Elderly Multimodal Interpersonal Conversation Dataset (E-MIC Dataset) is a dataset built to recognize the timing of turn-taking in human-human social interaction. We collected interpersonal conversational interaction data, including video and audio, from 100 elderly people.
|[Figure 1] Samples of E-MIC dataset|
- The subjects are 100 elderly people over the age of 65 (66 women and 34 men, 65~ 85 years old).
- We collected RGB image data with two cameras and audio data with two microphones.
- One session involves two elderly people sitting face to face and interacting with daily conversations, each lasting 15 to 20 minutes.
- The E-MIC dataset includes various conversations (e.g., travel, hobby, health) of the elderly.
- The E-MIC dataset contains conversation interaction data of the elderly from a first-person perspective.
- The E-MIC dataset contains approximately 33 hours of video and three hours of audio data.
We annotated 19 types of information (e.g., take, release, wait, hold, gaze, backchannel, lip state) related to four types of turn-taking.
The E-MIC dataset used the annotation tool ELAN (https://tla.mpi.nl/tools/tla-tools/elan/download/)) to generate annotations about images and audios. Annotation information can be edited through the .eaf file included in this dataset and exported (export) in various forms of file (XML, CSV, etc.)
|[Figure 2] Sample of annotation of the collected video and audio clip|
|Collected Data||File Format||Size|
|Annotation data||EAF, CSV, XML||25.24MB|
We collected interpersonal conversational interaction data, including video and audio, from 100 elderly people. Figure 3 shows the data collection environment. Participants wear microphones, sit facing each other, and have conversations.
|[Figure 3] The data collection setup environment: (left) participants wear microphones, sit facing each other, and have conversations|
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- The protocol and consent of data collection were approved by the Institutional Review Board(IRB) at Suwon Science College, our joint research institute.
- This work was supported by the ICT R&D program of MSIP/IITP. [2017-0-00162, Development of Human-care Robot Technology for Aging Society].