<p><em>Continuous Passive Motion (CPM) is a therapeutic tool to assist patients in moving their joints after the patient has experienced trauma or underwent joint surgery. The CPM machine is used to avoid joint stiffness in the patient. Joint stiffness can be caused because patients who have undergone joint surgery are reluctant to move their joints due to pain. The design of the CPM machine that will be carried out has motion in flexion and horizontal abduction. The choice of the type of DC motor as the driving force for the CPM machine will determine the ability (performance) of the CPM in carrying out the rehabilitation process for postoperative knee fractures. Movements in restoring and improving the range of motion of the elbow are flexion movements, extension movements, pronation movements and supination movements. From the results of the study, it can be concluded that there is one motor that meets the standards that can be used as a driving motor for Portable Continuous Passive Motion (CPM), namely the HY-2705B type motor. The HY-2705B motor meets minimum standards in terms of motor torque, motor rotation angle, working voltage, motor working temperature, and motor weight. For the other three motors Motor MG995, Motor 24H2A4428, and Motor DS3225MG there is one standard that is not met.</em>    </p><p> </p><p class="IndexTerms"><strong>Keywords</strong>: Continuous Passive Motion, CPM, Fracture, HY-2705B, MG995, 24H2A4428, DS3225MG</p><p> </p><p class="Abstract" align="center"><strong>ABSTRAK  </strong></p><p><em>Continuous Passive Motion</em><em> (CPM) </em>merupakan alat terapi untuk membantu pasien dalam menggerakkan sendi setelah pasien mengalami trauma atau menjalani operasi sendi. Mesin CPM digunakan untuk menghindari terjadinya kekakuan sendi pada pasien. Kekakuan sendi dapat disebabkan karena pasien yang telah menjalani operasi sendi enggan menggerakan sendinya akibat rasa nyeri. Perancangan mesin CPM yang akan dilakukan memiliki gerak secara fleksi dan horisontal abduksi. Pemilihan jenis motor DC sebagai penggerak mesin CPM sangat menentukan kemampuan (performa) CPM dalam melakukan proses rehabilitasi fraktur lutut pasca operasi. Gerakan-gerakan dalam mengembalikan dan memperbaiki lingkup gerak siku tangan adalah gerakan <em>flexion</em>, gerakan <em>extention</em> gerakan <em>pronation</em> dan gerakan supination. Dari hasil penelitian dapat disimpulkan bahwa terdapat satu motor yang memenuhi standar yang dapat digunakan sebagai motor penggerak untuk <em>Portable Continuous Passive Motion</em> (CPM) yaitu motor jenis HY-2705B. Motor HY-2705B memenuhi standar minimal baik dari torsi motor, sudut putar motor, tegangan kerja, suhu kerja motor, maupun berat motor. Untuk tiga motor yang lain Motor MG995, Motor 24H2A4428, dan Motor DS3225MG ada salah satu standar yang tidak terpenuhi.</p>

<p><em>One of the steps needed in patients with fractures of the knee joint is therapeutic exercises guided by physiotherapists. Therapeutic exercise is a physiotherapy modality by moving the joints both actively and passively. Inspired by this, the Portable Continuous Passive Motion (CPM) tool was created which is a physiotherapy aid for patients after surgery or knee surgery. Portable Continuous Passive Motion (CPM) has been developed based on IoT. Portable Continuous Passive Motion (CPM) uses Arduino LoRa as a micro controller, a stepper motor as a driver and uses a software application as a controller. The form of this research is research and development. From the results of the development, it can be concluded that Portable Continuous Passive Motion (CPM) can be useful for postoperative fracture patients as a practical and flexible postoperative manual therapy tool.</em></p>

<p><em style="text-align: justify; text-indent: 14.2pt;"><span style="font-family: 'Times New Roman',serif; mso-ansi-language: EN;" lang="EN">In pattern recognition, image processing plays a role in automatically separating objects from the background. In addition, the object will be processed by the pattern classifier. In the medical world, image processing plays a very important role. CT Scan (Computed Tomography) or CAT Scan (Computed Axial Tomography) is an example of an image processing application that can be used to view fragments or cross sections of parts of the human body. Tomography is the process of producing two-dimensional images from three-dimensional film through several one-dimensional scans. Magnetic resonance imaging (MRI) is the image most often used in the field of radiology. MRI images can display the anatomical details of objects clearly in multiple sections (multiplanar) without changing the patient's position. In this study, two methods were compared, namely K-Means and Fuzzy C Means, in a segmentation process with the aim of separating between normal areas or areas with disturbances (lesions). The images used are brain and chest MRI images with a total of 10 MRI images. The image quality of the segmentation results is compared with the quality test using the Variation of Information (VOI) parameters, Global Consistency Error (GCE), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and segmentation time.</span></em></p><pre style="text-align: justify; text-indent: 14.2pt;"><em><span style="font-family: 'Times New Roman',serif; mso-ansi-language: EN;" lang="EN">In pattern recognition, image processing plays a role in automatically separating objects from the background. In addition, the object will be processed by the pattern classifier. In the medical world, image processing plays a very important role. CT Scan (Computed Tomography) or CAT Scan (Computed Axial Tomography) is an example of an image processing application that can be used to view fragments or cross sections of parts of the human body. Tomography is the process of producing two-dimensional images from three-dimensional film through several one-dimensional scans. Magnetic resonance imaging (MRI) is the image most often used in the field of radiology. MRI images can display the anatomical details of objects clearly in multiple sections (multiplanar) without changing the patient's position. In this study, two methods were compared, namely K-Means and Fuzzy C Means, in a segmentation process with the aim of separating between normal areas or areas with disturbances (lesions). The images used are brain and chest MRI images with a total of 10 MRI images. The image quality of the segmentation results is compared with the quality test using the Variation of Information (VOI) parameters, Global Consistency Error (GCE), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and segmentation time.</span></em></pre>