Human muscle rigidity identification by human‐robot approximation characteristics framework on internet of things platform. Issue 6 (21st September 2021)

• Record Type: Journal Article
• Authors: Selvaraj, Rajalakshmi; Kuthadi, Venu Madhav; Baskar, S.
• Other Names: Montenegro‐Marin Carlos Enrique guestEditor.; Gaona‐Garcia Paulo Alonso guestEditor.; Nuñez Valdez Edward Rolando guestEditor.; Gao Honghao guestEditor.; Zhang Yudong guestEditor.; Hussain Walayat guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-21
• Subjects: electromyography -- human muscle rigidity -- human‐robot communication -- internet of things -- motion -- physical interaction -- wearable sensors; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12824
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: In the health care system and Internet of Things (IoT) platform, medical care robotics is becoming one of the quickest expanding areas of robot technology. The integration of robotics and human knowledge identifies human muscle rigidity from the healthcare data obtained from the wearable sensor. In an IoT platform, Electromyography is a method used for evaluating and tracking the electrical activity of muscles. The transferring of human muscle rigidity to a robot facilitates the robot to obtain resistive management initiatives in a useful and effective way while carrying out physical interaction activities in unstructured surroundings. The major challenges to overcome the unpredictability during physical interaction allow a robot to realize the individual behaviour with adaptability and versatility of muscles. Therefore, in this article, Human‐Robot Approximation Characteristics Framework (HRACF) has been proposed for developing physiological communication between humans and robots. HRACF permits robots to understand differential resistive abilities of muscles from human presentations. The pulses collected from Electromyography are used to retrieve human arm muscle rigidity during activity presentation. The characteristics of motion and rigidity are concurrently modelled using an estimation and approximation model with a logistic regression obtained by IoT devices. The analysed human arm muscle rigidity is then connected to the robot impedance regulator. HR model uses an optimized resistive approximator to measure the creative variables of the robot and continue driving to monitor the quoted pathways at the time of interaction. The relationship between motion data and rigidity data is systematically coded in the HR model. HRACF makes it possible to detect uncertainties through space and time that facilitates the robot to meet rigidity specification to 98[Nm/Rad] and error rate to 0.15% during physical interaction.