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Face touch is an unconscious human habit. Frequent touching of sensitive/mucosal facial zones (eyes, nose, and mouth) increases health risks by passing pathogens into the body and spreading diseases. Furthermore, accurate monitoring of face touch is critical for behavioral intervention. Existing monitoring systems only capture objects approaching the face, rather than detecting actual touches. As such, these systems are prone to false positives upon hand or object movement in proximity to one's face (e.g., picking up a phone). We present FaceSense, an ear-worn system capable of identifying actual touches and differentiating them between sensitive/mucosal areas from other facial areas. Following a multimodal approach, FaceSense integrates low-resolution thermal images and physiological signals. Thermal sensors sense the thermal infrared signal emitted by an approaching hand, while physiological sensors monitor impedance changes caused by skin deformation during a touch. Processed thermal and physiological signals are fed into a deep learning model (TouchNet) to detect touches and identify the facial zone of the touch. We fabricated prototypes using off-the-shelf hardware and conducted experiments with 14 participants while they perform various daily activities (e.g., drinking, talking). Results show a macro-F1-score of 83.4% for touch detection with leave-one-user-out cross-validation and a macro-F1-score of 90.1% for touch zone identification with a personalized model.
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Whose Touch is This? : Understanding the Agency Trade-Off Between User-Driven Touch vs. Computer-Driven Touch
Force-feedback enhances digital touch by enabling users to share non-verbal aspects such as rhythm, poses, and so on. To achieve this, interfaces actuate the user’s to touch involuntarily (using exoskeletons or electrical-muscle-stimulation); we refer to this as computer-driven touch. Unfortunately, forcing users to touch causes a loss of their sense of agency. While researchers found that delaying the timing of computer-driven touch preserves agency, they only considered the naïve case when user-driven touch is aligned with computer-driven touch. We argue this is unlikely as it assumes we can perfectly predict user-touches. But, what about all the remainder situations: when the haptics forces the user into an outcome they did not intend or assists the user in an outcome they would not achieve alone? We unveil, via an experiment, what happens in these novel situations. From our findings, we synthesize a framework that enables researchers of digital-touch systems to trade-off between haptic-assistance vs. sense-of-agency.
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- Award ID(s):
- 2047189
- PAR ID:
- 10390283
- Date Published:
- Journal Name:
- ACM Transactions on Computer-Human Interaction
- Volume:
- 29
- Issue:
- 3
- ISSN:
- 1073-0516
- Page Range / eLocation ID:
- 1 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3489608
