A Novel Finger Kinematic Tracking Method Based on Skin-Like Wearable Strain Sensors

Shanshan Yao, Luis Vargas, Xiaogang Hu, Yong Zhu

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Deficits in hand function are common in a majority of stroke survivors. Although hand performance can be routinely assessed during rehabilitation training, a lack of hand usage information during daily activities could prevent clinicians or therapists from making informative therapeutic decisions. In this paper, we demonstrated and validated the application of silver nanowire-based capacitive strain sensors for finger kinematic tracking. The fabricated strain sensors show high sensitivity (gauge factor close to one), low hysteresis, good linearity, large stretchability (150%), and skin-like mechanical property (Young's modulus of 96 kPa). All these features allow the sensors to be conformally attached onto the skin to track finger joint movement with minimal interference to daily activities. Recordings of the skin deformation from the strain sensors and joint angles from reflective markers are highly correlated (>93%) for different joint oscillation speeds in a stroke survivor and a control subject, indicating the high accuracy of the strain sensors in joint motion tracking. With the wearable silver nanowire-based strain sensors, accurate hand utility information on the impaired hand of stroke survivors can be acquired in a continuous and unobtrusive manner.

Original languageEnglish (US)
Pages (from-to)3010-3015
Number of pages6
JournalIEEE Sensors Journal
Volume18
Issue number7
DOIs
StatePublished - Apr 1 2018

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering

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