Highly stretchable fiber sensors have attracted significant interest recently due to their applications in wearable electronics, human–machine interfaces, and biomedical implantable devices. Here, a scalable approach for fabricating stretchable multifunctional electrical and optical fiber sensors using a thermal drawing process is reported. The fiber sensors can sustain at least 580% strain and up to 750% strain with a helix structure. The electrical fiber sensor simultaneously exhibits ultrahigh stretchability (400%), high gauge factors (≈1960), and excellent durability during 1000 stretching and bending cycles. It is also shown that the stretchable step‐index optical fibers facilitate detection of bending and stretching deformation through changes in the light transmission. By combining both electrical and optical detection schemes, multifunctional fibers can be used for quantifying and distinguishing multimodal deformations such as bending and stretching. The fibers’ utility and functionality in sensing and control applications are demonstrated in a smart glove for controlling a virtual hand model, a wrist brace for wrist motion tracking, fiber meshes for strain mapping, and real‐time monitoring of multiaxial expansion and shrinkage of porcine bladders. These results demonstrate that the fiber sensors can be promising candidates for smart textiles, robotics, prosthetics, and biomedical implantable devices.
Boosting Lying Posture Classification with Transfer Learning
Automatic lying posture tracking is an important factor in human health monitoring. The increasing popularity of the wrist-based trackers provides the means for unobtrusive, affordable, and long-term monitoring with minimized privacy concerns for the end-users and promising results in detecting the type of physical activity, step counting, and sleep quality assessment. However, there is limited research on development of accurate and efficient lying posture tracking models using wrist-based sensor. Our experiments demonstrate a major drop in the accuracy of the lying posture tracking using wrist-based accelerometer sensor due to the unpredictable noise from arbitrary wrist movements and rotations while sleeping. In this paper, we develop a deep transfer learning method that improves performance of lying posture tracking using noisy data from wrist sensor by transferring the knowledge from an initial setting which contains both clean and noisy data. The proposed solution develops an optimal mapping model from the noisy data to the clean data in the initial setting using LSTM sequence regression, and reconstruct clean synthesized data in another setting where no noisy sensor data is available. This increases the lying posture tracking F1-Score by 24.9% for left-wrist and by 18.1% for right-wrist sensors comparing to the case without mapping.
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- Award ID(s):
- 2210133
- NSF-PAR ID:
- 10325314
- Date Published:
- Journal Name:
- The 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
