323,033 research outputs found
Influence of Reaction Time in the Emotional Response of a Companion Robot to a Child’s Aggressive Interaction
No full textThe quality of a companion robot’s reaction is important to make it acceptable to the users and to sustain interactions. Furthermore, the robot’s reaction can be used to train socially acceptable behaviors and to develop certain skills in both normally developing children and children with cognitive disabilities. In this study, we investigate the influence of reaction time in the emotional response of a robot when children display aggressive interactions toward it. Different interactions were considered, namely, pickup, shake, drop and throw. The robot produced responses as audible sounds, which were activated at three different reaction times, namely, 0.5 s, 1.0 s, and 1.5 s. The results for one of the tasks that involved shaking the robotic toys produced a significant difference between the timings tested. This could imply that producing a late response to an action (i.e. greater than 1.0 s) could negatively affect the children’s comprehension of the intended message. Furthermore, the response should be comprehensible to provide a clear message to the user. The results imply that the designers of companion robotic toys need to consider an appropriate timing and clear modality for their robots’ responses
The impact of an object with different thicknesses of different soft materials at different impact velocities on a dummy head (dataset)
No full textThe purpose of this data is to study the effect of different thicknesses of different soft materials added to an object on the resultant head acceleration of a developed dummy head upon impact. The object was a cylinder (10 X 10 cm^2) and weigh 0.4 kg. The investigated materials were ecoflex, dragon skin, and clay while the thickness were 1 mm, 2 mm, 3 mm, and 5 mm. The impact velocities were in the range of 1 - 3 m/s. A total of 108 experiments were conducted. The raw data were analyzed for peak head linear acceleration, 3 ms criterion and the Head Injury Criterion (HIC) while the videos were analyzed for the impact velocity. This dataset includes the raw acceleration data and summary of the overall processed data
The impact of an object with different thicknesses of different soft materials at different impact velocities on a dummy head (dataset)
No full textThe purpose of this data is to study the effect of different thicknesses of different soft materials added to an object on the resultant head acceleration of a developed dummy head upon impact. The object was a cylinder (10 X 10 cm^2) and weigh 0.4 kg. The investigated materials were ecoflex, dragon skin, and clay while the thickness were 1 mm, 2 mm, 3 mm, and 5 mm. The impact velocities were in the range of 1 - 3 m/s. A total of 108 experiments were conducted. The raw data were analyzed for peak head linear acceleration, 3 ms criterion and the Head Injury Criterion (HIC) while the videos were analyzed for the impact velocity. This dataset includes the raw acceleration data and summary of the overall processed data
The impact of different shaped objects of different masses at different impact velocities on a dummy head (dataset)
No full textThe purpose of this study is to investigate the influence of the mass, the shape, and impact speed of an object on the resultant head acceleration of a developed dummy head. The investigated mass was in the range of 0.3 - 0.5 kg while the shapes considered were cube, wedge and cylinder. The impact velocities were in the range of 1 - 3 m/s. A total of 144 experiments were conducted and the corresponding videos and raw data were analyzed for impact velocity, peak head linear acceleration, 3ms criterion and the Head Injury Criterion (HIC). This dataset includes the raw acceleration data and summary of the overall processed data
The impact of different shaped objects of different masses at different impact velocities on a dummy head (dataset)
No full textThe purpose of this study is to investigate the influence of the mass, the shape, and impact speed of an object on the resultant head acceleration of a developed dummy head. The investigated mass was in the range of 0.3 - 0.5 kg while the shapes considered were cube, wedge and cylinder. The impact velocities were in the range of 1 - 3 m/s. A total of 144 experiments were conducted and the corresponding videos and raw data were analyzed for impact velocity, peak head linear acceleration, 3ms criterion and the Head Injury Criterion (HIC). This dataset includes the raw acceleration data and summary of the overall processed data
Diffusive author(s), cohesive author: Analysis of S/N (1994)
No full textThis study indicates the ways in which various aspects of the author(s) are brought forth in Dumb type’s performance art, the S/N production. Previous research has suggested a non-hierarchical organization of Dumb type and the absence of a “privileged author” in Dumb type’s collaborative work, S/N. However, the results that I have investigated from member’s interviews on the creative process of S/N along with my analysis of the recorded images of S/N, indicate a different aspect of the author(s). First, S/N was created through, so to speak, the collective ideas of the members of Dumb type. Further, S/N has at least nine quotations from previous performances, installations, and printed writings, besides the work-in-progress technique. Explicating one of the “author functions” as given by Michel Foucault, each text has plural subjects of the author. However, it has been revealed from members’ interviews that Teiji Furuhashi had a decision-making role in selecting the members’ ideas within the performance. Since then, S/N has had plural subjects of creation; however, Furuhashi is one of the subjects of creation along with the “privileged author.” S/N has plural authors (diffusive authors) yet at the same time, it has a “privileged author,” Teiji Furuhashi (cohesive author)
Data on the impact of objects with different shapes, masses, and impact velocities on a dummy head
No full textIn this article, a data generated from impacts of objects with different shapes, masses, and impact velocities on a developed dummy head. The mass considered was in the range of 0.3–0.5 kg while the shapes considered were cube, wedge, and cylinder. The impact velocities levels were in the range of 1–3 m/s. A total of 144 experiments were conducted and the corresponding videos and raw data were analyzed for impact velocity, peak head linear acceleration, 3 ms criterion, and the Head Injury Criterion (HIC). This dataset includes the raw acceleration data and a summary of the overall processed data. The data is available on Harvard Dataverse: https://doi.org/10.7910/DVN/AVC8GG
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Real-Time Social Robot’s Responses to Undesired Interactions Between Children and their Surroundings
No full textAggression in children is frequent during the early years of childhood. Among children with psychiatric disorders in general, and autism in particular, challenging behaviours and aggression rates are higher. These can take on different forms, such as hitting, kicking, and throwing objects. Social robots that are able to detect undesirable interactions within its surroundings can be used to target such behaviours. In this study, we evaluate the performance of five machine learning techniques in characterizing five possible undesired interactions between a child and a social robot. We examine the effects of adding different combinations of raw data and extracted features acquired from two sensors on the performance and speed of prediction. Additionally, we evaluate the performance of the best developed model with children. Machine learning algorithms experiments showed that XGBoost achieved the best performance across all metrics (e.g., accuracy of 90%) and provided fast predictions (i.e., 0.004 s) for the test samples. Experiments with features showed that acceleration data were the most contributing factor on the prediction compared to gyroscope data and that combined data of raw and extracted features provided a better overall performance. Testing the best model with data acquired from children performing interactions with toys produced a promising performance for the shake and throw behaviours. The findings of this work can be used by social robot developers to address undesirable interactions in their robotic designs
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