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Mensch und Computer 2022

https://dl.gi.de/handle/20.500.12116/38889

Mensch und Computer 2022 vom 4.-7. September 2022 in Darmstadt

https://muc2022.mensch-und-computer.de/

Vorworte / Frontmatter:

Auflistung nach:

Auflistung Mensch und Computer 2022 nach Schlagwort "Accessibility"

1 - 3 von 3
  • Konferenzbeitrag
    IDeA: 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, Tonja
    People 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.
  • Workshopbeitrag
    Justice-oriented Research in Peace and Conflict Studies in Times of Social Distancing
    (Mensch und Computer 2022 - Workshopband, 2022) Guntrum, Laura Gianna
    Not only since the COVID-19 pandemic, many research processes had already been increasingly digitized to maintain global ex-change of information efficiently. For years, scientific empirical studies have been conducted in politically sensitive contexts using digitally mediated methods, entailing numerous risks as well as potentials. One goal of digital research is to also capture marginal-ized voices. With emerging risks related to digital research, such as digital surveillance and social media monitoring by adversaries (e.g., the military in Myanmar), research needs to be more thoughtfully conducted. Considering research ethics, an evolving discrepancy between security measures and values of social jus-tice, such as accessibility and representation, appears as most data-secure applications are not used widely and offers such as “Free Basics” entice people to use rather data-unsecure applica-tions. Reflecting on this existing discrepancy in ethical require-ments, I illustrate challenges of the German research context re-lated to digitally conducted research focusing on overt conflictive social contexts.
  • Konferenzbeitrag
    Sign 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, Michael
    Although 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