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Elbow angle estimation for medical rehabilitation device based on EMG sensor with ARIMAX method

AIP Conference Proceedings 2022年第1期2663卷

作者： Amelia Lita I. Made Mas Dwiyana Prasetya Wibawa Alinda Anggraini Akif Rahmatillah Riries Rulaningtyas Alfian Pramudita Putra Khusnul Ain 1Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia

In the development of medical rehabilitation tools, muscle signals or electromyographic (EMG) are often used as feedback from the body in order to facilitate normal movement patterns. Through this signal, the force, torque, and angle can be estimated to produce the desired movement in the robot to assist patients in rehabilitation therapy. There had been various research done on estimating the extremities' joint angle, including the system identification method with Auto-Regressive Integrated Moving Average with Exogenous Input (ARIMAX) model structure. This research aims to model biceps brachii's EMG signal and the elbow joint angle using the ARIMAX model structure. In modeling, the order of the model was selected using the methods of minimizing the AIC (Akaike Information Criteria) and RMSE (Root Mean Square Error) values. The reported results showed the comparison of the outcome and model orders from three data sets with a frequency sampling of 47.6690 Hz, l66.3248 Hz, and 935.6743 Hz, respectively. The frequency sampling of l66.3248 Hz results showed the best accuracy performance of the ARIMAX model structure. The model order which is selected based on the minimization of the RMSE value obtained the best estimate, while the order models were selected based on the minimization of the AIC value showed a more consistent performance on the data estimation and validation.

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Characterization of crystal structure and mechanical properties of polycaprolactone-hydroxyapatite-collagen nanofiber for scaffold anterior cruciate ligament (ACL) applications

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AIP Conference Proceedings 2022年第1期2663卷

作者： Faika Hanum Salsabilah Djony Izak Rudyardjo Sofijan Hadi Tahta Amrillah Aminatun Aminatun 1Program Study of Biomedical Engineering Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia 2Program Study of Physics Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia 3Program Study of Chemistry Department of Chemistry Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia 4Program Study of Nanotechnology Faculty of Advance technology and Multidiscipline-Universitas Airlangga Surabaya 60115 Indonesia

Recently, it was found that the case of knee injuries could reach a prevalence of 48 per 1000 peoples in one year, with 9% being categorized as Anterior Cruciate Ligament (ACL) injuries. The most common cause of ACL injuries is sports activities. Several strategies were used to heal this injury, such as a treatment, up to autographs bone graft surgery. Current research developments lead to the use of artificial ligaments to heal the ACL injuries which have many advantages over autografts. Thus, this present study aims to fabricate the artificial ligament using hydroxyapatite (HA)/collagen/polycaprolactone (PCL) nanofiber composites. Here, we try to determine the effect of variations of HA, collagen, and PCL compositions in the nanofiber composites by studying their crystal structure and mechanical properties (tensile strength). We use variations of PCL: HA: collagen compositions about of 50:45:5, 50:40:10, 50:35:15, 50:30:20, and 50:25:25 wt%, respectively, in the 10% w/v of solution concentrations. In this present research, the nanofiber was fabricated by using electrospinning method with a voltage of 23 kV, while, the distance between the needle and the collector of about of 15 cm. The XRD and tensile strength tests were used to study the effects of PCL: HA: collagen compositions. In the XRD test, we found that the PCL peaks were detected at 2θ = 21° and 23° and HA was found at 2θ = 31°. The PCL crystal size is found to be 47.82-86.87 nm and the degree of crystallinity is 24.31-36.87%. In the mechanical test, the Ultimate Tensile Strength (UTS) value was obtained in the range of 0.942 MPa-2.864 MPa. It was found that the PCL could increase the tensile strength. However, the obtained UTS value is still far from the UTS of the original human ACL value.

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Digital filter effect of the ventilator tester parameters to identify inflation phases

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AIP Conference Proceedings 2022年第1期2537卷

作者： Tomy Abuzairi Ahli Irfan Basari 1Electrical Engineering Study Program Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia 3Research Center for Biomedical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia 2Biomedical Engineering Study Program Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia

A ventilator is a breathing support device that serves as a therapeutic tool or treatment tool for patients with respiratory system failure caused by various health problems or diseases. On ventilators, there are crucial parameters that need to be monitored in advance, such as inspiration–expiration (I:E) ratio, respiration rate, tidal volume, peak inspiration pressure (PIP), positive end-expiratory pressure (PEEP), and oxygen concentration. To monitor those parameters, a ventilator tester is needed. In this paper, we analyzed the effect of a digital filter in the ventilator tester, COVENT-Tester, to identify the inflation phase in the ventilator parameters. The results show that the digital filter is not effective in measuring I:E ratio and respiration rate because of the delay in inspiration and expiration time. However, the digital filter is effective in measuring tidal volume, PIP, PEEP, and oxygen concentration.

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Sweat sensing in wearable sensor: A review of the future non-invasive technology for real-time health monitoring system

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AIP Conference Proceedings 2022年第1期2537卷

作者： Elly Septia Yulianti Nurul Intan Siti Fauziyah Rahman Basari 1Biomedical Engineering Study Program Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia 2Research Center for Biomedical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia

Wearable sensors are devices used in the diagnostic process for real-time health monitoring systems. Types of sensors are generally based on physical sensors and chemical sensors. However, the types of wearable sensors that are largely commercialized are limited to physical sensors. Some diagnostic processes generally use the blood collecting method to determine a patient’s condition. However, this method can cause adverse effects such as infection, pain, and bruising for some patients. Chemical-based wearable sensors are capable of detecting metabolic conditions in the body and can be an alternative to the existing method. Based on an approach using alternative analytes, sweat is the most frequently used because it is highly accessible and proceeds good parameters compared to the blood. Several studies related to wearable sensors using sweat have been done to increase convenience during the sampling process. Electrochemical, biosensor, and hybrid with several sampling methods, including microfluidic system, absorbent material, superhydrophobic-super hydrophilic surface, and iontophoresis, can be used for wearable sensor analysis method. Over the past decade, the characteristics of wearable sensors (i.e., selectivity, sensitivity, stability, and response time), textile materials, and biofuel cell technology for self-powered sensor development have also increased. From these classifications, it can be concluded that the method which is highly feasible to be developed is electrochemical wearable sensors that combine microfluidic and iontophoresis systems. The system is also expected to have stretchable material and is equipped with an integrated biofuel cell system that has the potential to be developed in our upcoming research topic.

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Design of BCI motor imagery classification using WPT-CSP and CNN

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AIP Conference Proceedings 2022年第1期2537卷

作者： Dio Alif Pradana Basari 1Biomedical Engineering Study Program Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia 2Research Center for Biomedical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia

This study proposes a new method in an electroencephalograph (EEG)-based Brain-Computer Interface (BCI) that can directly utilize brain signals to control external devices. The motor Imagery (MI) signal, which contains an image of a certain limb movement, is generally used in BCI. It does not need direct movements. The implementation of MI-EEG signal into BCI still experiences major issues because the patterns obtained for each recording can vary from one another even though they have the same type of motion. In this study, we utilized the Wavelet Packet Transform (WPT) method to decompose the EEG signal into specific sub-band frequencies and Common Spatial Pattern (CSP) as a spatial filter to increase the spatial resolution of the EEG signal. The Convolutional Neural Network (CNN) was subsequently selected for training from the classifier. Next, the results of the training were used to classify the movements of the given MI-EEG. We evaluated the model using dataset 2a from Brain-Computer Interface Competition (BCIC) IV. The results of this method showed the increase in the accuracy of 32% and in Kappa up to 0.42 and the decrease in Root Mean Square Error (RMSE) up to 1.21, compared with only using CNN as the classifier. These results showed fairly good performance compared to other methods used previously in dataset 2a from BCIC IV.

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Lumen and nuclei detection in histopathology of prostate cancer based on morphological feature extraction

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AIP Conference Proceedings 2023年第1期2554卷

作者： S. T. Yukiko Irliyani Riries Rulaningtyas S. Anny Rahaju I. Made Mas Dwiyana Prasetya Wibawa 1Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Airlangga University Surabaya Indonesia 2Anatomical Pathology Department Medical Faculty Universitas Airlangga Dr. Soetomo Academic Hospital Universitas Airlangga Hospital Surabaya Indonesia

Prostate cancer is the most common cancer in men. The results of a fast and precise classification can be a fundamental objective of reference. Histological assessment is valuable information for doctors. The most common assessment for histological assessment of prostate cancer is the Gleason Grading System. Gleason pattern detection was done by extracting morphological features in the form of Line Length, Area Fraction, and the comparison between Line Length and Area Fraction. All morphological features become the input of backpropagation. Based on the results of backpropagation testing, this method can classify Gleason pattern images with an accuracy of 91.67%. The threshold of segmentation in determining the lumen and nucleus did not have a specific range in its threshold due to the lack of uniform contrast in each image.

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Human-computer-interface for controlling the assistive technology device

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AIP Conference Proceedings 2020年第1期2314卷

作者： Osmalina Nur Rahma Maydiana Nurul Kurniawati Akif Rahmatillah Khusnul Ain 1Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Airlangga University Indonesia

Imagining a motion without doing the actual movement is known as Motor imaginary (MI). However, translating MI as an input for BCI that control the Assistive Technology (AT) device was a challenging method due to the extensive training required, poor user engagement, and visual feedback response delays. For a person who has difficulties to imagine without visual stimulation, more training should be conducted. Even with a visual stimulus, sometimes a person still finds it hard to distinguish between one movement and another. So, finding a suitable input to control an AT device that suitable for someone who has extremely limited movement is very challenging. In the other hand, EEG signal had been proven sufficient in detecting facial expression. Even though the EEG signal contains EMG facial artifact and considered as an unwanted signal for BCI, facial movement artifact could become a compensator signal for EEG based Human Computer Interface (HCI) system to improve efficiency. This study aims to develop HCI systems to distinguish three different movements (forward, backward, and stop) EEG signals that recorded from EEG EMOTIV Epoc based on different facial expression. This HCI system could bring benefit if it is implemented for AT device, such as rollator since the user could easily distinguish different movement without any visual stimuli required so could reduce an extensive training before usage and could improve the quality of life people with limited extremity movement.

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Application of gray level run length matrices features extraction for diabetic retinopathy detection based on artificial neural network

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AIP Conference Proceedings 2020年第1期2314卷

作者： Bestia Kumala Wardani Nathania Earlene Belinda Riries Rulaningtyas Endah Purwanti 1Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia

Diabetic Retinopathy (DR) is Diabetes Mellitus microvascular complication, which attacks the eyes and can cause blindness. The DR examination currently is done with the fundus photography technique. The image produced by the technique is studied by the ophthalmologist manually. This research aimed to develop a computer aided diagnosis in the DR detection system using Gray Level Run Length Matrices (GLRLM) features extraction and Artificial Neural Network. This DR detection was done by Gray Level Run Length Metrices (GLRLM) texture features extraction method. Five texture features used were: Short Run Emphasis (SRE), Long Run Emphasis (LRE), Gray Level Non-Uniformity (GLN), Run Length Non-Uniformity (RLN), and Run Perecentage (RP). The five features were used as Backpropagation Artificial Neural Network input. The parameters are varied in the Backpropagation Artificial Neural Network training phase. The data used in this research were 110 retinal fundus images consists of 20 normal retinal fundus images, 45 NPDR retinal fundus images, and 45 PDR retinal fundus images. The highest accuracy result gained in the training phase is 97.14% and testing phase is 92.5%. This detection program produces a sensitivity value of 93.33% and a specificity value of 90%, therefore, it can be used as an initial indicator to detect DR.

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Filter design on EMG multichannel based python programmer

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AIP Conference Proceedings 2020年第1期2314卷

作者： Prihartini Widiyanti Aji Sapta Pramulen Akif Rahmatillah 1Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia

Currently, EMG devices are used as recording and monitor device for muscle activity. However, the existing tools use licensed software that result in limited features provided. In addition, the inability of the tools to be able to record simultaneous movements involving several muscles at the same time is another problem regarding the design of EMG device systems. This research is done to design a multichannel EMG filter using python programming. Therefore, the design of this device consists of hardware in the form of instrumentation amplifier circuit, NI DAQ USB 6009 as data acquisition, and software part consisting of signal processing (filtering, rectifying, smoothing) and signal interface on Personal Computer (PC) using Python 2.7. The device testing is done in 2 ways, that is finance and tools from EMG itself. The instrumentation test got the strengthening value 15,241 times with linearity of 0.941. In addition, Butterworth IIR filter response is obtained according to the design of the cut off frequency of 15-450 Hz and 50 Hz. Performance testing of prototype EMG devices is done with compare of standard EMG and recording is done on normal male. Qualitatively the recording signal features using EMG of research with simultaneous movements identical to the recording signal features using standard EMG. The analysis of the results uses this type of qualitative analysis which sees how the similarity of the frequency range on the FFT of EMG research results with standard EMG in hospitals. The results obtained are the same frequency range at 15 – 450 Hz.

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Finite element analysis of posterior leaf spring ankle-foot orthosis for tibialis anterior atrophy case

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AIP Conference Proceedings 2022年第1期2663卷

作者： Alfian Pramudita Putra Arief Sofian Hidayat Akif Rahmatillah Pujiyanto Pujiyanto Dinda Cyberputri I. Putu Alit Pawana 1Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia 2Biomedical Signals and Systems Research Group Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia 3Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia 4Department of Rehabilitation and Physical Medicine Dr. Soetomo General Academic Hospital Surabaya Indonesia

Stroke is a neurological disease and is a significant cause of disability throughout the world. Patients with stroke or post-stroke usually still experience interference in walking in the form of a drop foot caused by atrophy of the anterior tibialis muscle, so they cannot perform dorsiflexion. Drop foot disorders can be treated by rehabilitation using Posterior Leaf Spring Ankle-Foot Orthosis (PLS AFO). PLS AFO has the advantage of a higher level of flexibility compared to other AFO types. The primary purpose of this research is to observe the level of resilience based on the influence of AFO thickness. This study uses the finite element analysis of three model variations in the initial contact, midstance, terminal stance, and calf loading phases. The material used in this study was carbon fiber. The simulation will result in the distribution of stress, strain, and deformation values in each AFO model. The simulation results show that AFO resistance is affected by variations in AFO thickness where the increase in strength is proportional to the increase in AFO thickness. The AFO with a thickness of 10 mm is the most suitable for the tibialis anterior atrophy case.

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