Human Machine Interfaces in Upper-Limb Prosthesis Control: A Survey of Techniques for Preprocessing and Processing of Biosignals

Ahmadizadeh, Chakaveh; Khoshnam, Mahta; Menon, Carlo

doi:10.1109/msp.2021.3057042

Cited by 33 publications

(26 citation statements)

References 56 publications

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“…Force-myography offers a valid alternative to EMG and is increasingly used for the control of human-machine interfaces, as showed in recent surveys [45,46]. Moreover, a further study [47] restates the high percentage of total rejection of EMG hand prostheses or the passive use of such devices, due to the control difficulties encountered by users.…”

Section: Discussionmentioning

confidence: 91%

The “Federica” Hand

et al. 2021

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Hand prostheses partially restore hand appearance and functionalities. In particular, 3D printers have provided great opportunities by simplifying the manufacturing process and reducing costs. The “Federica” hand is 3D-printed and equipped with a single servomotor, which synergically actuates its five fingers by inextensible tendons; no springs are used for hand opening. A differential mechanical system simultaneously distributes the motor force on each finger in predefined portions. The proportional control of hand closure/opening is achieved by monitoring muscle contraction by means of a thin force sensor, as an alternative to EMG. The electrical current of the servomotor is monitored to provide sensory feedback of the grip force, through a small vibration motor. A simple Arduino board was adopted as the processing unit. A closed-chain, differential mechanism guarantees efficient transfer of mechanical energy and a secure grasp of any object, regardless of its shape and deformability. The force sensor offers some advantages over the EMG: it does not require any electrical contact or signal processing to monitor muscle contraction intensity. The activation speed (about half a second) is high enough to allow the user to grab objects on the fly. The cost of the device is less then 100 USD. The “Federica” hand has proved to be a lightweight, low-cost and extremely efficient prosthesis. It is now available as an open-source project (CAD files and software can be downloaded from a public repository), thus allowing everyone to use the “Federica” hand and customize or improve it.

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Section: Discussionmentioning

confidence: 91%

The “Federica” Hand

et al. 2021

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“…They are caused by the movements of the sensor, which are increased by pressing or otherwise manipulating with the sensor, rapid movements of the parts of the body where the sensor is mounted, or changes in the balance of the electrode-skin interface caused by the muscle contraction leading to the changes in volume. It is possible to remove them using inter alia high-pass filters, which do not affect the measured frequency range of both EMG and ENG signals [59][60][61].…”

Section: Clinical Applicationsmentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

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Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

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“…Recently, electroencephalography (EEG), able to detect motor brain function, has increasingly been proposed as HMI control signals (these techniques are referred to as Brain-Computer Interfaces (BCIs) [5][6][7][8]). Human movement can also be captured by cameras (the so-called image-based HMIs), which do not require any physical contact with the user [9,10]. Different sensors can be combined to obtain greater sensitivity and specificity in recognizing the user's intention.…”

Section: Introductionmentioning

confidence: 99%

“…Baniqued et al [7] presented a review study on BCI robotics for motor rehabilitation of hand movements after stroke. Different surveys such as [9,10,[23][24][25][26][27][28] focused specifically on the state-of-the-art and control strategies of upper limb prostheses, while further reviews presented exergaming applications for rehabilitation neuromotor functions [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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

Esposito

Centracchio

Andreozzi

et al. 2021

Sensors

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As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.

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Human Machine Interfaces in Upper-Limb Prosthesis Control: A Survey of Techniques for Preprocessing and Processing of Biosignals

Cited by 33 publications

References 56 publications

The “Federica” Hand

The “Federica” Hand

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals

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

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