DEVELOPMENT OF AN ALGORITHM FOR IDENTIFICATION OF TERRAIN IN A LOWER LIMB PROSTHESIS CONTROL SYSTEM

Статья посвящена разработке алгоритма определения типа местности, по которой перемещается пользователь протезом нижней конечности. Идентификация местности, по которой происходит движение, является важной задачей при управлении протезами нижних конечностей, так как на разных типах местности протез должен совершать разные модели движений. В данном исследовании высказано предположение о возможности определения типа местности с помощью электромиографических датчиков, которые записывают сигналы от определенных мышц пользователя протезом. Чтобы подтвердить это утверждение, было проведено исследование. Испытуемые выполняли несколько типов движений: движение прямо, подъем и спуск по лестнице, подъем и спуск по наклонной поверхности. Электромиографические сигналы регистрировались от разных мышц нижних конечностей испытуемых. После первичной обработки из сигналов были выделены параметры биоэлектрического сигнала, чаще всего используемые в управлении протезами. Результаты исследования показали, что существует статистическая разница в некоторых параметрах сигнала в зависимости от канала регистрации сигнала и типа местности, на которой происходит движение. Исследование доказало возможность определения типа местности по четырем комбинациям параметр сигнала - мышца. На основании полученных результатов предложен алгоритм идентификации местности The article is devoted to the development of an algorithm for determining the type of terrain along which the user moves with a lower limb prosthesis. Identification of the terrain along which the movement takes place is when controlling the prostheses of the lower extremities, since on different types of terrain the prosthesis must perform different models of movements. In this study, it is assumed that it is possible to determine the location using electromyographic sensors that record signals from the muscles of the wearer with a prosthesis. Research has been done to confirm this claim. The subjects performed several types of movements: straight movement, climbing and descending stairs, ascending and descending an inclined surface. Electromyographic signals were recorded from different muscles of the lower end objects. After primary processing, the parameters of the bioelectric signal were extracted from the signals. The research results show that there is a statistical difference in some signal parameters depending on the signal registration channel and the type of terrain on which the movement takes place. The study proved the possibility of determining the type of terrain by four combinations of signal parameter - muscle. Based on the results obtained, an algorithm for identifying the area is proposed

THE IMPLEMENTATION OF THE HUMAN COMPUTER INTERACTION SYSTEM BASED ON BIOELECTRICITY

The electroencephalogram (EEG) evaluates brain waves, whereas the electrooculogram (EOG) evaluates blinking eye signals. To use bioelectric signals in bio-metric and clinical applications, preprocessing of the signal needs to be done. Signals are used to transmit data in nearly every sector of life including technology, manufacturing, and electronics, etc. Nowadays HCI is based on bioelectric signal has got loads of demand. Almost every sector bioelectrical signals are used especially in the medical sector and researches. In this paper, two bioelectric signals are used that is EEG, EOG. Two HCI (Human-Computer Interaction) systems were designed which is based on two kinds of bioelectric signals is EOG and EEG. The signal is transmitted by wireless mode only because in a wired HCI System user is not comfortable. Here HCI system contains three sections first is signal acquisition and signal transmission module second is EOG and EEG handling module and the last one is function implementing modular. This paper deals with the concentration level and meditation level of a person which is very useful in sports and other researches.

Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm could maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.

Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm can maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.

Wheelchair for Quadriplegic Patient with Electromyography Signal Control Wireless

<p class="0abstract">Quadriplegia is a paralysis condition in both arms and legs so that the patient is only able to move his neck and head. Manual or electric wheelchairs with joystick or switch control as a tool for people with paralysis certainly cannot be controlled independently by quadriplegia sufferers. This study aimed to help quadriplegia sufferers not to depend on others in carrying out daily activities by developing electric wheelchairs that can be controlled independently. The bioelectric signal which has only been used for diagnostic purposes can be utilized as an electric wheelchair control system for quadriplegia sufferers. In this study, electric wheelchairs were controlled by electromyography (EMG) signals from muscle contractions that can be driven by quadriplegia sufferers, namely the neck and face muscles. The increase in EMG signal amplitude during the muscle contraction is used as a trigger for the electric motor in a wheelchair to move forward, backward, turn right, and turn left. An electronic circuit for signal conditioning was used to amplify the EMG signal leads and filter frequencies that are not needed by the system before being processed by the microcontroller circuit. The use of wireless systems was developed to reduce the use of cables connecting electrodes to patients with electronic devices that will provide comfort to the user. Based on the results of the data collection on the wheelchair system, the detectability and selectivity values were for the 100% and 94% forward commands, 94.33% and 100% reverse commands, 92.31%, and 96% right turn commands and 97.96% and 94.12% left turn commands. The electric wheelchair system with EMG signal control is expected to help the mobility of quadriplegia sufferers.</p>

About bioelectric buffer system of the brain

Objective. Elaboration of objective quantitative criterion of electroencephalogram for estimation of the brain functional state in man. Маterials and methods. The background electroencephalograms analysis was conducted in 6 groups of the examined patients with various diagnosis (41 patients at all). Control group consisted of 7 patients, ageing 20 - 56 yrs (average age 35 yrs). Recording of EEG was conducted, using 16-channel electroencephalograph «NeuroCom standart» (KhАI - Меdika, Ukraine) in accordance to international system of recording «10-20». There were analyzed a quantity of meaningful interhemispheric asymmetries in accordance to power of summarized bioelectric signal in bilateral-synchronous points of the head in every group. The analysis time have constituted 1 min. Results. There was established, that the least meaningful difference in accordance to the bioelectrical signal power in bilateral-synchronous points of head may be considered in 1.4 times. Quantity of meaningful interhemispheric asymmetries in man may vary in large diapason - from 9 tо 25. Not all meaningful interhemispheric asymmetries in accordance to power of signals of separate rhythms are preserved while doing analysis of meaningful interhemispheric asymmetries in accordance to power of a summarized bioelectrical signal. Interhemispheric asymmetries in accordance to power of the summarized bioelectric signal in bilateral-synchronous points of the head may have more important informative meaning, than interhemispheric asymmetry in accordance to the signals power of separate rhythms. Conclusion. Quantity of meaningful interhemispheric asymmetries in accordance to power of signals of separate rhythms in healthy persons may vary from 16 tо 18. The interhemispheric asymmetries quantity reduction in accordance to power of the summarized bioelectric signal, comparing with quantity of interhemispheric asymmetries in accordance to power of signals of separate rhythms more than in 4 times, witnesses presence of the brain bioelectrical buffer system.

Monitoring the level of infection by COVID-19: an previous experiment to possibility of future application to the C-reactive protein detection by bioelectric signals

C-reactive protein (CRP) is a marker of inflammation and infection, and is altered in COVID-19 patients. 2-methacryloyloxyetyl phosphorylcholine (MPC) is a polymer containing phosphorylcholine, a protein that anchors CRP. The purpose of this work was to detect CRP by bioelectric signal resulting from its interaction with MPC. The signal acquisition system was elaborated using Arduino in conjunction with the Parallax Data Acquisition (PLX-DAQ) program for data transfer to Excel, which allowed the treatment of the obtained signal. 10 volunteers were also enrolled to provide blood samples for the purpose of using CRP on confectioned biomaterial containing MPC. After pipetting the volunteer's blood serum into the biomaterial containing MPC, it was possible to obtain a bioelectric signals from the interaction of MPC with CRP. It is concluded that it is possible to detect the presence of CRP by bioelectric signal, and that the use of MPC is promising for future application in collection strips, which would allow the quantification of CRP by portable electronic equipment. An application example would be monitoring the infection level of patients with COVID-19.