TY - JOUR
T1 - A Novel Finger Kinematic Tracking Method Based on Skin-Like Wearable Strain Sensors
AU - Yao, Shanshan
AU - Vargas, Luis
AU - Hu, Xiaogang
AU - Zhu, Yong
N1 - Funding Information:
Manuscript received December 19, 2017; revised January 28, 2018; accepted January 29, 2018. Date of publication February 5, 2018; date of current version March 9, 2018. This work was supported by the National Science Foundation under Award IIS-1637892. The associate editor coordinating the review of this paper and approving it for publication was Prof. Subhas C. Mukhopadhyay. (Shanshan Yao and Luis Vargas contributed equally to this work.) (Corresponding author: Xiaogang Hu; Yong Zhu.) S. Yao is with the Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695 USA (e-mail: syao2@ncsu.edu).
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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.
AB - 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.
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U2 - 10.1109/JSEN.2018.2802421
DO - 10.1109/JSEN.2018.2802421
M3 - Article
AN - SCOPUS:85041692855
SN - 1530-437X
VL - 18
SP - 3010
EP - 3015
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 7
ER -