Drone assisted navigation aids for supporting walking activities of visually impaired have been established in related work but fine-point object grasping tasks and the object localization in unknown environments still presents an open and complex challenge. We present a drone-based interface that provides fine-grain haptic feedback and thus physically guides them in hand-object localization tasks in unknown surroundings. Our research is built around community groups of blind or visually impaired (BVI) people, which provide in-depth insights during the development process and serve later as study participants. A pilot study infers users’ sensibility to applied guiding stimuli forces and the different human-drone tether interfacing possibilities. In a comparative follow-up study, we show that our drone-based approach achieves greater accuracy compared to a current audio-based hand guiding system and delivers overall a more intuitive and relatable fine-point guiding experience.

We present a dynamic error prediction system for industrial production machines. We implemented a flexible data collection tool to create error warnings for a production line, which aims to improve the already existing static alarm models. For industrial machines, there are threshold-based alarm models set by prior experiences and observations of the operator. For machines without standardized interfaces and communication protocols, which are not Industry 4.0 compatible, it represents a challenge to implement and add a dynamic and opportunistic system behavior. Machines need to learn from past errors autonomously and adapt the production properties dynamically. We implemented a framework that makes production machines conform to the Internet of Things (IoT) concepts, by making previously non-IoT enabled resources available to get new insights into the production processes.The system component recognition and the database setup is done fully automatically by our developed system.We designed and applied a feature-based data drift model in a real-world industrial setting to determine data deviation between normal and erroneous work-pieces in real-time to predict upcoming erroneous behavior. The drift analysis flagged and predicted work-pieces as erroneous several minutes before the pre-defined machine alarms would have been raised. The resulting flagged sensors and values can be compared to the system determined errors to get new insights into the abnormal machine behavior. For the reduction of downtime, the most valuable immediate result of the system is the ability to notify the operator earlier and reduce overall downtime.

In this study, we develop an electromagnetic-based haptic interface to provide controlled magnetic forces to the operator through a wearable haptic device (an orthopedic finger splint with single dipole moment) without position feedback. First, we model the electromagnetic forces exerted on a single magnetic dipole attached to the wearable haptic device, and derive magnetic force-current mapping for the dipole moment. Second, this mapping is used as basis for parameter selection of the electromagnetic coils of the haptic interface, dipole moment of the wearable haptic device, and the operating workspace of the system. The electromagnetic-based haptic interface enables three-dimensional (3D) virtual object rendering in mid-air within a workspace of 150 mm × 150 mm × 20 mm, using magnetic forces in excess of 50 mN. Participants experimentally demonstrate a 61% success rate in distinguishing the geometry of 4 representative 3D virtual objects. However, our statistical analysis shows that the ability of the participants to distinguish between geometries is not statistically significant, for 95% confidence level.

Experienced poker players have the ability to suppress and hide emotions and reactions to avoid providing information about the quality of the dealt private cards and the own probability of winning to the adversaries. Besides unswayable luck and bravery, bluffing is the only skill that could massively improve the own chance of winning. This paper investigates whether a subliminal reaction in terms of changing facial surface skin temperature can be linked to the quality of the dealt private cards (i.e., the probability of winning the actual hand). Therefore, a dataset containing thermal imaging has been recorded during a No Limit Texas Hold'Em Poker tournament-session with six players in total and two players being observed with a high-resolution thermal imaging camera and manual provision of their dealt private cards as ground-truth. Preliminary results show that the facial skin temperature varies massively (±1.2°C), which constitutes the research hypothesis that a significant change in the surface face skin temperature can be linked to the quality of the dealt cards in terms of winning chance for an actually played hand.

Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices that just happen to be available instead of pre-defining them at the design time of the system in order to achieve a recognition goal at runtime. Whenever a user and/or application states a recognition goal at runtime to the system, the available sensing devices configure an ensemble of the best available set of sensors for the specified recognition goal. This paper presents an approach to show how machine learning technologies (classification, fusion and anomaly detection) are integrated in a prototypical opportunistic activity and context recognition system (referred to as the OPPORTUNITY Framework). We define a metric that quantifies the ensemble's capabilities according to a recognition goal and evaluate the approach with respect to the requirements of an opportunistic system (e.g. to compute an ensemble's configuration and reconfiguration at runtime).