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

Esposito, Daniele; Centracchio, Jessica; Andreozzi, Emilio; Savino, Sergio; Gargiulo, Gaetano; Naik, Ganesh R.; Bifulco, Paolo

doi:10.3390/machines10010057

Cited by 36 publications

(22 citation statements)

References 49 publications

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“…Forcecardiography is a novel technique based on specific wearable force sensors that measure the local forces induced on the chest wall by the mechanical activity of the heart and lungs [ 110 , 111 , 112 , 113 , 114 , 115 ]. FCG signals were first acquired by means of sensors based on force-sensing resistors (FSR), which have already proved suitable for muscle contraction monitoring [ 116 ], gesture recognition [ 117 ], and the control of biosignal-based human–machine interfaces [ 118 ], such as the “Federica Hand” prosthesis [ 119 , 120 , 121 , 122 ] and an upper-limb exoskeleton [ 123 ]. The use of such FSR-based sensors has also been demonstrated for continuous respiratory monitoring [ 114 ].…”

Section: Methodsmentioning

confidence: 99%

Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

Andreozzi

Sabbadini

Centracchio

et al. 2022

Sensors

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Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels’ lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical–optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland–Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.

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Section: Methodsmentioning

confidence: 99%

Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

Andreozzi

Sabbadini

Centracchio

et al. 2022

Sensors

Self Cite

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“…Xiao et al [17] employed the ipsilateral setting in their study to help the participants familiarize themselves with the control of a RHO by using FMG sig- nals collected from their ipsilateral forearm. Esposito et al [18] implemented both, contra-and ipsilateral control and selected between those settings depending on the quality of the measurable FMG signal of the user. However, neither of these studies reported any quantitative results of the ipsilateral setting.…”

Section: Feasibility Of Controlling a Rhomentioning

confidence: 99%

“…Thus, for RHO which are primarily targeting assistive applications in activities of daily living, contralateral control is not fully representative. Only a few studies collected FMG signals from the forearm ipsilateral to the RHO [17,18], yet did not evaluate this strategy quantitatively.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of force myography for the direct control of an assistive robotic hand orthosis

Gantenbein

Ahmadizadeh

Heeb

et al. 2023

Preprint

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Background: Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated. Methods: In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined achieved classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance. Results: We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4% up to 100%. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance. Conclusion: The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.

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“… Application in medical. ( a ) Use FMG and machine learning techniques to differentiate between grasping and no grasping [ 169 ]; ( b ) textile electrodes integrated with a clothing belt for EIT lung imaging [ 170 ]; ( c ) assisted rehabilitation design of 3D printed hand exoskeleton based on FMG control [ 171 ]; ( d ) EMG biofeedback device for gait rehabilitation [ 172 ]; ( e ) detection of changes in lower extremity muscle impedance properties immediately after functional electrical stimulation-assisted cycling training in chronic stroke survivors [ 173 ]; ( f ) an evaluation of spontaneous respiratory idiopathic pulmonary fibrosis using EIT [ 174 ]. …”

Section: Figurementioning

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

Cited by 36 publications

References 49 publications

Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

Feasibility of force myography for the direct control of an assistive robotic hand orthosis

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

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