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Povzetek: Background and objectives Virtual reality (VR) technologies are increasingly being explored in long-term care (LTC) settings as tools to promote engagement and well-being among older adults, including those with cognitive impairments. However, the usability and accessibility of such technologies remain some critical concerns. This study aimed to examine how older adults in Slovenia adapted to using VR equipment for virtual walking experiences, focusing on their ability to operate the technology and their subjective evaluation of the experience. Method The study involved 31 older adults from two LTC homes in Slovenia who participated in virtual walking sessions using Google Street View, accessed through VR glasses and controlled via a joystick. Researchers observed participants' ability to use the equipment, assessed their cognitive and general health, and collected feedback on their experience via a structured questionnaire. Results Unlike prior studies that focus on VR exposure, our study quantifies learning time and links it to cognitive profiles, proposing a framework for adaptive VR interventions. The results showed that cognitive abilities significantly influenced how quickly participants could navigate the virtual environment, with those possessing higher cognitive abilities adapting more quickly. Although some participants found the navigation challenging, particularly those with dementia, this did not affect their overall enjoyment. Nearly all participants (97%) rated the experience as pleasant, and 74% expressed a desire to repeat it. Discussion and implications The study highlights the strong potential of VR to enhance residents' well-being in LTC homes. However, to ensure accessibility and maximise benefits, varying levels of support and customised technological solutions should be provided based on each resident's health status and cognitive abilities.
Ključne besede: older adults, virtual reality, VR glasses, Google Maps, cognitive abilities, virtual walk, dementia
Objavljeno v DiRROS: 17.08.2026; Ogledov: 176; Prenosov: 248
Celotno besedilo (2,31 MB)
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Povzetek: The turning process is a widely used cutting operation in industry. Any optimization of this process can significantly improve product quality, streamline costs, or reduce unwanted events. With automatic monitoring of turning tools, we can reduce costs, increase efficiency, and decrease the number of undesirable events that occur during machining (scrap, tool breakage, etc.). In single-piece or small-batch production, tool wear is monitored by the machine operator; however, such wear assessment is left to subjective judgment and requires intervention in the process. The presented solution eliminates this problem with automated monitoring of the cutting tool’s condition. An IR camera was used for process monitoring, which also captures the thermographic state. The camera was properly protected and mounted right next to the turning tool, enabling close-up observation of the machining. During the experiment, constant cutting parameters were set for turning the workpiece (low-alloy steel designated 1.7225, i.e.,42CrMo4) without the use of coolant. Using turning inserts with varying levels of wear, a database of more than 6,000 images was created during the turning process. With a convolutional neural network (CNN), a model was developed to predict wear and damage to the cutting tool. Based on the captured thermographic image during turning, the model automatically determines the cutting tool’s condition (no wear, minor wear, severe wear).The achieved classification accuracy was 99.55%, confirming the suitability of the proposed method. Such a system enables immediate action in the event of tool wear or breakage, regardless of the operator’s knowledge and training.
Ključne besede: deep learning, tool condition monitoring, turning, tool wear
Objavljeno v DiRROS: 03.02.2026; Ogledov: 498; Prenosov: 325
Celotno besedilo (12,76 MB)
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