Locomotion mode classification using force myography

Godiyal, Anoop Kant; Pandit, Srinivas; Vimal, Amit Kumar; Singh, Upinderpal; Anand, Sneh; Joshi, Deepak

doi:10.1109/lsc.2017.8268158

Cited by 5 publications

(2 citation statements)

References 17 publications

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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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“…In classification discrete output states are predicted. In FMG, this method has been implemented to classify forearm, wrist and hand gestures [87,100,101,103] and also been used to identify locomotion modes and ankle positions [144][145][146]. The implementation of classification techniques involves three main steps i.e.…”

Section: Classificationmentioning

confidence: 99%

FMG based upper limb motion detection methods, performance analysis and control of assistive exoskeletons

Islam¹

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Exoskeletons are wearable devices designed to assist humans according to their needs. Their applications can be found in rehabilitation, assistance and power augmentation. For assistive powered exoskeletons human motion intention detection is an important element for implementing assistive control strategies. While many methods of motion detection have been developed, however, there still exists many challenges i.e. high robustness, convenience, data repeatability and applicability for implementation on assistive powered exoskeletons. Therefore, new methods that can fulfill these requirements are needed.The aim of this thesis is to develop novel methods of motion intention detection for control of exoskeletons. The focus of this thesis is to analyze the performance of force myography (FMG) to detect upper limb movements and based on it develop control methods for upper limb assistive exoskeletons.In this thesis performance of FMG is analyzed by comparing it with sEMG. Motion detection accuracy and data repeatability were compared for detecting forearm motions i.e. forearm flexion, extension, pronation, supination and rest. The study showed the feasibility of FMG when implemented for assistive powered exoskeleton control.Exoskeleton control with FMG is another focus of this thesis. FMG is first used to control a soft exoskeleton by detecting dynamic hand gestures i.e. rest, opening, closing and grasping. This study addressed the challenges associated with object grasping task i.e. amount of training data, robust detection and assistance level determination. The influence of sensor placement on detection performance was also experimentally analyzed.Finally, FMG based control method for upper limb exoskeleton, i.e. elbow and shoulder joint, is presented in this thesis. A machine learning based algorithm is developed for determining assistance level during object carrying tasks by estimating the carried payload. The performance of the method is analyzed by testing on healthy subjects. Whereas, the results of physical assistance are verified by comparing the results of load carrying tasks with and without exoskeleton.This thesis contributes to the state-of-the-art of upper limb motion iniii tention detection using FMG. Studies verify that FMG, being accurate and a convenient method to interpret motion intention, has great potential for application of assistive exoskeletons. A contribution of this thesis is performance analysis of muscle activity detection methods that compares FMG and sEMG in terms of accuracy/repeatability. Another contribution is the novel methods for grasping and load carrying. The proposed techniques are able to reduce system complexity for convenient and robust use in actual environment.

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A survey on the state of the art of force myography technique (FMG): analysis and assessment

Sherif,

Bassuoni,

Mehrez

2024

Med Biol Eng Comput

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Precise feedback assures precise control commands especially for assistive or rehabilitation devices. Biofeedback systems integrated with assistive or rehabilitative robotic exoskeletons tend to increase its performance and effectiveness. Therefore, there has been plenty of research in the field of biofeedback covering different aspects such as signal acquisition, conditioning, feature extraction and integration with the control system. Among several types of biofeedback systems, Force myography (FMG) technique is a promising one in terms of affordability, high classification accuracies, ease to use, and low computational cost. Compared to traditional biofeedback systems such as electromyography (EMG) which offers some invasive techniques, FMG offers a completely non-invasive solution with much less effort for preprocessing with high accuracies. This work covers the whole aspects of FMG technique in terms of signal acquisition, feature extraction, signal processing, developing the machine learning model, evaluating tools for the performance of the model. Stating the difference between real-time and offline assessment, also highlighting the main uncovered points for further study, and thus enhancing the development of this technique. Graphical abstract

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Locomotion mode classification using force myography

Cited by 5 publications

References 17 publications

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

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

FMG based upper limb motion detection methods, performance analysis and control of assistive exoskeletons

A survey on the state of the art of force myography technique (FMG): analysis and assessment

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