DropleX: Liquid sensing on tablet touchscreens
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918432785563648 |
|---|---|
| author | Zhang, Siqi Goel, Mayank Chan, Justin |
| author_facet | Zhang, Siqi Goel, Mayank Chan, Justin |
| contents | We present DropleX, the first system that enables liquid sensing using the capacitive touchscreen of commodity tablets. DropleX detects microliter-scale liquid samples, and performs non-invasive, through-container measurements for liquid analysis. These capabilities are made possible by a physics-informed mechanism that disables the touchscreen's built-in adaptive filters, originally designed to reject the effects of liquid drops such as rain, without any hardware modifications. We model the touchscreen's sensing capabilities, limits, and non-idealities to inform the design of a signal processing and learning-based pipeline for liquid sensing. Under controlled laboratory conditions, our system achieves 89-99% accuracy in detecting microliter-scale adulteration in soda, wine, and milk, 94-96% accuracy in threshold detection of trace chemical concentrations, and 86-96% accuracy in through-container adulterant detection. These exploratory results demonstrate the potential of repurposing commodity touchscreens as a liquid characterization platform for laboratory settings, food and beverage testing, and chemical analysis applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DropleX: Liquid sensing on tablet touchscreens Zhang, Siqi Goel, Mayank Chan, Justin Human-Computer Interaction We present DropleX, the first system that enables liquid sensing using the capacitive touchscreen of commodity tablets. DropleX detects microliter-scale liquid samples, and performs non-invasive, through-container measurements for liquid analysis. These capabilities are made possible by a physics-informed mechanism that disables the touchscreen's built-in adaptive filters, originally designed to reject the effects of liquid drops such as rain, without any hardware modifications. We model the touchscreen's sensing capabilities, limits, and non-idealities to inform the design of a signal processing and learning-based pipeline for liquid sensing. Under controlled laboratory conditions, our system achieves 89-99% accuracy in detecting microliter-scale adulteration in soda, wine, and milk, 94-96% accuracy in threshold detection of trace chemical concentrations, and 86-96% accuracy in through-container adulterant detection. These exploratory results demonstrate the potential of repurposing commodity touchscreens as a liquid characterization platform for laboratory settings, food and beverage testing, and chemical analysis applications. |
| title | DropleX: Liquid sensing on tablet touchscreens |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.02694 |