Summary of Inferring Intentions to Speak Using Accelerometer Data In-the-wild, by Litian Li et al.
Inferring Intentions to Speak Using Accelerometer Data In-the-Wild
by Litian Li, Jord Molhoek, Jing Zhou
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This AI research paper explores the possibility of recognizing intentions to speak from accelerometer data, which is a privacy-preserving and feasible modality for in-the-wild settings. The study uses real-life social networking event data to train a machine-learning model that infers successful and unsuccessful intentions to speak. The results show that while there are some correlations between posture shifts and intentions to speak, the accuracy of the model is limited due to the complexity of human behavior. The paper concludes that additional modalities may be needed to reliably infer intentions to speak. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI tries to figure out when someone wants to talk by using data from special badges that can track movements like posture shifts. Researchers used real-life event data to train a computer model that can guess when people want to speak and when they don’t. But, it turns out that people often change their posture for reasons other than wanting to speak, or vice versa. So, the AI needs more ways to figure things out before it’s really good at guessing. |
Keywords
* Artificial intelligence * Machine learning
