{"id":1702,"date":"2024-05-21T11:34:00","date_gmt":"2024-05-21T09:34:00","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=1702"},"modified":"2025-08-25T11:29:01","modified_gmt":"2025-08-25T09:29:01","slug":"acquiring-multimodal-data-in-the-emotionai-box","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/acquiring-multimodal-data-in-the-emotionai-box\/","title":{"rendered":"Acquiring Multimodal Data in the Exposure Cabin"},"content":{"rendered":"\n

When acquiring data for affective computing<\/a>, we aim for a multimodal mix of data as it allows for a more comprehensive and accurate understanding of human emotions and behaviors. By collecting multimodal data, such as physiological signals or behavioral changes, we can capture a more nuanced and holistic picture of an individual’s affective states. This, of course, leads to more reliable and robust results and high-quality data, which in turn is the prerequisite for the sound classification of emotions with AI-based algorithms.<\/p>\n\n\n\n

Main challenges<\/h3>\n\n\n\n

What sounds good in theory, though, poses two major challenges to any (applied) research endeavor aiming to obtain state-of-the-art data on affective states:<\/p>\n\n\n\n

Challenge no. 1<\/strong>: There is no one-size-fits-all solution when it comes to measurement modalities. Different study designs and use cases may have individual requirements to measurement modalities and protocols.<\/p>\n\n\n\n

Challenge no. 2<\/strong>: While researchers \u2013 at least in their heart \u2013 wish for the optimum when it comes to study design and data, we are all constrained by feasibility and budgetary considerations.<\/p>\n\n\n\n

And that\u2019s where our exposure cabin comes in: We developed a closed, interference-free measurement cabin that facilitates the combination of various modalities \u2013 customized to individual needs. It provides researchers with<\/p>\n\n\n\n