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 relative 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.
Highlights
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
A handgrip estimation system based on a flexible deformation sensor is proposed in this paper
The system can be used to estimate the handgrip strength at different wrist angles with good accuracy and stability
Summary
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. 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. Researchers have decoded the surface electromyograph (sEMG) signal [11,12] and EEG [13] signals of disabled patients to obtain the intention of the patients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
