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

Islam, Muhammad Raza Ul; Waris, Asim; Kamavuako, Ernest Nlandu; Bai, Shaoping

doi:10.1177/2055668320938588

Cited by 14 publications

(8 citation statements)

References 47 publications

(54 reference statements)

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“…The oldest research discussing FMG features is from 2017 [ 28 ], while most research has implemented FMG as raw signals for gesture recognition. The discussed features for force myography are primarily used in grasping detection, robot hand control, and gait analysis [ 28 , 29 , 30 , 31 , 32 ]. Many researchers have achieved hand gesture recognition based on various machine learning methods.…”

Section: Related Workmentioning

confidence: 99%

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

Barioul

Kanoun

2023

Sensors

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The recognition of hand signs is essential for several applications. Due to the variation of possible signals and the complexity of sensor-based systems for hand gesture recognition, a new artificial neural network algorithm providing high accuracy with a reduced architecture and automatic feature selection is needed. In this paper, a novel classification method based on an extreme learning machine (ELM), supported by an improved grasshopper optimization algorithm (GOA) as a core for a weight-pruning process, is proposed. The k-tournament grasshopper optimization algorithm was implemented to select and prune the ELM weights resulting in the proposed k-tournament grasshopper extreme learner (KTGEL) classifier. Myographic methods, such as force myography (FMG), deliver interesting signals that can build the basis for hand sign recognition. FMG was investigated to limit the number of sensors at suitable positions and provide adequate signal processing algorithms for perspective implementation in wearable embedded systems. Based on the proposed KTGEL, the number of sensors and the effect of the number of subjects was investigated in the first stage. It was shown that by increasing the number of subjects participating in the data collection, eight was the minimal number of sensors needed to result in acceptable sign recognition performance. Moreover, implemented with 3000 hidden nodes, after the feature selection wrapper, the ELM had both a microaverage precision and a microaverage sensitivity of 97% for the recognition of a set of gestures, including a middle ambiguity level. The KTGEL reduced the hidden nodes to only 1000, reaching the same total sensitivity with a reduced total precision of only 1% without needing an additional feature selection method.

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Section: Related Workmentioning

confidence: 99%

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

Barioul

Kanoun

2023

Sensors

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“…A previous study showed the high similarity between the EMG-LE and the signal obtainable from a custom force sensor capable of detecting the mechanical activity of muscles (referred to as force-myography (FMG)) [24]. The advantages deriving from the use of FMG instead of EMG are various [25]: the problems related to EMG recording and its processing are avoided; the force sensor output can be used as is (no processing is required) and has already been successfully demonstrated for various applications (hand prosthesis control [26][27][28][29]; hand gesture recognition [30,31]; physiological parameters monitoring [32][33][34]); moreover, a very recent study suggests that force sensors seem to perform better than EMG in daily activities for exoskeleton controls [35].…”

Section: Introductionmentioning

confidence: 99%

Design of a 3D-Printed Hand Exoskeleton Based on Force-Myography Control for Assistance and Rehabilitation

et al. 2022

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Voluntary hand movements are usually impaired after a cerebral stroke, affecting millions of people per year worldwide. Recently, the use of hand exoskeletons for assistance and motor rehabilitation has become increasingly widespread. This study presents a novel hand exoskeleton, designed to be low cost, wearable, easily adaptable and suitable for home use. Most of the components of the exoskeleton are 3D printed, allowing for easy replication, customization and maintenance at a low cost. A strongly underactuated mechanical system allows one to synergically move the four fingers by means of a single actuator through a rigid transmission, while the thumb is kept in an adduction or abduction position. The exoskeleton’s ability to extend a typical hypertonic paretic hand of stroke patients was firstly tested using the SimScape Multibody simulation environment; this helped in the choice of a proper electric actuator. Force-myography was used instead of the standard electromyography to voluntarily control the exoskeleton with more simplicity. The user can activate the flexion/extension of the exoskeleton by a weak contraction of two antagonist muscles. A symmetrical master–slave motion strategy (i.e., the paretic hand motion is activated by the healthy hand) is also available for patients with severe muscle atrophy. An inexpensive microcontroller board was used to implement the electronic control of the exoskeleton and provide feedback to the user. The entire exoskeleton including batteries can be worn on the patient’s arm. The ability to provide a fluid and safe grip, like that of a healthy hand, was verified through kinematic analyses obtained by processing high-framerate videos. The trajectories described by the phalanges of the natural and the exoskeleton finger were compared by means of cross-correlation coefficients; a similarity of about 80% was found. The time required for both closing and opening of the hand exoskeleton was about 0.9 s. A rigid cylindric handlebar containing a load cell measured an average power grasp force of 94.61 N, enough to assist the user in performing most of the activities of daily living. The exoskeleton can be used as an aid and to promote motor function recovery during patient’s neurorehabilitation therapy.

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“…Compared to EMG, FMG is robust to electrical interference and sweating, whilst also being non-invasive and inexpensive [ 9 , 10 ]. In the work of Islam et al [ 11 ], the performance of motion detection with FMG and surface electromyography (sEMG) were compared in a daily scenario. They tested four different limb motions in five healthy male subjects.…”

Section: Introductionmentioning

confidence: 99%

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Zheng

Zhao

et al. 2022

Biosensors

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Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

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

Cited by 14 publications

References 47 publications

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition

Design of a 3D-Printed Hand Exoskeleton Based on Force-Myography Control for Assistance and Rehabilitation

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

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