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The strategy of detecting physiological signals and body movements using fabric-based pressure sensors offers the opportunity to unobtrusively collect multimodal health metrics using loose-fitting, familiar garments in natural environments. (A. Kiaghadi, S. Z. Homayounfar, J. Gummeson, T. Andrew, and D. Ganesan,Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.,3, 1–29 (2019)). However, many sensing scenarios, such as sleep and posture monitoring, involve an added static pressure from exerted body weight, which overpowers weaker pressure signals originating from heartbeats, respiration and pulse and phonation. Here, we introduce an all-fabric piezoionic pressure sensor (PressION) that, on account of its ionic conductivity, functions over a wide range of static and dynamic applied pressures (from subtle ballistic heartbeats and pulse waveforms, to larger-scale body movements). This piezoionic sensor also maintains its pressure responsivity in the presence of an added background pressure and upon integration into loose-fitting garments. The broad ability of PressION to record a wide variety of physiological signals in realistic environments was confirmed by acquiring heartbeat, pulse, joint motion, phonation and step data from different body locations. PressION’s sensitivity, along with its low-cost fabrication process, qualifies it as a uniquely useful sensing element in wearable health monitoring systems.
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Indutivo: Contact-Based, Object-Driven Interactions with Inductive Sensing
We present Indutivo, a contact-based inductive sensing technique for contextual interactions. Our technique recognizes conductive objects (metallic primarily) that are commonly found in households and daily environments, as well as their individual movements when placed against the sensor. These movements include sliding, hinging, and rotation. We describe our sensing principle and how we designed the size, shape, and layout of our sensor coils to optimize sensitivity, sensing range, recognition and tracking accuracy. Through several studies, we also demonstrated the performance of our proposed sensing technique in environments with varying levels of noise and interference conditions. We conclude by presenting demo applications on a smartwatch, as well as insights and lessons we learned from our experience.
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- PAR ID:
- 10101353
- Date Published:
- Journal Name:
- Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology
- Page Range / eLocation ID:
- 321 to 333
- 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
- Conference Paper:
- https://doi.org/10.1145/3242587.3242662
