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"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.

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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. "

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Parkinson menyerang 1% dari populasi orang berusia di atas 65 tahun di dunia, dengan salah satu gejala utama kondisi resting tremor pada bagian tangan pasien. Akuisisi informasi mengenai status kesehatan dari pasien parkinson hingga saat ini masih hanya dapat dilakukan ketika pasien menemui dokter dengan menghampiri klinik atau rumah sakit, saat aspek-aspek dari penyakit dapat menunjukkan fluktuasi pada periode di antara kunjungan tersebut. Pada penelitian ini, dirancang sistem monitoring alat EMG nirkabel yang dapat digunakan sebagai alat home monitoring, sehingga pasien dapat diperiksa setiap saat dan dengan frekuensi lebih sering agar perkembangan terhadap kesehatan pasien dapat selalu diperbarui. Sistem monitoring berikut memanfaatkan web server dan NodeMCU yang dilengkapi dengan ESP8266. Data yang berhasil terakuisisi tersebut kemudian diproses dengan transformasi wavelet diskrit (DWT) dan ekstraksi fitur RMS, logRMS, MAV, dan RES Index pada enam jenis gerakan yang berbeda untuk dibandingkan satu dengan lainnya. Melalui analisa yang telah dilakukan, didapatkan bahwa fitur yang memiliki pola penyebaran terbaik adalah standar deviasi dengan nilai RES Index sebesar 2,4. Selain itu, didapatkan pula bahwa setiap gerakan baik normal maupun dalam kondisi bergetar memiliki pola gelombang serta nilai fitur ekstraksi yang berbeda-beda. Dengan demikian, pola gerakan pasien Parkinson dapat dibedakan satu dengan lainnya melalui pembacaan sinyal EMG.

 

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Parkinson's disease attacking 1% of the elder population in this world, with one of the main symptoms of this disease being resting tremor. Until now, Parkinsons disease information acquisition can only be done through patients' visits to clinics or hospitals, whereas the aspects of the disease are also shown during in-between those visits.  Therefore, an EMG device that could be used as a remote-monitoring device is designed in this research. This is so that patients can be checked more frequently and hopefully results in an always-renewed information of the patients' developments. This monitoring system utilizes NodeMCU with ESP8266 and web server. The data acquired are then processed through discrete wavelet transform (DWT) method. To compare six kind of gestures, features like RMS, logRMS, MAV, standard deviation, and RES Index are extracted from the reconstructed EMG signal. The results show that standard deviations plot has the best pattern separation with an RES Index score of 2.4 and every movement or gesture, whether its in normal or vibrating condition, have its own pattern of signal and different values of extracted features. Conclusively, the movement pattern of Parkinsons disease patients can be interpreted from one another using EMG signal reading.

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Brain-Machine Interface (BMI), atau saat ini juga terdapat Hybrid Brain-Machine Interface (hBMI),teknologi yang saat ini sedang berkembang pesat. Teknologi ini juga telah diaplikasikan pada berbagai bidang. BMI adalah sistem yang secara langsung mengubah pikiran seseorang dari otak menjadi sebuah informasi yang dapat diproses untuk mengartikannya menjadi informasi yang dapat dipahami manusia. BMI ini juga memiliki pengembangan lanjut dimana sinyal otak digabungkan oleh sinyal biologis lain seperti electromyography (EMG), electrooculography (EOG), atau juga electrocardiography (ECG). Pengembangan teknologi ini memiliki aplikasi sebagai alat bantu rehabilitasi untuk seseorang yang menderita ketidakmampuan dalam menggerakkan anggota tubuhnya, seperti tangan. Melalui penelitian ini diharapkan untuk dapat merancang sistem pengendalian orthosis sebagai alat bantu rehabilitasi dengan menggunakan metode klasifikasi dengan sinyal otak dan sinyal otot, sehingga subjek yang menggunakan alat ini dapat melakukan rehabilitasi dalam pergerakan lengan atas khususnya pada sendi siku. Hasil klasifikasi gerakan dengan menggunakan sinyal otak dan sinyal otot ini, dengan menggunakan fitur delta alpha rasio dan root mean square, didapatkan akurasi training untuk tiga gerakan yakni relaks, fleksi, dan ekstensi yaitu sebesar 90.3% dan untuk akurasi testing sebesar 85.2%.

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Brain-Machine Interface (BMI) or also its advancement, hybrid brain machine interface (hBMI), is a technology that is vastly developed. This technology has been used in many fields. BMI is a system that directly changes human’s mind into information that can be extracted to informations that can be meaningful to people. BMI also has advancement in which the brain signal is combined with other biopotential signal such as electromyography (EMG), electrooculography (EOG), or electrocardiography (ECG). The development of this technology has applications as a rehabilitation aid for someone suffering from an inability to move his limbs, such as the hands. Through this research it is hoped to be able to design an orthosis control system as a rehabilitation device by using a classification method with brain signals and muscle signals, so that subjects who use this tool can carry out rehabilitation in upper arm movements especially in the elbow joint. The results of the movement classification using brain signals and muscle signals, using the delta alpha ratio and root mean square features, obtained training accuracy for three movements namely relax, flexion, and extension of 90.3% and for testing accuracy of 85.2%.

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"[ABSTRAKLATAR BELAKANG. Kelemahan otot yang terjadi pada 75% - 80% pasien stroke menyebabkan terbatasnya aktivitas pasien stroke. Cara pengukuran kekuatan otot yang mudah digunakan di klinis adalah dengan hand-held dinamometer, sedangkan untuk mengukur kemampuan berjalan yang cukup efektif pada pasien stroke adalah uji jalan 2 menit. Telah banyak penelitian yang mendapatkan adanya hubungan antara kekuatan otot sisi paresis dengan kemampuan berjalan pada pasien stroke, namun belum ada yang menghubungkan antara kekuatan otot tersebut dengan uji jalan 2 menit. Tujuan penelitian ini untuk mengetahui korelasi antara kekuatan otot sisi paresis dengan kemampuan berjalan pada pasien stroke kronik yang diukur dngan uji jalan 2 menit dan mengetahui kelompok otot yang paling berpengaruh terhadap kemampuan berjalan tersebut.METODE. Pada 28 subyek penelitian yang memenuhi kriteria dan bersedia mengikuti penelitian dilakukan pengukuran kekuatan otot tungkai sisi paresis, yaitu otot ekstensor panggul, fleksor panggul, ekstensor lutut, fleksor lutut, dorsifleksor pergelangan kaki, plantarfleksor pergelangan kaki, dengan hand-held dinamometer, kemudian dilakukan uji jalan 2 menit. Data demografis dan klinis pasien dikumpulkan dan dicatat.HASIL. Diperoleh korelasi positif yang bermakna antara kekuatan otot tungkai sisi paresis yaitu otot ekstensor panggul, fleksor panggul, ekstensor lutut, fleksor lutut, dorsifleksor pergelangan kaki, dan plantarfleksor pergelangan kaki dengan jarak tempuh berjalan yang diuji dengan uji jalan 2 menit (r= 0,410 hingga r = 0,645) . Diperoleh korelasi positif yang bermakna antara kekuatan otot tungkai sisi paresis yaitu otot ekstensor panggul, fleksor panggul, ekstensor lutut, fleksor lutut, dorsifleksor pergelangan kaki, dan plantarfleksor pergelangan kaki dengan kecepatan berjalan yang diuji dengan uji jalan 2 menit (r= 0,409 hingga r = 0,641). Otot yang paling berpengaruh terhadap kemampuan berjalan pada pasien stroke kronik dengan nilai r tertinggi dan p terendah adalah otot plantarfleksor pergelangan kaki, diikuti dorsifleksor pergelangan kaki, fleksor panggul, fleksor lutut, ekstensor lutut, dan terakhir ekstensor panggul.KESIMPULAN. Terdapat korelasi positif antara kekuatan otot tungkai sisi paresis dengan kemampuan berjalan pada pasien stroke kronik, dengan otot yang paling berpengaruh terhadap kemampuan berjalan yaitu otot plantarfleksor pergelangan kaki.

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ABSTRACTBACKGROUND. Muscle weakness that occurs in 75% - 80% of stroke patients causing limited activity of stroke patients. The easy way to measure muscle strength in clinical is by using a hand-held dynamometer, whereas the effective way to measure the ability to walk in stroke patients is using a 2-minute walk test. Previous studies said that there was relationship between muscle strength in paretic side with walking ability in stroke patients, but there is still no study about correlation between the muscle strength with a 2 minute walk test. The purpose of this study to determine the correlation between muscle strength of the lower extremity of the paretic side with walking ability in patients with chronic stroke using 2 minutes walk test, and determine which muscle groups that has the best correlation with the ability of walking.METHOD. There were 28 subjects who were eligible and willing to participate in the research. They got measurements of lower extremity muscle strength of the paretic side (hip extensor muscles, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, ankle plantarflexor) using hand-held dynamometer, then 2 minutes walk test. Patient demographic and clinical data were collected and recorded.THE RESULTS. There are significant positive correlation between the strength of the lower extremity muscles of the paretic side (hip extensor, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, and ankle plantarflexor muscles) with the length of 2-minute walk test (r = 0.410 - r = 0.645). There are significant positive correlation between the strength of the lower extremity muscles of the paretic side (hip extensor, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, and ankle plantarflexor muscles) with walking speed of the 2-minute walk test (r = 0.409 - r = 0,641). The muscles with the best correlation with the ability of walking in patients with chronic stroke are the muscles with the highest r and the lowest p, plantarflexor ankle muscles, followed by dorsiflexor ankle muscles, hip flexors muscles, knee flexors muscles, knee extensor muscles, and hip extensor muscles.CONCLUSION. There are positive correlation between the strength of the lower extremity muscles of the paretic side with walking ability in patients with chronic stroke, and the muscles with the best correlation with the ability of walking are plantarflexor ankle muscles. ;BACKGROUND. Muscle weakness that occurs in 75% - 80% of stroke patients causing limited activity of stroke patients. The easy way to measure muscle strength in clinical is by using a hand-held dynamometer, whereas the effective way to measure the ability to walk in stroke patients is using a 2-minute walk test. Previous studies said that there was relationship between muscle strength in paretic side with walking ability in stroke patients, but there is still no study about correlation between the muscle strength with a 2 minute walk test. The purpose of this study to determine the correlation between muscle strength of the lower extremity of the paretic side with walking ability in patients with chronic stroke using 2 minutes walk test, and determine which muscle groups that has the best correlation with the ability of walking.METHOD. There were 28 subjects who were eligible and willing to participate in the research. They got measurements of lower extremity muscle strength of the paretic side (hip extensor muscles, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, ankle plantarflexor) using hand-held dynamometer, then 2 minutes walk test. Patient demographic and clinical data were collected and recorded.THE RESULTS. There are significant positive correlation between the strength of the lower extremity muscles of the paretic side (hip extensor, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, and ankle plantarflexor muscles) with the length of 2-minute walk test (r = 0.410 - r = 0.645). There are significant positive correlation between the strength of the lower extremity muscles of the paretic side (hip extensor, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, and ankle plantarflexor muscles) with walking speed of the 2-minute walk test (r = 0.409 - r = 0,641). The muscles with the best correlation with the ability of walking in patients with chronic stroke are the muscles with the highest r and the lowest p, plantarflexor ankle muscles, followed by dorsiflexor ankle muscles, hip flexors muscles, knee flexors muscles, knee extensor muscles, and hip extensor muscles.CONCLUSION. There are positive correlation between the strength of the lower extremity muscles of the paretic side with walking ability in patients with chronic stroke, and the muscles with the best correlation with the ability of walking are plantarflexor ankle muscles. , BACKGROUND. Muscle weakness that occurs in 75% - 80% of stroke patients causing limited activity of stroke patients. The easy way to measure muscle strength in clinical is by using a hand-held dynamometer, whereas the effective way to measure the ability to walk in stroke patients is using a 2-minute walk test. Previous studies said that there was relationship between muscle strength in paretic side with walking ability in stroke patients, but there is still no study about correlation between the muscle strength with a 2 minute walk test. The purpose of this study to determine the correlation between muscle strength of the lower extremity of the paretic side with walking ability in patients with chronic stroke using 2 minutes walk test, and determine which muscle groups that has the best correlation with the ability of walking.METHOD. There were 28 subjects who were eligible and willing to participate in the research. They got measurements of lower extremity muscle strength of the paretic side (hip extensor muscles, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, ankle plantarflexor) using hand-held dynamometer, then 2 minutes walk test. Patient demographic and clinical data were collected and recorded.THE RESULTS. There are significant positive correlation between the strength of the lower extremity muscles of the paretic side (hip extensor, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, and ankle plantarflexor muscles) with the length of 2-minute walk test (r = 0.410 - r = 0.645). There are significant positive correlation between the strength of the lower extremity muscles of the paretic side (hip extensor, hip flexors, knee extensors, knee flexors, ankle dorsiflexor, and ankle plantarflexor muscles) with walking speed of the 2-minute walk test (r = 0.409 - r = 0,641). The muscles with the best correlation with the ability of walking in patients with chronic stroke are the muscles with the highest r and the lowest p, plantarflexor ankle muscles, followed by dorsiflexor ankle muscles, hip flexors muscles, knee flexors muscles, knee extensor muscles, and hip extensor muscles.CONCLUSION. There are positive correlation between the strength of the lower extremity muscles of the paretic side with walking ability in patients with chronic stroke, and the muscles with the best correlation with the ability of walking are plantarflexor ankle muscles. ]"

"ABSTRACTPergerakan suatu anggota tubuh adalah hasil dari usaha kolektif yang dilakukan oleh otak, saraf, dan kegiatan otot. Jika salah satu faktor penentu diatas tidak dapat berfungsi, maka pergerakan tidak dapat dilakukan. Hal itu mungkin menjadi sesuatu yang alamiah bagi mereka yang sejak awal kehilangan fungsi tubuhnya, namun mereka yang kehilangan fungsi tersebut setelah pergerakan menjadi bagian mendasar dari keseharian hidup mereka merupakan hal yang berbeda. Pada skripsi ini akan dibahas penelitian untuk merancang bangun alat akuisisi data sinyal Electromyograph EMG dengan menggunakan elektroda surface Ag-AgCl serta analisis kinerjanya. Sinyal 2-channel surface electromyograph SEMG didapatkan dari bagian ekstremitas atas tubuh yaitu Flexor Carpi Radialis yang kemudian akan difilter dengan serangkaian rancangan biopotential amplifier dan band-pass filter sebelum diproses menggunakan mikrokontroler. Selanjutnya sinyal yang didapat akan digunakan untuk klasifikasi dan spesialisasi pola gerakan tangan. Hasil pengujian menunjukan bahwa rangkaian filter yang dirancang telah menunjukan pola keluaran sinyal EMG dengan jelas. Karena karakteristik sinyal EMG yang berbeda pada setiap orang maka untuk melihat kinerja dari perangkat, pola yang dihasilkan dibandingkan dengan hasil jurnal yang sudah ada. Terlihat bahwa pola yang ditunjukan sudah sangat mirip dengan penelitian yang dilakukan sebelumnya dengan fluktuasi sinyal yang sangat intens ketika kerja selain rileks dilakukan.

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ABSTRACTLimb movement is the result of a collective effort done by the brain, nerves, and muscle activity. If one of the above determinants does not work, the movement can not be performed. It may be natural for those who have lost their bodily functions from the very beginning, but those who lose their function after the movement becomes a fundamental part of their daily lives are different. In this research Electromyograph signal data acquisition EMG by using AgCl surface electrode will be designed. The 2 channel surface electromyograph SEMG signal is obtained from the upper extremity of the body, the Flexor Carpi Radialis which will then be filtered with a series of filter before being processed using a microcontroller. Furthermore, the signal obtained will be used for classification and specialization of hand movement patterns. The test results show that the designed filter circuit has shown EMG signal output pattern clearly. Due to the characteristics of different EMG signals in each person, to see the performance of the device, the resulting pattern is compared with the results pattern of an existing journal. It is seen that the pattern shown similarity to previous research with very intense signal fluctuations when muscle being contracted. "

"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.

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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."

"Rancangan sistem kontrol lengan robot dengan menggunakan sinyal elektromiogram (EMG) telah dibuat dengan elektroda permukaan sebagai transduser. Sinyal EMG diolah dengan sistem pengolahan sinyal dan diakuisisi dengan menggunakan mikrokontroler H8/3069F . Data pengamatan ditampilkan dalam bentuk Graphical User Interface (GUI) yang dibuat dengan bahasa pemrograman Python dan disimpan dalam database Microsoft Access. Kontrol lengan robot dilakukan berdasarkan gerakan fleksi-ekstensi pergelangan tangan. Sinyal EMG dikarakterisasi berdasarkan root mean square (RMS) sehingga sinyal EMG dapat diklasifikasikan. Gerakan fleksi memiliki RMS antara 0.01 - 0.13 V dan gerakan ekstensi memiliki RMS antara 0.69 - 1.19 V. Sinyal EMG yang telah diklasifikasi ini digunakan sebagai input untuk mengontrol servo motor pada lengan robot.

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Designing control system of arm robot using electromyiogram (EMG) signal have been made with surface electrode as tranducer. EMG signal is processed by signal conditoning system dan acquired by microcontroller H8/3069F. Recording EMG signal is displayed on Graphical User Interface (GUI) with Python as programming language and stored in Microsoft Access database. Arm robot is controlled by flexion-extension of wrist joint movements. Extract feature EMG signal is determined by root mean square (RMS). RMS for each movements is vary, 0.01 - 0.13 V for flexion and 0.69 - 1.19 V for extension. These classification feature of EMG signal is used to control servo motor of arm robot."

"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.

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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."

"Dalam riset ini dikembangkan satu teknik baru untuk mendeteksi kerusakan dini pada satu elemen mesin dinamis, yaitu suatu bantalan gelinding. Dengan menggunakan bantalan gelinding sebagai benda uji elemen mesin, ternyata teknik baru yang dikembangkan tersbut bekerja dengan sangat baik sebagaimana yang diharapkan. Tahapan-tahapan eksperimen telah dilakukan dari percobaan simulasi computer sampai percobaan-percobaan simulasi langsung nyata (real-time). Eksperimen-eksperimen dalam penelitian ini terdiri dari pengembangan suatu rancangan khusus sensor AE, sekalian dengan pengembangan metode pemrosesan sinyal yang mampu menampilkan kembali sinyal asli AE dari ketertutupannya oleh sinyal-sinyal pengotor (noise) yang menyembunyikannya. Sinyal AE tersebut mempunyai frekuensi yang sangat tinggi, tetapi dengan amplitude yang sangat rendah sehingga sulit dideteksi secara langsung karena terkubur didalam sinyalsinyal pengotor lainnya. AE, Accoustic Emissions, adalah gelombang tegangan yang menjalar sebagai emisi akustik walaupun tidak ada hubungannya sama sekali dengan suara. Sensor-sensor rancangan khusus dikembangkan terlebih dahulu, dan kemudian metode-metode pemrosesan sinyal diteliti secara luas sampai ditemukan satu metode yang paling efektif dan cukup efisien. Metode pemrosesan sinyal yang terpilih tersebut dimodifikasi dan disesuaikan lagi agar cocok dengan penggunaan dan persyaratan dari sistem deteksi yang dikembangkan. Dengan kemampuan untuk mendeteksi penjalaran gelombang tegangan, maka kerusakan dini pada elemen mesin dinamis dapat ditentukan. Ini adalah dikarenakan bahwa pada kondisi saat-saat sangat awal yang kemudian tumbuhnya keretakan, bahan dari elemen mesin mengalami regangan sehingga melepaskan energi regangan tersebut dengan sangat cepat dengan membangkitkan gelombang-gelombang tegangan. Gelombang-gelombang tegangan ini akan menjalar ke semua arah dengan bentuk yang sama dengan gelombang suara, yaitu bentuk gelombanggelombang Raleigh atau bentuk gelombang P longitudinal.The development of a new technique to detect incipient damage in a dynamic machine component, i.e. rolling elements bearing, had been done in this research. By using a rolling element bearing as the machine component test object, it was revealed that this new developed technique performed quite well as expected. Experimental stages had been done from computer simulation to real time simulation tests. The experimental research consisted of the development of the special design AE sensor, as well as the development of the signal processing method that enhanced the AE signal out of the corrupting noise signals. The AE were very high frequency, but with very low amplitude that difficult to detect directly since they were burried under other noise signals. The stress waves were called as Accoustic Emissions (AE) even had nothing to do with sound. Special design sensors were developed first and then signal-processing methods were studied extensively. The chosen method was modified and adjusted in order to suit the requirements of the detection system. By the ability to detect the stress waves, the incipient failure of the dynamic machine component could be determined. This was dued to the conditions that at the beginning of the failure, and then in the crack growth, material were strained and would release its strain energy very quick which were generating stress waves. These stress waves were propagating to all direction with a same type of wave with the sound wave, i.e. Raleigh or longitudinal P waves."

"ABSTRAKEksoskeleton secara umum adalah sebuah struktur yang secara anatomis dirancang untuk mengakomodasi gerakan fisik pemakainya dan memberikan kekuatan tambahan. Salah satu tantangan dalam perancangan eksoskeleton adalah dalam menentukan metode kendali yang akan digunakan. Terdapat berbagai macam metode untuk mengendalikan eksoskeleton dan pada penelitian kali ini pengendalian dengan menggunakan sinyal EMG diujicobakan. Tujuan dari penelitian ini adalah untuk mencapai pengendalian optimum menggunakan sinyal EMG. Sinyal EMG adalah tegangan yang muncul ketika otot berkontraksi sehingga gerakan pengguna akan berkorelasi langsung dengan besar RMS sinyal EMG otot tersebut. Nilai RMS dari otot bicep dan tricep digunakan untuk menentukan arah gerak dan kecepatan rotasi motor DC yang menggerakkan eksoskeleton tangan kanan 1 DOF. Perhitungan nilai RMS dilakukan dengan memvariasikan panjang datanya (array length) yang secara teori akan mempengaruhi akurasinya. Selisih diantara kedua nilai RMS tersebut dihitung dan diinterpretasikan sebagai keinginan pengguna untuk melakukan gerakan fleksi atau ekstensi dan akan mengatur arah putaran motor DC. Nilai absolut dari selisih RMS tersebut yang kemudian dikalikan dengan konstanta (gain) digunakan untuk mengatur siklus kerja (duty cycle) sebuah sinyal PWM yang akan mengatur kecepatan putaran motor DC. Pengendalian sebuah sistem dikatakan baik jika settling time-nya kecil. Untuk mendapatkan settling time yang kecil, array length dan gain divariasikan. Pengujian dilakukan dalam dua tahap, yaitu pengujian statis dan dinamis. Hasil pengujian menunjukkan kecenderungan dimana settling time mengecil ketika array length makin pendek dan gain diperbesar. Hal tersebut menunjukkan bahwa pengendalian optimum dapat dicapai dengan memilih nilai array length dan gain yang tepat.

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ABSTRACTExoskeleton in general is a structure that is anatomically designed to be able to accommodate the physical movement of its user and provide additional strength. One of the biggest challenge in designing an exoskeleton is to determine the method of control that will be implemented. There are various control methods that can be used and the use of EMG signal to control a 1 DOF right arm exoskeleton is evaluated in this research. This research aims to achieve optimum control using EMG signal. EMG signal is a variation of voltage that occurs when a muscle contracts hence its strong correlation with the user?s intention of movement. The RMS values of each EMG signal that originates from bicep and tricep muscle are calculated and processed to determine the direction and speed of rotation of a DC motor that actuates the exoskeleton. The RMS calculation is conducted at various array length that will theoretically affect its accuracy. The difference between those two RMS values is then calculated and interpreted as the intention of flexion or extension movement that will control the DC motor rotation direction. The absolute value of the RMS difference multiplied with a gain factor is used to regulate the duty cycle of a PWM signal that is used to control the rotational speed of the DC motor. A good system control is characterized by its settling time, the smaller the better. To achieve the smallest settling time, array length and gain factor is varied. The test was conducted in two stages, static and dynamic test. The test result shows a trend where the settling time decreases when array length is shortened and gain is increased. It shows that optimum control can be achieved by selecting the right array length and gain."