Proxemics and Approach Evaluation by Service Robot Based on User Behavior in Domestic Environment

Bhagya, S. M.; Samarakoon, S. M. Bhagya P.; Sirithunge, H. P. Chapa; Viraj, M. A.; Muthugala, M. A. Viraj J.; Buddhika, A. G.; Jayasekara, A. G. Buddhika P.

doi:10.1109/iros.2018.8593713

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…The human pose can also provide important information through nonverbal communication. The stance gives The proposed method will be improved with non-wearable sensor-based systems, such as vision-based detection methods that can detect the human skeleton [32][33][34][35][36]. Our solution enables real-time data monitoring.…”

Section: Discussionmentioning

confidence: 99%

Human Signature Identification Using IoT Technology and Gait Recognition

et al. 2021

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This study aimed to develop an autonomous design system for recognizing the subject by gait posture. Gait posture is a type of non-verbal communication characteristic of each person, and can be considered a signature used in identification. This system can be used for diagnosis. The system helps aging or disabled subjects to identify incorrect posture to recover the gait. Gait posture gives information for subject identification using leg movements and step distance as characteristic parameters. In the current study, the inertial measurement units (IMUs) located in a mobile phone were used to provide information about the movement of the upper and lower leg parts. A resistive flex sensor (RFS) was used to obtain information about the foot contact with the ground. The data were collected from a target group comprising subjects of different age, height, and mass. A comparative study was undertaken to identify the subject after the gait posture. Statistical analysis and a machine learning algorithm were used for data processing. The errors obtained after training data are presented at the end of the paper and the obtained results are encouraging. This article proposes a method of acquiring data available to anyone by using indispensable devices purchased by all users such as mobile phones.

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Section: Discussionmentioning

confidence: 99%

Human Signature Identification Using IoT Technology and Gait Recognition

et al. 2021

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“…For example, detecting structural elements such as doors can help some robots enter or leave a room, or detecting electrical outlets to establish a connection to recharge their own batteries [ 71 ]. Furthermore, human detection becomes quite relevant for service robots in social environments, since the way a robot should behave in front of an object is different from the way it should behave in front of a person [ 72 ]. Thus, a robot is unable to perform socially acceptable navigation if it does not have the ability to extract basic information from its environment, such as people or special elements detection.…”

Section: Mappingmentioning

confidence: 99%

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Sánchez

Ceriotti

Capitán

et al. 2022

Sensors

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Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments.

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“…DILMO proxemic environments, in which all dimensions are considered are described in [8,27,29,[31][32][33][34][35][36][37][38]. The work presented in [33], illustrates how the proxemic dimensions can support interaction among entities (people and objects), with a context-aware framework.…”

Section: B Proxemic Interactions In Hcimentioning

confidence: 99%

Proxemic Interactions in Mobile Devices to Avoid the Spreading of Infections

Pérez

Roose

Cardinale

et al. 2020

2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Currently, people's daily lives are affected by the pandemic produced by the COVID-19. One of the main problem is the quickly and easy spreading of the virus. Healthcare workers are affected by nosocomial infections (also called as hospital-acquired infections) that exist in workplaces and more specifically, from health care equipment. In practice, the use of technology is quite common in health care settings. However, due to the touchability of mobile digital devices, their use contributes to nosocomial infections, according to several studies. Some applications based on tracking people have been implemented in order to facilitate Human-Computer Interaction (HCI) and preventing contamination of surfaces by people's hands. Notwithstanding, their use still presents limitations related to implementing applications to be used in some hospital environments, such as care rooms, laboratories, clinical workrooms. To overcome these limitations, we propose the use of interpersonal distances and proxemic dimensions (i.e., Distance, Identity, Location, Movement, and Orientation -DILMO) for implementing HCI with mobile devices that reduces their touchability. The aim is to facilitate the development of mobile apps with proxemic HCI, supported in a proposed architecture, to stop spreading of nosocomial infection of COVID-19 and others. To show the usability and suitability of our proposal, we present two prototypes of apps for mobile devices as proof-of concept, using several combination of proxemic DILMO dimensions to model proxemic HCI that allow flexibility in interpersonal and devices people interactions.

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Proxemics and Approach Evaluation by Service Robot Based on User Behavior in Domestic Environment

Cited by 14 publications

References 23 publications

Human Signature Identification Using IoT Technology and Gait Recognition

Human Signature Identification Using IoT Technology and Gait Recognition

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Proxemic Interactions in Mobile Devices to Avoid the Spreading of Infections

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