Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

Esposito, Daniele; Centracchio, Jessica; Andreozzi, Emilio; Naik, Ganesh R.; Bifulco, Paolo

doi:10.3390/s21206863

Cited by 46 publications

(24 citation statements)

References 218 publications

(317 reference statements)

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“…Regarding the exoskeleton control system, it is worth pointing out that force-myography (FMG) represents a valid alternative to surface electromyography (sEMG) for monitoring muscle contraction and piloting human-machine interfaces [19,47]. FMG provides a ready to use signal (no need for signal processing) that allows fast activation times of the exoskeleton: just 0.9 s from the muscle trigger to complete closing or opening of the hand.…”

Section: Discussionmentioning

confidence: 99%

“…In the last few decades, research on motor rehabilitation after stroke has focused on robot-assisted strategies [19]. In this scenario, the use of exoskeletons has gained attention.…”

Section: Introductionmentioning

confidence: 99%

“…A more detailed classification considers the type of actuation (e.g., electrical motors, pneumatics, series elastic actuators, hydraulics), power transmission (e.g., mechanical links, gears, cables), sensing (e.g., potentiometers, encoders, EMG, EEG, force sensors) and control method (e.g., high-level, low-level continuous passive motion, master/slave). In particular, as shown in the literature, electromyography (EMG) is by far the most used technique for providing control to human-machine interfaces, including hand exoskeletons [19,20]. However, different issues are related to the use of EMG, such as: the need for electrodes and stable electrical contact; physiological cross-talk with other EMG signals from near muscles; electromagnetic interferences; demanding processing to obtain a suitable control signal (e.g., the EMG linear envelope (EMG-LE) proportional to the developed muscle strength) [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…In the last few decades, research on motor rehabilitation after stroke has focused on robot-assisted strategies [19]. In this scenario, the use of exoskeletons has gained attention.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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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“…Specific force sensors are employed, based on both the piezoresistive and piezoelectric effects, which are equipped with dome-shaped mechanical couplers to optimize the transduction of forces from the tissues to the sensors. Initially, FCG was acquired via piezoresistive force sensors [ 22 ], which had previously been demonstrated for muscle contraction monitoring [ 59 ], gesture recognition [ 60 ], and the control of biosignal-based human–machine interfaces [ 61 ] such as the “Federica Hand” prosthesis [ 62 , 63 , 64 , 65 ] and an upper-limb exoskeleton [ 66 ]. These piezoresistive FCG sensors have also been successfully used for respiratory monitoring [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

Centracchio

Esposito

Andreozzi

2022

Sensors

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The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland–Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R2 = 0.86) and MSi (R2 = 0.97); the Bland–Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content.

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Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

Cited by 46 publications

References 218 publications

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

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

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

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

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