{'fraction_non_alphanumeric': 0.01694915254237288, 'fraction_numerical': 0.0, 'mean_word_length': 5.314465408805032, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Standardizing Objective Measures of Presence in Immersive Virtual Environments

Presence is the feeling of actually being located in a virtual environment, and has been subject to intensive research as long as virtual reality exists. Nowadays, it is commonly evaluated using subjective measures, which most of the time take the form of questionnaires. However, there have been doubts about whether measures of this type are able to truly capture the actual concept behind presence (if it exists, although this is highly likely). While working on my master’s thesis, I found that it is quite difficult in practice to purposefully employ more objective measures into specific experimental designs. This is why I decided to dedicate myself to contributing ways to make this easier for as many scenarios as possible. My current work involves advanced pattern recognition and general methods from digital signal processing to analyze objective, continuous sensory data like eye-tracking and electromyography.