Journal
ACS APPLIED MATERIALS & INTERFACES
Volume 14, Issue 19, Pages 22666-22677Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsami.2c05823
Keywords
wearable electronics; fl exible strain sensor; fi ber Bragg grating; human motion monitoring; bionic
Funding
- National Natural Science Foundation of China [51905398, 52075398]
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In this study, a bionic stretchable optical strain sensor is developed, which enables large strain and bending angle measurements with temperature self-compensation. The sensor possesses excellent tensile strain range, high sensitivity, durability, and waterproofness, and is capable of measuring different human activities and achieving HMI control. By combining with machine learning techniques, gesture classification and motion intention of prosthetics can be achieved. This research contributes to medical care HMI and shows promise in smart medical and rehabilitation medicine.
Wearable integrated sensing devices with flexible electronic elements exhibit enormous potential in human-machine interfaces (HMI), but they have limitations such as complex structures, poor waterproofness, and electromagnetic interference. Herein, inspired by the profile of Lindernia nummularifolia (LN), a bionic stretchable optical strain (BSOS) sensor composed of an LN-shaped optical fiber incorporated with a stretchable substrate is developed for intelligent HMI. Such a sensor enables large strain and bending angle measurements with temperature self-compensation by the intensity difference of two fiber Bragg gratings' (FBGs') center wavelength. Such configurations enable an excellent tensile strain range of up to 80%, moreover, leading to ultrasensitivity, durability (>= 20,000 cycles), and waterproofness. The sensor is also capable of measuring different human activities and achieving HMI control, including immersive virtual reality, robot remote interactive control, and personal hands-free communication. Combined with the machine learning technique, gesture classification can be achieved using muscle activity signals captured from the BSOS sensor, which can be employed to obtain the motion intention of the prosthetic. These merits effectively indicate its potential as a solution for medical care HMI and show promise in smart medical and rehabilitation medicine. human-machine interaction, machine learning
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