Dataset Information

Clinical implementation of a bionic hand controlled with kineticomyographic signals.

ABSTRACT: Sensing the proper signal could be a vital piece of the solution to the much evading attributes of prosthetic hands, such as robustness to noise, ease of connectivity, and intuitive movement. Towards this end, magnetics tags have been recently suggested as an alternative sensing mechanism to the more common EMG signals. Such sensing technology, however, is inherently invasive and hence only in simulation stages of magnet localization to date. Here, for the first time, we report on the clinical implementation of implanted magnetic tags for an amputee's prosthetic hand from both the medical and engineering perspectives. Specifically, the proposed approach introduces a flexor-extensor tendon transfer surgical procedure to implant the tags, artificial neural networks to extract human intention directly from the implanted magnet's magnetic fields -in short KineticoMyoGraphy (KMG) signals- rather than localizing them, and a game strategy to examine the proposed algorithms and rehabilitate the patient with his new prosthetic hand. The bionic hand's ability is then tested following the patient's intended gesture type and grade. The statistical results confirm the possible utility of surgically implanted magnetic tags as an accurate sensing interface for recognizing the intended gesture and degree of movement between an amputee and his bionic hand.

SUBMITTER: Moradi A

PROVIDER: S-EPMC9433417 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Clinical implementation of a bionic hand controlled with kineticomyographic signals.

Moradi Ali A Rafiei Hamed H Daliri Mahla M Akbarzadeh-T Mohammad-R MR Akbarzadeh Alireza A Naddaf-Sh Amir-M AM Naddaf-Sh Sadra S

Scientific reports 20220831 1

Sensing the proper signal could be a vital piece of the solution to the much evading attributes of prosthetic hands, such as robustness to noise, ease of connectivity, and intuitive movement. Towards this end, magnetics tags have been recently suggested as an alternative sensing mechanism to the more common EMG signals. Such sensing technology, however, is inherently invasive and hence only in simulation stages of magnet localization to date. Here, for the first time, we report on the clinical i ...[more]

PMID: 36045214

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Project description:BackgroundGood hand hygiene is the most effective basic measure for preventing hospital-acquired infections. This research project, which originated from a project report on improving hand hygiene at a general hospital in Hangzhou, Zhejiang, China, aimed to investigate the effectiveness of hand hygiene improvement among the hospital staff.MethodsSince 2017, a hand hygiene improvement project involving the staff of a 2,500-bed general teaching hospital in Zhejiang, China, has been carried out. This study summarized the implementation and effectiveness of the project, which is based on the five factors of systematic evaluation. The research summary was divided into three phases: phase I (December 2017 to August 2018), phase 2 (September 2018 to April 2019), and phase 3 (May 2019 to December 2019). The data of hand hygiene compliance rates of different groups of professionals in the different research periods were statistically analyzed.ResultsThe results showed that continuous intervention led to a gradual increasing trend (Ptrend<0.001) in the hand hygiene implementation rate with as the intervention time and phases progressed. The hand hygiene compliance rates differed significantly during different phases (76.61%, 79.95%, and 83.34% in phases 1, 2, and 3, respectively, P<0.001). At the same time, the compliance rates of hand hygiene at each phase differed significantly between different professions (P<0.001, the compliance rate of hand hygiene among nurses was the highest and lowest among workers). The compliance rate of hand hygiene for different professions during the three phases were: nurses, 84.73%; doctors, 78.35%; interns, 77.62%; and other hospital workers, 72.79%.ConclusionsThe hand hygiene compliance rate was effectively improved among the hospital staff after the implementation of the hand hygiene improvement project. In this hospital, the project yielded remarkable results. Hand hygiene must be continuously practiced and improved to develop good habits. Effective and detailed planning as well as key factors, such as hand hygiene facilities, information monitoring, the active participation and response of employees, training and education, and supervision and feedback, could help to guarantee the effectiveness of the project.

Project description:A neuroprosthesis using a brain-machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects' ability to control a neuroprosthesis.Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition.The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student's t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition.Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.

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Clinical implementation of a bionic hand controlled with kineticomyographic signals.

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Clinical implementation of a bionic hand controlled with kineticomyographic signals.

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