No abstract

Animals coordinate their various body parts, sometimes in elaborate manners to swim, walk, climb, fly, and navigate their environment. The coordination of body parts is essential to behaviors such as, chasing, escaping, landing, and the extraction of relevant information. For example, by shaping the movement of the head and body in an active and controlled manner, flying insects structure their flights to facilitate the acquisition of distance information. They condense their turns into a short period of time (the saccade) interspaced by a relatively long translation (the intersaccade). However, due to technological limitations, the precise coordination of the head and thorax during insects' free-flight remains unclear. Here, we propose methods to analyse the orientation of the head and thorax of bumblebees Bombus terrestris, to segregate the trajectories of flying insects into saccades and intersaccades by using supervised machine learning (ML) techniques, and finally to analyse the coordination between head and thorax by using artificial neural networks (ANN). The segregation of flights into saccades and intersaccades by ML, based on the thorax angular velocities, decreased the misclassification by 12% compared to classically used methods. Our results demonstrate how machine learning techniques can be used to improve the analyses of insect flight structures and to learn about the complexity of head-body coordination. We anticipate our assay to be a starting point for more sophisticated experiments and analysis on freely flying insects. For example, the coordination of head and body movements during collision avoidance, chasing behavior, or negotiation of gaps could be investigated by monitoring the head and thorax orientation of freely flying insects within and across behavioral tasks, and in different species.

Many animals rely on robust visual navigation which can be explained by snapshot models, where an agent is assumed to store egocentric panoramic images and subsequently use them to recover a heading by comparing current views to the stored snapshots. Long-range route navigation can also be explained by such models, by storing multiple snapshots along a training route and comparing the current image to these. For such models, memory capacity and comparison time increase dramatically with route length, rendering them unfeasible for smallbrained insects and low-power robots where computation and storage are limited. One way to reduce the requirements is to use a compressed image representation. Inspired by the filter bank-like arrangement of the visual system, we here investigate how a frequency-based image representation influences the performance of a typical snapshot model. By decomposing views into wavelet coefficients at different levels and orientations, we achieve a compressed visual representation that remains robust when used for navigation. Our results indicate that route following based on wavelet coefficients is not only possible but gives increased performance over a range of other models.

AGE-WELL NCE is a Canadian centre of excellence dedicated to improving quality of life for older adults. The Rural/ Remote and Indigenous Technology Needs Exploration (RRITE) is a multisite AGE-WELL project with research sites in Saskatchewan and Ontario, Canada. The Indigenous research explores how older Indigenous adults with dementia might be supported, through technology, to age in place. Using a combination of Indigenous research methodologies and community-based participatory action research, the research teams work continually, from proposal to dissemination, with community advisory groups to develop, direct and refine the research process. Advisory groups are composed of health care providers and Indigenous people with lived experience, as well as Elders. This multisite study considers qualitative data from focus groups and interviews with older Indigenous adults, their family members, care providers, and natural helpers from First Nations communities in Ontario and Saskatchewan. This paper presents the results related to technology needs, the accessibility of assistive technology within Indigenous communities, and the role technology may have in fulfilling the health and social needs of older Indigenous adults in Ontario and Saskatchewan. Understanding the unique needs of Indigenous older adults and their communities can lead to the development of culturally safe and appropriate cognitively assistive technology, which can greatly add to the literature on prevalence and perceptions of assistive technology use by Indigenous people. The neighborhood people live in as well as the existence of neighborhood networks can have a crucial impact on their quality of life. These neighborhood networks can be complemented and strengthened by digital neighborhood platforms, which additionally can enable people with mobility limitations to participate in social life and live self-determinedly. Therefore, the trans-disciplinary research project "QuartiersNETZ" ("Neighborhood NET") aims to develop networks and digital platforms in four urban neighborhoods in the Ruhr area in Germany. User participation in technology development processes has become more important. However, these involved users are often not representative of the heterogeneous potential user groups so that the actual circumstances and requirements are not met. To overcome this problem the project has explored different life situation types. On the basis of a representative survey within the case study neighborhoods with a random sample of in total 4,000 residents aged 50 years and older (response rate 29.7%, N=1,186) a hierarchical cluster analysis was conducted. Eight life situation types were identified based on the following characteristics: socioeconomic status, health, social relationships and housing conditions. In a next step, local actors selected three to four representatives for each cluster who were willing to participate in the digital platform development process. Qualitative interviews were performed in order to get to know their challenges ...

This paper presents an interactive media installation that aims at providing users with the experience to sing like an opera singer from the 19th century. We designed a set of tangible and body-related interaction and feedback techniques and developed a singing voice synthesizer system that is controlled by the user's mouth shapes and gestures. This musical interface allows users to perform an aria without real singing. We adapted techniques from 3D body tracking, face recognition, singing voice synthesis, 3D rendering and tangible interaction to integrate them into an interactive musical interface.