Position Sensing and Control with FMG Sensors for Exoskeleton Physical Assistance

Islam, Muhammad Raza Ul; Xu, Kun; Bai, Shaoping

doi:10.1007/978-3-030-01887-0_1

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…Many, but not all, exoskeletons use exteroceptive sensors such as force sensitive resistors, load cells, EMG, and EEG. References [161][162][163] have used force-sensitive resistors (FSR) to estimate the different arm movements for exoskeleton control. Reference [164] has used FSR sensors for the payload estimation of robotic exoskeleton.…”

Section: Sensing and Estimationmentioning

confidence: 99%

“…Islam et al [161] has developed a FMG-based sensor that was initially used to estimate the upper-limb muscle contraction and relaxation. Later, it was upgraded to estimate the human upper-limb joint angles [163]. Based on muscle contraction and relaxation, the sensor data was processed to identify the joint angles via support vector machine algorithm.…”

Section: Adaptive Controlmentioning

confidence: 99%

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A Review on Design of Upper Limb Exoskeletons

2020

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Exoskeleton robotics has ushered in a new era of modern neuromuscular rehabilitation engineering and assistive technology research. The technology promises to improve the upper-limb functionalities required for performing activities of daily living. The exoskeleton technology is evolving quickly but still needs interdisciplinary research to solve technical challenges, e.g., kinematic compatibility and development of effective human–robot interaction. In this paper, the recent development in upper-limb exoskeletons is reviewed. The key challenges involved in the development of assistive exoskeletons are highlighted by comparing available solutions. This paper provides a general classification, comparisons, and overview of the mechatronic designs of upper-limb exoskeletons. In addition, a brief overview of the control modalities for upper-limb exoskeletons is also presented in this paper. A discussion on the future directions of research is included.

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Section: Sensing and Estimationmentioning

confidence: 99%

Section: Adaptive Controlmentioning

confidence: 99%

A Review on Design of Upper Limb Exoskeletons

2020

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“…This novel method using FSR sensor bands offers a robust and accurate alternative for human-robot interaction. The works presented in this paper and in previous studies (Islam et al, 2018;Islam and Bai, 2019) have shown that FSR-based sensor bands can be applied for control of upper-body assistive exoskeletons in different ways. Beside these, sensor bands can be applied for other types of applications of upper-limb and lower-limb exoskeletons.…”

Section: Discussionmentioning

confidence: 80%

Effective Multi-Mode Grasping Assistance Control of a Soft Hand Exoskeleton Using Force Myography

Islam

Bai

2020

Front. Robot. AI

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Human intention detection is fundamental to the control of robotic devices in order to assist humans according to their needs. This paper presents a novel approach for detecting hand motion intention, i.e., rest, open, close, and grasp, and grasping force estimation using force myography (FMG). The output is further used to control a soft hand exoskeleton called an SEM Glove. In this method, two sensor bands constructed using force sensing resistor (FSR) sensors are utilized to detect hand motion states and muscle activities. Upon placing both bands on an arm, the sensors can measure normal forces caused by muscle contraction/relaxation. Afterwards, the sensor data is processed, and hand motions are identified through a threshold-based classification method. The developed method has been tested on human subjects for object-grasping tasks. The results show that the developed method can detect hand motions accurately and to provide assistance w.r.t to the task requirement.

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“…23–26 The results indicate that performance continuously downgrades as the time difference between training and testing day increases. On the other hand, FMG as an alternative to detect upper and lower limb muscle activities has been used in different applications with healthy subject 27–37 and with stroke/amputated subjects. 38,39…”

Section: Introductionmentioning

confidence: 99%

A comparative study of motion detection with FMG and sEMG methods for assistive applications

Islam

Waris

Kamavuako

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.

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Position Sensing and Control with FMG Sensors for Exoskeleton Physical Assistance

Cited by 10 publications

References 7 publications

A Review on Design of Upper Limb Exoskeletons

A Review on Design of Upper Limb Exoskeletons

Effective Multi-Mode Grasping Assistance Control of a Soft Hand Exoskeleton Using Force Myography

A comparative study of motion detection with FMG and sEMG methods for assistive applications

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