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This study investigates how different motion planning algorithms, implemented on a collaborative robot (cobot), are perceived by 48 human subjects. The four implemented algorithms ensure human safety based on the concept of speed and separation monitoring, but differ based on the following characteristics: (a) the cobot motion happens either along a fixed path or with a trajectory that is continuously planned in real time via nonlinear model predictive control, to increase cobot productivity; (b) the cobot speed is further reduced—or not—in real time based on heart rate measurements, to increase perceived safety. We conclude that (1) using a fixed path—compared to real-time motion planning—may reduce productivity and, at least when heart rate measurements are not used to modify the cobot speed, increases perceived safety; (2) reducing cobot speed based on heart rate measurements reduces productivity but does not improve perceived safety; (3) perceived safety is positively affected by habituation during the experiment, and unaffected by previous experience. [ABSTRACT FROM AUTHOR]

Parental involvement in traditional autism therapy is key to the effective treatment of children with ASD. Little is known about parental involvement in robot-assisted autism therapy (RAAT)—novel therapeutic support for children with ASD. Our study investigates the effect of parental presence on multiple-session RAAT conducted with 16 children with ASD. They interacted with the social robot in the presence or absence of their parents. We measured children's socio-behavioral outcomes and conducted semi-structured interviews with parents. Parents did not necessarily affect the children's outcomes during the interventions. However, children's autism-related symptoms resulted in different socio-behavioral outcomes between sessions with and without parents. Most parents have reported positive changes in their children's behaviors when interacting with the robot. [ABSTRACT FROM AUTHOR]

This paper presents a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) for the purposes of Sign Language Processing. We envision it to serve as a new benchmark dataset for performance evaluations of Continuous Sign Language Recognition (CSLR) and Translation (CSLT) tasks. The proposed FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers resulting in 43,250 video samples. Dataset contributors recorded videos in real-life settings on a wide variety of backgrounds using various devices such as smartphones and web cameras. Therefore, distance to the camera, camera angles and aspect ratio, video quality, and frame rates varied for each dataset contributor. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life sign language. FluentSigners-50 baseline is established using two state-of-the-art methods, Stochastic CSLR and TSPNet. To this end, we carefully prepared three benchmark train-test splits for models' evaluations in terms of: signer independence, age independence, and unseen sentences. FluentSigners-50 is publicly available at https://krslproject.github.io/FluentSigners-50/ [ABSTRACT FROM AUTHOR]

Facial expressions in sign languages are used to express grammatical functions, such as question marking, but can also be used to express emotions (either the signer's own or in constructed action contexts). Emotions and grammatical functions can utilize the same articulators, and the combinations can be congruent or incongruent. For instance, surprise and polar questions can be marked by raised eyebrows, while anger is usually marked by lowered eyebrows. We investigated what happens when different emotions (neutral/surprise/anger) are combined with different sentence types (statement/polar question/wh-question) in Kazakh-Russian Sign Language (KRSL), replicating studies previously made for other sign languages. We asked 9 native signers (5 deaf, 4 hearing children of deaf adults) to sign 10 simple sentences in 9 conditions (3 emotions * 3 sentence types). We used OpenPose software to track eyebrow position in the video recordings. We found that emotions and sentence types influence eyebrow position in KRSL: eyebrows are raised for polar questions and surprise, and lowered for anger. There are also some interactions between the two factors, as well as some differences between hearing and deaf native signers, namely a smaller effect of polar questions for the deaf group, and a different interaction between emotions and wh-question marking in the two groups. We thus find evidence for the complex influences on non-manual behavior in signers of sign languages, and showcase a quantitative approach to this field. [ABSTRACT FROM AUTHOR]

This review paper focuses on different aspects of perceived safety for a number of autonomous physical systems. This is a major aspect of robotics research, as more and more applications allow humans and autonomous systems to share their space, with crucial implications both on safety and on its perception. The alternative terms used to express related concepts (e.g., psychological safety, trust, comfort, stress, fear, and anxiety) are listed and explained. Then, the available methods to assess perceived safety (i.e., questionnaires, physiological measurements, behavioral assessment, and direct input devices) are described. Six categories of autonomous systems are considered (industrial poly-articulated manipulators, indoor mobile robots, mobile manipulators, humanoid robots, drones, and autonomous vehicles), providing an overview of the main themes related to perceived safety in the specific domain, a description of selected works, and an analysis of how motion and characteristics of the system influence the perception of safety. The survey also discusses experimental duration and location of the reviewed papers, and the connection between perceived safety and safety standards. • This review paper focuses on perceived safety for different autonomous systems. • Alternative terms used to express perceived safety are listed and explained. • Methods to assess perceived safety are described and discussed. • Considered robots: industrial and mobile manipulators, mobile robots and humanoids. • We also consider other autonomous systems, i.e., drones and autonomous vehicles. [ABSTRACT FROM AUTHOR]