Figure 1: With aVRness, users can choose between displaying real-world objects through fitting placeholders (Anchor Mode), or a faded view of the real world (Gradient Passthrough Mode) to increase their safety and avoid collisions during VR locomotion.

Virtual Reality (VR) promises to enable people to fully immerse themselves in virtual worlds.However, the body-centricity of VR results in accessibility concerns for people with physical disabilities.In our work, we build on existing research that focuses on physical accessibility and discuss the digital layer that includes the design of avatars and virtual worlds, and the experiential layer that addresses the experience that VR provides for people with physical disabilities.We present findings from a qualitative study (N=16) that combined semi-structured interviews with hands-on exploration of state-of-the-art VR hardware and applications.Leveraging Qualitative Content Analysis, our results show that physical, digital, and experiential accessibility concerns are often intertwined and must be seen in conjunction.Additionally, we show that application contexts (e.g., gaming or social VR) shape accessibility concerns and preferences.On this basis, we contribute a nuanced discussion of the complexity of VR accessibility and examine context-specific accessibility preferences using the examples of VR gaming and social VR that need to be accounted for to create accessible and enriching VR experiences.

Ending a gaming session can be challenging for children and parents/guardians. Although many tools exist to monitor and restrict playtime, many do not adequately communicate session progress to children, and do not effectively involve guardians in the process. This is a missed opportunity for families to develop media literacy, and can also lead to conflicts and fights about playtime. To open up conversation about playtime management as a shared responsibility between children and guardians, we present "Schlusslicht"’ – an ambient display that seeks to facilitate a shared awareness of progression of a gaming session and remaining time between guardians and children, and can be leveraged to further explore how we can best support children when disengaging from play sessions. Video link: https://www.youtube.com/watch?v=khEdN9C9Sgw

Human-machine interfaces (HMIs) based on muscular and kinematic information promise intuitive real-time control of assistive devices such as grasp neuroprosthesis for persons with cervical spinal cord injury (SCI). However, interpreting this data is challenging due to high dimensionality and nested discriminative information. Hence, feature engineering and ranking are imperative to minimize computational load while maintaining high performance. In this work, we recorded electromyography (EMG) and kinematic (acceleration, orientation, angular rate) information of inertial measurement units (IMUs) during reach-and-grasp movements (uni-/bimanual palmar/lateral grasps) in groups of non-disabled people ($\mathrm{n}=12$) and of people with incomplete cervical SCI ($\mathrm{n}=3$). We extracted 12 EMG and 11 IMU feature types of 8 EMG and 45 IMU channels. We applied the feature selection approaches chi-square, maximum-relevance-minimum-redundancy (MRMR), Random Forest (RF), and Boruta for dimensionality reduction of the feature set and evaluated resulting subsets. We could show for both groups that there was no significant decrease in classification accuracy (RF model) with chi-square and Boruta subsets compared to the baseline set of all features, despite their heavily reduced dimensionalities (<25% and <74%, respectively). Accuracies peaked at 98.6 ± STD 0.9% (control group, Boruta) and 97.7 ± STD 1.1% (participants with SCI, Boruta). We found the MRMR subsets to be performing significantly worse. We could further show high information interpretability of chi-square and RF scores that indicated the importance of sensors and extracted features for reach-and-grasp classification. We plan to investigate how the approach can be implemented in real-time reach-and-grasp HMIs for persons with SCI.

Brain-computer interfaces (BCIs) could enable persons with cervical spinal cord injury (SCI) to intuitively control assistive motor devices for regaining lost grasping function. Previous studies, mostly performed in non-disabled persons, have already shown that complex upper limb movements can be decoded from the low-frequency time domain of the electroencephalogram (EEG).In this work, we attempted to translate these results to persons with cervical SCI and investigated whether executed reach and attempted grasp actions could be decoded from their EEG signals. For this, we chose three different reach-and-grasp actions, two unimanual and one bimanual, towards objects of daily life. During participants’ self-initiated, executed reach and attempted grasp actions, we recorded EEG using mobile, water-based electrodes. We measured two participants with subacute cervical SCI who had preserved shoulder movements and elbow flexion but no wrist and hand functions. Both repeated the session three times. We also recorded the EEG of 10 non-disabled persons performing the same tasks (control group). We extracted and analyzed movement-related cortical potentials (MRCPs) from the EEG’s low-frequency time domain. Consecutively, we assessed the decoding capabilities of two linear (shrinkage based linear discriminant analysis (sLDA), linear support vector machine (SVM)) and two non-linear (Random Forests (RF), naive Bayes (NBC)) classification models for the discrimination of the grasp actions.We could show that sLDA, SVM, and Random Forest yielded comparable classification results on average with 63.4% ± SD 9% for participants with SCI and 69.7% ± SD 9% for the control group (chance level 29.3%). Our results indicate that it is feasible to decode executed reach and attempted grasp actions from MRCPs of persons with subacute cervical SCI. Future measurements will provide additional data to assess the generalizability of our results in a larger group of people with cervical SCI.