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Humans can recognize their whole-body movements even when displayed as dynamic dot patterns. The sparse depiction of whole-body movements, coupled with a lack of visual experience watching ourselves in the world, has long implicated nonvisual mechanisms to self-action recognition. Using general linear modeling and multivariate analyses on human brain imaging data from male and female participants, we aimed to identify the neural systems for this ability. First, we found that cortical areas linked to motor processes, including frontoparietal and primary somatomotor cortices, exhibit greater engagement and functional connectivity when recognizing self-generated versus other-generated actions. Next, we show that these regions encode self-identity based on motor familiarity, even after regressing out idiosyncratic visual cues using multiple regression representational similarity analysis. Last, we found the reverse pattern for unfamiliar individuals: encoding localized to occipitotemporal visual regions. These findings suggest that self-awareness from actions emerges from the interplay of motor and visual processes.
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PolarTag: Invisible Data with Light Polarization
Visual tags (e.g., barcodes, QR codes) are ubiquitous in modern day life, though they rely on obtrusive geometric patterns to encode data, degrading the overall user experience. We propose a new paradigm of passive visual tags which utilizes light polarization to imperceptibly encode data using cheap, widely-available components. The tag and its data can be extracted from background scenery using off-the-shelf cameras with inexpensive LCD shutters attached atop camera lenses. We examine the feasibility of this design with real-world experiments. Initial results show zero bit errors at distances up to 3.0~m, an angular-detection range of \ang110, and robustness to manifold ambient light and occlusion scenarios.
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- PAR ID:
- 10191708
- Date Published:
- Journal Name:
- HotMobile '20: The 21st International Workshop on Mobile Computing Systems and Applications
- Page Range / eLocation ID:
- 74 to 79
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3376897.3377854
