Head Impact Severity Measures for Small Social Robots Thrown During Meltdown in Autism

Social robots have gained a lot of attention recently as they have been reported to be effective in supporting therapeutic services for children with autism. However, children with autism may exhibit a multitude of challenging behaviors that could be harmful to themselves and to others around them. Furthermore, social robots are meant to be companions and to elicit certain social behaviors. Hence, the presence of a social robot during the occurrence of challenging behaviors might increase any potential harm. In this paper, we identified harmful scenarios that might emanate between a child and a social robot due to the manifestation of challenging behaviors. We then quantified the harm levels based on severity indices for one of the challenging behaviors (i.e. throwing of objects). Our results showed that the overall harm levels based on the selected severity indices are relatively low compared to their respective thresholds. However, our investigation of harm due to throwing of a small social robot to the head revealed that it could potentially cause tissue injuries, subconcussive or even concussive events in extreme cases. The existence of such behaviors must be accounted for and considered when developing interactive social robots to be deployed for children with autism.The work is supported by a research grant from Qatar University under the grant No. QUST-1-CENG-2018-7Scopu

Data on the impact of objects with different shapes, masses, and impact velocities on a dummy head

In 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.The work is supported by a research grant from Qatar University under the Grant no. QUST-1-CENG-2018-7 .Scopu

A low-cost test rig for impact experiments on a dummy head

Anthropomorphic test dummies are commonly used to evaluate the potential harm to humans due to dangerous scenarios, such as that due to car accidents. Furthermore, they have been used in sports to evaluate the efficacy of protective gears in mitigating harm due to impacts. Recently, they have been considered in industrial and collaborative robotics to assess risks due interactions between a human and a robot. In this article, we describe the development of a low-cost dummy head impact rig. The motivation behind this project is to quantify the potential harm to a child's head due to impacts with a small robotic toy. Three severity indices can be estimated, namely, Head Acceleration Criterion (HIC), 3 ms criterion, and peak head acceleration. Furthermore, the artificial skin of the dummy head can be used to assess the potential for tissue injuries. 3D-printed parts were used to develop the head. A tri-axial accelerometer embedded inside the head was used to measure the changes in accelerations. The developed head was placed inside a dedicated experimental bench. A data acquisition card that is connected to a computer system was used to acquire the raw data and then store it. A script was used to postprocess the stored data for the three severity indices. A video camera recording in slow-motion was used to record the impacts. The calculation of the impact velocities was based on the analysis of the video recordings using an open-source software. The developed experimental setup was validated by producing comparable results to that of relevant previous studies. - 2019The work is supported by a research grant from Qatar University under the Grant No. QUST-1-CENG-2019-10 . The statements made herein are solely the responsibility of the authors. Appendix AScopu

Recognition of Aggressive Interactions of Children Toward Robotic Toys

Social robots are now being considered to be a part of the therapy of children with autism. During the interactions, some aggressive behaviors could lead to harmful scenarios. The ability of a social robot to detect such behaviors and react to intervene or to notify the therapist would improve the outcomes of therapy and prevent any potential harm toward another person or to the robot. In this study, we investigate the feasibility of an artificial neural network in classifying 6 interaction behaviors between a child and a small robotic toy. The behaviors were: hit, shake, throw, pickup, drop, and no interaction or idle. Due to the ease of acquiring data from adult participants, a model was developed based on adults' data and was evaluated with children's data. The developed model was able to achieve promising results based on the accuracy (i.e. 80%), classification report (i.e. overall F1-score=80%), and confusion matrix. The findings highlight the possibility of characterizing children's negative interactions with robotic toys to improve safety. - 2019 IEEE.The work is supported by a research grant from Qatar University under the grant No. QUST-1-CENG-2019-10. The statements made herein are solely the responsibility of the authors.Scopu