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Abstract: People around the world are living longer. The question arises of how to help elderly people to live longer independently and feel safe in their homes. Activity of Daily Living (ADL) recognition systems automatically recognize the daily activities of residents in smart homes. Automated monitoring of the daily routine of older individuals, detecting behavior patterns, and identifying deviations can help to identify the need for assistance. Such systems must ensure the confidentiality, privacy, and autonomy of residents. In this chapter, we review research and development in the field of ADL recognition. Breakthrough advancements have been evident in recent years with advances in sensor technology, the Internet of Things (IoT), machine learning, and artificial intelligence. We examine the main steps in the development of an ADL recognition system, introduce metrics for system evaluation, and present the latest trends in knowledge transfer and detection of behavior changes. The literature overview shows that deep learning approaches currently provide promising results. Such systems will soon mature for more diverse practical uses as transfer learning enables their fast deployment in new environments.
Keywords: digital health, elderly, activities of daily living, recognition of activities, sensors, machine learning
Published in DKUM: 15.01.2026; Views: 0; Downloads: 0
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Abstract: This master's thesis explores the simplification of the registration process for digital government services in Slovenia through a mobile application. It examines the existing SI-TRUST registration process, identifies challenges such as mandatory in-person identification, and proposes a mobile solution using facial recognition and AI verification to improve accessibility and efficiency. The research includes user experience testing via the System Usability Scale (SUS) method and evaluates the feasibility, security, and legal compliance of the proposed approach.
Keywords: digital identity, SUS, facial recognition, user experience, e-government
Published in DKUM: 30.09.2025; Views: 0; Downloads: 17
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Abstract: Sensing and remembering features in visual scenes are conditioned by visual attention and methods to guide it. This should be relevant in terms of product placement, which has become an important part of incorporating brands into different mass media formats with a commercial purpose. The approach can be challenging in 360◦ video, where an omnidirectional view enables consumers to choose different viewing perspectives, which may result in overlooking the brands. Accordingly, attention guidance methods should be applied. This study is the first to explore diegetic guidance methods as the only appropriate guiding method for an unobtrusive and unconscious nature of product placement. To test the effectiveness of three different diegetic guiding methods, a between-subject design was employed, where the participants were assigned randomly to one of four videos with the same scene but different guiding methods. The findings show and explain the discrepancy with studies on guiding attention in other contexts, as there were no significant differences between the guiding cues according to brand recall and brand recognition. The results also indicate a significant influence of brand familiarity on brand recall in 360◦ video. The article concludes by providing limitations, future research directions, and recommendations for audiovisual policy.
Keywords: product placement, visual attention, attention guidance methods, diegetic guiding cues, brand recall, brand recognition
Published in DKUM: 01.04.2025; Views: 0; Downloads: 10
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Abstract: Automatic speech recognition is essential for establishing natural communication with a human–computer interface. Speech recognition accuracy strongly depends on the complexity of language. Highly inflected word forms are a type of unit present in some languages. The acoustic background presents an additional important degradation factor influencing speech recognition accuracy. While the acoustic background has been studied extensively, the highly inflected word forms and their combined influence still present a major research challenge. Thus, a novel type of analysis is proposed, where a dedicated speech database comprised solely of highly inflected word forms is constructed and used for tests. Dedicated test sets with various acoustic backgrounds were generated and evaluated with the Slovenian UMB BN speech recognition system. The baseline word accuracy of 93.88% and 98.53% was reduced to as low as 23.58% and 15.14% for the various acoustic backgrounds. The analysis shows that the word accuracy degradation depends on and changes with the acoustic background type and level. The highly inflected word forms’ test sets without background decreased word accuracy from 93.3% to only 63.3% in the worst case. The impact of highly inflected word forms on speech recognition accuracy was reduced with the increased levels of acoustic background and was, in these cases, similar to the non-highly inflected test sets. The results indicate that alternative methods in constructing speech databases, particularly for low-resourced Slovenian language, could be beneficial.
Keywords: human–computer interaction, automatic speech recognition, acoustic modeling, highly inflected word forms, acoustic background
Published in DKUM: 28.03.2025; Views: 0; Downloads: 9
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Keywords: human gait, activity recognition, wearable, machine learning, convolutional neural network, recurrent neural network, attention mechanism
Published in DKUM: 10.04.2024; Views: 274; Downloads: 28
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