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- Morillo-Mendez, Lucas, 1991-, et al.
(författare)
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Age-Related Differences in the Perception of Eye-Gaze from a Social Robot
- 2021
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Ingår i: Social Robotics. - Cham : Springer. - 9783030905255 - 9783030905248 ; , s. 350-361
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Konferensbidrag (refereegranskat)abstract
- The sensibility to deictic gaze declines naturally with age and often results in reduced social perception. Thus, the increasing efforts in developing social robots that assist older adults during daily life tasks need to consider the effects of aging. In this context, as non-verbal cues such as deictic gaze are important in natural communication in human-robot interaction, this paper investigates the performance of older adults, as compared to younger adults, during a controlled, online (visual search) task inspired by daily life activities, while assisted by a social robot. This paper also examines age-related differences in social perception. Our results showed a significant facilitation effect of head movement representing deictic gaze from a Pepper robot on task performance. This facilitation effect was not significantly different between the age groups. However, social perception of the robot was less influenced by its deictic gaze behavior in older adults, as compared to younger adults. This line of research may ultimately help informing the design of adaptive non-verbal cues from social robots for a wide range of end users.
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- Morillo-Mendez, Lucas, 1991-, et al.
(författare)
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Age-Related Differences in the Perception of Robotic Referential Gaze in Human-Robot Interaction
- 2024
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Ingår i: International Journal of Social Robotics. - : Springer. - 1875-4791 .- 1875-4805. ; 16:6, s. 1069-1081
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Tidskriftsartikel (refereegranskat)abstract
- There is an increased interest in using social robots to assist older adults during their daily life activities. As social robots are designed to interact with older users, it becomes relevant to study these interactions under the lens of social cognition. Gaze following, the social ability to infer where other people are looking at, deteriorates with older age. Therefore, the referential gaze from robots might not be an effective social cue to indicate spatial locations to older users. In this study, we explored the performance of older adults, middle-aged adults, and younger controls in a task assisted by the referential gaze of a Pepper robot. We examined age-related differences in task performance, and in self-reported social perception of the robot. Our main findings show that referential gaze from a robot benefited task performance, although the magnitude of this facilitation was lower for older participants. Moreover, perceived anthropomorphism of the robot varied less as a result of its referential gaze in older adults. This research supports that social robots, even if limited in their gazing capabilities, can be effectively perceived as social entities. Additionally, this research suggests that robotic social cues, usually validated with young participants, might be less optimal signs for older adults.Supplementary Information: The online version contains supplementary material available at 10.1007/s12369-022-00926-6.
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- Rahaman, G M Atiqur, Dr. 1981-, et al.
(författare)
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Deep Learning based Aerial Image Segmentation for Computing Green Area Factor
- 2022
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Ingår i: 2022 10th European Workshop on Visual Information Processing (EUVIP). - : IEEE. - 9781665466233 - 9781665466240
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Konferensbidrag (refereegranskat)abstract
- The Green Area Factor(GYF) is an aggregate norm used as an index to quantify how much eco-efficient surface exists in a given area. Although the GYF is a single number, it expresses several different contributions of natural objects to the ecosystem. It is used as a planning tool to create and manage attractive urban environments ensuring the existence of required green/blue elements. Currently, the GYF model is gaining rapid attraction by different communities. However, calculating the GYF value is challenging as significant amount of manual effort is needed. In this study, we present a novel approach for automatic extraction of the GYF value from aerial imagery using semantic segmentation results. For model training and validation a set of RGB images captured by Drone imaging system is used. Each image is annotated into trees, grass, soil/open surface, building, and road. A modified U-net deep learning architecture is used for the segmentation of various objects by classifying each pixel into one of the semantic classes. From the segmented image we calculate the class-wise fractional area coverages that are used as input into the simplified GYF model called Sundbyberg for calculating the GYF value. Experimental results yield that the deep learning method provides about 92% mean IoU for test image segmentation and corresponding GYF value is 0.34.
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| 6. |
- Rahaman, G. M. Atiqur, et al.
(författare)
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Deep learning based automated estimation of urban green space index from satellite image : A case study
- 2024
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Ingår i: Urban Forestry & Urban Greening. - : Elsevier. - 1618-8667 .- 1610-8167. ; 97
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Tidskriftsartikel (refereegranskat)abstract
- The green area factor model is a crucial tool for conserving and creating urban greenery and ecosystem services within neighborhood land. This model serves as a valuable index, streamlining the planning, assessment, and comparison of local-scale green infrastructures. However, conventional on-site measurements of the green area factor are resource intensive. In response, this study pioneers a computational approach that integrates ecological and social dimensions to estimate the green area factor. Employing satellite remote sensing and advanced deep learning techniques, the methodology utilizes satellite orthophotos of urban areas subjected to semantic segmentation, identifying and categorizing diverse green elements. Ground truths are established through on-site measurements of green area factors and satellite orthophotos from benchmarking sites in Orebro, Sweden. Results reveal an 82.0% average F1-score for semantic segmentations, signifying a favourable correlation between computationally estimated and measured green area factors. The proposed methodology is potential for adapting to various urban settings. In essence, this research introduces a promising, cost-effective solution for assessing urban greenness, particularly beneficial for urban administrators and planners aiming for insightful and comprehensive green strategies in city planning.
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