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Auflistung Tagungsband MuC 2022 nach Schlagwort "Accessibility"
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- KonferenzbeitragIDeA: A Demonstration of a Mixed Reality System to Support Living with Central Field Loss(Mensch und Computer 2022 - Tagungsband, 2022) Lang, Florian; Grootjen, Jesse W.; Chuang, Lewis L.; Machulla, TonjaPeople with visual impairment face multiple challenges in their everyday life. They must regularly visit doctors to examine the progress of their impairment and advisory offices or local support groups to learn strategies to overcome challenges in everyday life. However, traveling to appropriate facilities itself often poses challenges to the patients. We propose IDeA, a system based on augmented and virtual reality technology for people with visual impairments. IDeA can lower the cost and improve access to medical care, support digitalization in treating visual impairments, and provide support in the everyday lives of people with visual impairments. IDeA is a three-fold system supporting: 1) the simulation of symptoms of visual impairments to raise awareness in persons without visual impairment, 2) showcasing early detection of symptoms and providing visual augmentations of the real world to support patients in overcoming challenges, and 3) supporting doctors through telemedical consultation, medical eye examinations, and the training of visual strategies. We outline the benefit for all stakeholders and how IDeA can improve the lives of people with visual impairment.
- KonferenzbeitragSign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital Pen(Mensch und Computer 2022 - Tagungsband, 2022) Schrapel, Maximilian; Grannemann, Dennis; Rohs, MichaelAlthough in many cases contracts can be made or ended digitally, laws require handwritten signatures in certain cases. Forgeries are a major challenge with digital contracts, as their validity is not always immediately apparent without forensic methods. Illiteracy or disabilities may result in a person being unable to write their full name. In this case x-mark signatures are used, which require a witness for validity. In cases of suspected fraud, the relationship of the witnesses must be questioned, which involves a great amount of effort. In this paper we use audio and motion data from a digital pen to identify users via handwritten symbols. We evaluated the performance our approach for 19 symbols in a study with 30 participants. We found that x-marks offer fewer individual features than other symbols like arrows or circles. By training on three samples and averaging three predictions we reach a mean F1-score of F 1 = 0.87, using statistical and spectral features fed into SVMs