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"Perkembangan yang cepat di bidang Biosensing telah membuka gerbang kepada peneliti untuk mengeksplorasi penggunaan sinyal biologis. Salah satu dari penggunaan sinyal ini adalah Human-Computer Interface (HCI), yang memungkinkan seseorang untuk berinteraksi dengan komputer tanpa kontak fisik. Agar sebuah perangkat HCI dapat berfungsi dengan efisien, sensor-sensor yang digunakan untuk mengakuisisi sinyal biologis harus nyaman digunakan dan mudah dibawa. Pada Tugas Akhir ini penulis mengajukan sebuah desain modifikasi untuk OpenBCI Cyton biosensing board, yang akan menggantikan modul RFDuino RFD22302 dengan modul Espresif ESP-32. Proses penelitian meliputi modifikasi desain PCB, fabrikasi, pemrograman, sampai pengujian 2 iterasi. Pada pengujian iterasi pertama purwarupa berhasil mengidentifikasi kontraksi/aktivasi otot dengan amplitudo di sekitar 1 milivolt dan juga aktivitas ripple/noise yang berhasil dieliminasi pada iterasi kedua dengan perbaikan posisi perekaman sinyal. Perbandingan pola rekaman dengan Myoware muscle sensor juga menunjukkan kemiripan hasil yang menandakan kemiripan hasil satu sama lain dengan perbendaan yang minimum.......The rapid development in the field of Biosensing technology has allowed scientists to explore a multitude of biosignal application. One of this application is the Human-Computer Interface, which allows humans to directly control a machine without direct physical inputs. For a HCI device to be efficiently utilized, the sensors utilized in acquiring human biosignal must be somewhat comfortable to use and mobile. In this Bachelor’s Thesis the author proposed a modification design for OpenBCI Cyton biosensing board, which replaced its outdated RFDuino RFD22302 with the newer, widely used, and well-documented Espressif ESP-32. Research processes include the base PCB modification, its fabrication, programming, and 2 iteration of testing. First has shown that the prototype is capable of detecting EMG signals with the amplitude of around 1 millivolt but also the presence of noise/ripple, which was successfully eliminated in the second iteration with better recording positioning. Further comparison also has shown that the prototype’s recording result was highly similar with the recording result from Myoware muscle sensor with only slight differences."

"Objectives: (1) To assess the masticatory muscles activity in patients with Temporomandibular Disorder (TMD) before orthodontic treatment, (2) to determine the correlation between TMD and the masticatory muscles activity (masseter muscles and anterior temporalis muscles). Methods: Twenty-two patients with malocclusion before undergoing orthodontic treatment (8 males, 14 females; mean age of 26,78 ± 4.34 years) were enrolled in the study and were divided into two groups: 11 patients with TMD and 11 patients without TMD (Non- TMD). The masticatory muscles were evaluated using standardized electromyography during 5 seconds of maximum voluntary contraction (MVC) through cotton-roll biting. For statistical analysis, the root mean square (RMS) valueof masticatory muscles was calculated and compared between the two groups. Results: The TMD groups showed alower electromyographic activity than the non- TMD group during MVC, with no significant differences in the right and left masticatory muscles between these groups. A weak negative correlation and no statistically significant differences were found between TMD and the electromyography activity of masseter muscles. Conclusions: Patients with TMD had a lower electromyographic activity in the masticatory muscles than those without TMD. Thus, electromyography can be an objective parameter to assess muscles activity for TMDdiagnosis.......Objectives: (1) To assess the masticatory muscles activity in patients with Temporomandibular Disorder (TMD) before orthodontic treatment, (2) to determine the correlation between TMD and the masticatory muscles activity (masseter muscles and anterior temporalis muscles). Methods: Twenty-two patients with malocclusion before undergoing orthodontic treatment (8 males, 14 females; mean age of 26,78 ± 4.34 years) were enrolled in the study and were divided into two groups: 11 patients with TMD and 11 patients without TMD (Non- TMD). The masticatory muscles were evaluated using standardized electromyography during 5 seconds of maximum voluntary contraction (MVC) through cotton-roll biting. For statistical analysis, the root mean square (RMS) valueof masticatory muscles was calculated and compared between the two groups. Results: The TMD groups showed alower electromyographic activity than the non- TMD group during MVC, with no significant differences in the right and left masticatory muscles between these groups. A weak negative correlation and no statistically significant differences were found between TMD and the electromyography activity of masseter muscles. Conclusions: Patients with TMD had a lower electromyographic activity in the masticatory muscles than those without TMD. Thus, electromyography can be an objective parameter to assess muscles activity for TMDdiagnosis."

"Sistem kendali merupakan hal penting di dalam perancangan sebuah alat bantu berjalan untuk pasien pasca stroke yang mengalami hemiparetik pada kakinya. Sistem kendali yang baik harus mampu mengetahui keinginan bergerak atau berjalan dari manusia dan menerjemahkan keinginan tersebut menjadi sebuah gerakan yang alami melalui alat bantu berjalan yang umumnya digerakkan oleh sebuah perangkat DC motor. Sudah banyak penelitian yang telah dilakukan untuk melakukan deteksi terhadap keinginan manusia untuk bergerak atau berjalan melalui berbagai macam sensor yang dipasang pada otot-otot yang terkait. Fokus dalam penelitian ini adalah melakukan deteksi gaya berjalan melalui sinyal elektromiografi yang diperoleh dengan menggunakan sensor-sensor EMG yang dipasangkan pada permukaan 12 otot yang sangat berkaitan dengan gerakan atau gaya berjalan pada manusia. Adapun 12 otot ini terdiri dari 2 otot bahu yaitu Deltoid Anterior (DA) dan Deltoid Posterior (DP), dan 10 otot kaki yang terdiri dari Rectus Femoris (RF), Biceps Femoris (BF), Vastus Medialis (VM), Vastus Lateralis (VL), Tibialis Anterior (TA), Medial Gastrocnemius (MG), Soleus (S), Gluteus Maximus (GMax), Semitendinosus (ST), dan Peroneus Longus (PL). Sinyal elektromiografi dari 12 otot tersebut direkam dari 2 pasien sehat yang tidak mengalami gangguan berjalan, terdiri dari 1 orang pria dan 1 orang wanita. Sinyal tersebut kemudian diproses melalui aplikasi Matlab untuk dilakukan proses klasifikasi dengan menggunakan teknik Artificial Neural Network (ANN). Di samping itu, metode machine learning juga dilakukan yaitu dengan teknik Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN), yang bertujuan untuk mendapatkan perbandingan berbagai teknik tersebut agar didapatkan hasil dengan tingkat akurasi terbaik di dalam melakukan deteksi gaya berjalan yang dibedakan menjadi 3 yaitu: berjalan normal, naik tangga dan turun tangga. Hasil terbaik yang diperoleh dari penelitian ini dengan menggunakan algoritma ANN yang mampu menghasilkan prediksi sempurna dengan tingkat akurasi 100%, kemudian tingkat akurasi terbaik yang diperoleh dengan metode machine learning masing-masing untuk algoritma SVM adalah sebesar 99.2%, algoritma KNN sebesar 98.8% dan algoritma LDA sebesar 97.2% yang semuanaya diperoleh dari dataset kombinasi sinyal EMG otot bahu dan kaki. Hasil ini sangatlah penting di dalam penelitian yang akan dilakukan di kemudian hari dalam merancang sebuah sistem kendali yang mampu mengenali keinginan bergerak atau berjalan manusia baik saat berjalan normal maupun ketika hendak naik atau turun tangga sehingga alat bantu berjalan yang dihasilkan dapat digunakan dengan nyaman dan aman oleh pemakainya. ......Control strategy is a fundamental role and very important part to create a walking assistive device for patients after stroke with a hemiparetic leg. A good control strategy must have the ability to predict the human motion or walking intention and naturally deliver force by the walking assistive device thereafter. This force is usually generated by the electric actuator using direct-drive motor. Recently, many studies have addressed and put more interest in predicting the human motion intention through various sensors which put on the surface of related skeletal muscles. This study focuses on gait event detection using electromyography signals from 12 muscles comprise of 2 shoulder muscles those are Deltoid Anterior (DA) and Deltoid Posterior (DP) and 10 lower limb muscles those are Rectus Femoris (RF), Biceps Femoris (BF), Vastus Medialis (VM), Vastus Lateralis (VL), Tibialis Anterior (TA), Medial Gastrocnemius (MG), Soleus (S), Gluteus Maximus (GMax), Semitendinosus (ST), and Peroneus Longus (PL). The EMG signals are recorded unilaterally using surface EMG sensor from 2 healthy subjects without walking disorder, consist of 1 male and 1 female. The signals are processed on Matlab platform subsequently for classification process using Artificial Neural Network (ANN) technique. Besides, the machine learning methods are also used in this research i.e. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The purpose of using several methods is to output the comparison with highest accuracy result in predicting the gait events which are divided into 3 types: normal walking, stair ascent, and stair descent. The best outcome along this research is generated from ANN algorithm which could steadily predict without any error with accuracy rate 100%. Furthermore, the best results from machine learning method are 99.2% using SVM algorithm, 98.8% using KNN algorithm and 97.2% using LDA algorithm. All those performances are resulted from datasets with combination between EMG signals from shoulder and lower limb muscles. This achievement becomes a significant factor for the future studies to design a control strategy with good human-robot interaction that can recognize the human motion intention in each different gait event to contrive comfort and safety walking assistive device for the wearer. "