Citation: Wang, Y.; Zheng, L.; Yang,
J.; Wang, S. A Grip Strength
Estimation Method Using a Novel
Flexible Sensor under Different Wrist
Angles. Sensors 2022, 22, 2002.
https://doi.org/10.3390/s22052002
Academic Editor: Chi Hwan Lee
Received: 21 January 2022
Accepted: 1 March 2022
Published: 4 March 2022
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Article
A Grip Strength Estimation Method Using a Novel Flexible
Sensor under Different Wrist Angles
Yina Wang
1,
*, Liwei Zheng
1
, Junyou Yang
1
and Shuoyu Wang
2
1
School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China;
zhengliwei@smail.sut.edu.cn (L.Z.); junyouyang@sut.edu.cn (J.Y.)
2
Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology,
Kami 7828502, Japan; wang.shuoyu@kochi-tech.ac.jp
* Correspondence: wang.yina@sut.edu.cn
Abstract:
It is a considerable challenge to realize the accurate, continuous detection of handgrip
strength due to its complexity and uncertainty. To address this issue, a novel grip strength estimation
method oriented toward the multi-wrist angle based on the development of a flexible deformation
sensor is proposed. The flexible deformation sensor consists of a foaming sponge, a Hall sensor, an
LED, and photoresistors (PRs), which can measure the deformation of muscles with grip strength.
When the external deformation squeezes the foaming sponge, its density and light intensity change,
which is detected by a light-sensitive resistor. The light-sensitive resistor extended to the internal
foaming sponge with illuminance complies with the extrusion of muscle deformation to enable rela-
tive muscle deformation measurement. Furthermore, to achieve the speed, accuracy, and continuous
detection of grip strength with different wrist angles, a new grip strength-arm muscle model is
adopted and a one-dimensional convolutional neural network based on the dynamic window is
proposed to recognize wrist joints. Finally, all the experimental results demonstrate that our proposed
flexible deformation sensor can accurately detect the muscle deformation of the arm, and the designed
muscle model and convolutional neural network can continuously predict hand grip at different
wrist angles in real-time.
Keywords: deformation sensor; flexible grip; muscle model; strength estimation
1. Introduction
With global aging, the nonfatal injury rate of people has been increasing [
1
]. The
number of disabled people in the world has reached 1 billion, accounting for 15% of the
total population [
2
]. Physical disability seriously affects disabled people’s daily activity,
quality of life, and mental health, especially for patients with upper limb disabilities or
amputations, whose daily activity is severely inconvenient despite a healthy body physical
performance. Therefore, to guarantee the patient’s quality of life while adding no more
burden on the family, the exploitation of artificial limbs is highly desirable. In recent years,
prostheses for upper limbs have seen great development and a rapidly increasing amount
of research [3,4].
With the development of computer-assisted medical technology, human–machine
interface technology [
5
–
7
] has been widely used in the field of rehabilitation medicine,
especially in the field of functional assistance for the disabled [
8
–
10
]. Human–machine
interface technology aims to establish communication between humans and computers
by using biological signals of the human body itself. The computer receives commands
from human biological signals directly and controls some external devices to complete
the corresponding actions. All kinds of human actions are directed by the brain and
nervous system, and all kinds of actions are carried out by muscle activities. Therefore,
to help the disabled limbs recover their functions, the people hope that the disabled
Sensors 2022, 22, 2002. https://doi.org/10.3390/s22052002 https://www.mdpi.com/journal/sensors
