Digital assistive technologies (DATs) present a significant opportunity to address the challenges of the healthcare system. However, they have not been designed for this purpose and are still in the process of further development. The objective of this study is to ascertain the extent to which a digital cross-device control and application platform for the simplified utilization of DATs alters the willingness of nurses to utilize them in the care process. An exploratory, longitudinal design with a mixed-methods approach was employed in this study. The co-creative design cycle was conducted in two long-term care facilities, with 25 caregivers participating in the form of testing for the platform’s prototypes across multiple iteration loops. The preliminary results indicate a high intention to use DAT, suggesting that the actual use of DAT through a control and application platform will be considerable. The overall trend demonstrates an increase in acceptance, willingness, and intention to use DAT among the participants. One proposed solution to the usability issues of DAT is to develop a platform with an interface for non-programmers to create individual interventions using everyday controls and a few intuitive steps. This could facilitate the sustainable implementation of DAT in healthcare.

Object detection is an important tool in many areas, such as robotics or autonomous driving. Especially in these areas, a wide variety of object classes must be detected or interacted with. Models from the field of Open-Vocabulary Detection (OVD) provide a solution here, as they can detect not only base classes but also novel object classes, i.e., those classes that were not seen during training. However, one problem with OVD models is their poor calibration, meaning that the predictions are often too over- or under-confident. To improve the calibration, Temperature Scaling is used in this study. Using YOLO World, one of the best-performing OVD models, the aim is to determine the optimal T-value for this calibration method. For this reason, it is investigated whether there is a correlation between the logit distribution and the optimal T-value and how this can be modeled. Finally, the influence of Temperature Scaling on the Expected Calibration Error (ECE) and the mAP (Mean Average Precision) will be analyzed. The results of this study show that similar logit distributions of different datasets result in the same optimal T-values. This correlation could be best modeled using Kernel Ridge Regression (KRR) and Support Vector Machine (SVM). In all cases, the ECE could be improved by Temperature Scaling without significantly reducing the mAP.

The demographic shift towards an aging population necessitates innovative solutions for care and mobility support. While commercial quadruped robots like the Unitree Go1 offer dynamic stability, their native following modes often lack the safety margins and predictability required, and they do not consistently follow the user, at times deviating and navigating independently. This paper presents a robust, vision-based, person-following algorithm designed to address these limitations. Utilizing a ZED 2 stereo camera and Robot Operating System (ROS), the system employs a finite state machine to ensure deterministic target tracking. A velocity control strategy partitions the robot’s motion into distinct stability, proportional, and braking zones based on depth data to ensure fluid interaction. The framework was validated on a Unitree Go1 quadruped platform in an outdoor environment involving 90-degree turns to evaluate tracking robustness. By operating in a headless mode, the system achieved a mean processing latency of $66.5\pm 4.3$ ms. Experimental results demonstrated consistent operational stability, 0.0% intrusion into the intimate safety zone, and effective velocity synchronization between $0.47$ and $0.54$ m/s. While this study establishes a robust technical baseline using healthy subjects, it serves as a preliminary development platform; further iterative testing with elderly users in clinical settings is required to move toward deployment. Beyond the evaluated trials, the framework maintained reliable functional performance across various care facility workshops, successfully following the target in all deployment scenarios. These findings establish a stable technical foundation for the future development of robotic walking partners.

Custom-built quadrupedal platforms offer an accessible path for robotics research, yet researchers frequently encounter a “framework gap” when porting complex open-source control software to custom hardware. This paper documents the integration and validation process of Scotty, a custom quadrupedal robot, evaluating two distinct open-source control frameworks: the torque-based MIT Mini Cheetah and the position-based CHAMP architectures. Due to minimal documentation, hardware-dependent complexities, and a tightly coupled architecture, it was difficult to achieve a satisfactory result using the MIT framework within our system’s scope. Conversely, CHAMP’s structured integration documentation enabled the deployment of a locomotion pipeline validated in Gazebo simulation alongside a functional hardware interface middle layer. To overcome CHAMP’s lack of native operational state management, we developed a custom state-based controller with a web-based GUI that safely orchestrates transitions across Idle, Ready, Down, Stand, and Walk configurations. While walking was successfully validated in the simulation environment to verify the control software pipeline, physical hardware evaluation was restricted to individual joint control, localized leg movements, and GUI-based parameter tuning. Full closed-loop hardware locomotion was not achieved, as the extensive tuning of gait parameters and controller gains under full system weight was bounded by project constraints. The integration logs, practical hurdles, and architectural lessons documented in this work are shared openly to provide a clear, transferable roadmap for future developers of robotic systems.