which proposes the user physical or cognitive activities [15,[40][41][42]65]. Similarly, the robot can stimulate the user it accompanies to perform everyday activities [66].…”

Section: Expected Servicesmentioning

confidence: 99%

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Socially Assistive Robots in Smart Environments to Attend Elderly People—A Survey

Cruces,

Jerez,

Bandera

et al. 2024

Applied Sciences

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The aging of the population in developed and developing countries, together with the degree of maturity reached by certain technologies, means that the design of care environments for the elderly with a high degree of technological innovation is now being seriously considered. Assistive environments for daily living (Ambient Assisted Living, AAL) include the deployment of sensors and certain actuators in the home or residence where the person to be cared for lives so that, with the help of the necessary computational management and decision-making mechanisms, the person can live a more autonomous life. Although the cost of implementing such technologies in the home is still high, they are becoming more affordable, and their use is, therefore, becoming more popular. At a time when some countries are finding it difficult to provide adequate care for their elderly, this option is seen as a help for carers and to avoid collapsing health care services. However, despite the undoubted potential of the services offered by these AAL systems, there are serious problems of acceptance today. In part, these problems arise from the design phase, which often does not sufficiently take into account the end users—older people but also carers. On the other hand, it is complex for these older people to interact with interfaces that are sometimes not very natural or intuitive. The use of a socially assistive robot (SAR) that serves as an interface to the AAL system and takes responsibility for the interaction with the person is a possible solution. The robot is a physical entity that can operate with a certain degree of autonomy and be able to bring features to the interaction with the person that, obviously, a tablet or smartphone will not be able to do. The robot can benefit from the recent popularization of artificial intelligence-based solutions to personalize its attention to the person and to provide services that were unimaginable just a few years ago. Their inclusion in an AAL ecosystem should, however, also be carefully assessed. The robot’s mission should not be to replace the person but to be a tool to facilitate the elderly person’s daily life. Its design should consider the AAL system in which it is integrated, the needs and preferences of the people with whom it will interact, and the services that, in conjunction with this system, the robot can offer. The aim of this article is to review the current state of the art in the integration of SARs into the AAL ecosystem and to determine whether an initial phase of high expectations but very limited results have been overcome.

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Section: Expected Servicesmentioning

confidence: 67%

Section: Robotic Platformsmentioning

confidence: 99%

Section: Expected Servicesmentioning

confidence: 99%

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Socially Assistive Robots in Smart Environments to Attend Elderly People—A Survey

Cruces,

Jerez,

Bandera

et al. 2024

Applied Sciences

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“…Extensive series of experiments were performed in order to evaluate and compare the performance of process mining to alternative commonly used detection methods, including elliptic envelope, support-vector machine, local outlier factor and isolation forest techniques, also considering the influence of the time splitting, the influence of the pre-processing clustering and the detection performance according to multiple criteria. A more thorough description of the process mining methodology used can be found in our previous work [ 45 ]. Further consideration is also given next.…”

Section: Discussionmentioning

confidence: 99%

Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications

Vulpe

Crăciunescu

Drăgulinescu

et al. 2021

Sensors

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Today’s IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners and third party developers. Overall, these require scalable, decentralized and intelligent IoT security mechanisms and services which are addressed by the SecureIoT project. This paper presents the definition, implementation and validation of a SecureIoT-enabled socially assisted robots (SAR) usage scenario. The aim of the SAR scenario is to integrate and validate the SecureIoT services in the scope of personalized healthcare and ambient assistive living (AAL) scenarios, involving the integration of two AAL platforms, namely QTrobot (QT) and CloudCare2U (CC2U). This includes risk assessment of communications security, predictive analysis of security risks, implementing access control policies to enhance the security of solution, and auditing of the solution against security, safety and privacy guidelines and regulations. Future perspectives include the extension of this security paradigm by securing the integration of healthcare platforms with IoT solutions, such as Healthentia with QTRobot, by means of a system product assurance process for cyber-security in healthcare applications, through the PANACEA toolkit.

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Development of a metric to evaluate the ergonomic principles of assistive systems, based on the DIN 92419

2022

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IoT Security Approaches in Social Robots for Ambient Assisted Living Scenarios

Cited by 6 publications

References 3 publications

Socially Assistive Robots in Smart Environments to Attend Elderly People—A Survey

Socially Assistive Robots in Smart Environments to Attend Elderly People—A Survey

Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications

Development of a metric to evaluate the ergonomic principles of assistive systems, based on the DIN 92419

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