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"Penyakit stroke adalah penyebab kematian terbesar kedua di dunia. Pasien stroke harus menjalani perawatan berupa latihan rehabilitasi secara rutin untuk memulihkan fungsi motorik mereka. Sering kali pasien stroke kesulitan mendapatkan perawatan karena keterbatasan ekonomi dan mobilisasi. Selain itu, kondisi pandemi COVID-19 sekarang ini membuat pasien takut untuk pergi ke rumah sakit. Telehealth sebagai pelayanan kesehatan jarak jauh merupakan salah satu solusi untuk kondisi tersebut. Aplikasi telehealth untuk rehabilitasi stroke dapat dikembangkan dikombinasikan dengan teknologi human motion detection. Penelitian ini bertujuan untuk mengetahui model human motion detection yang dapat mendeteksi gerakan secara stabil serta untuk mengetahui model klasifikasi yang dapat mengklasifikasi gerakan stroke dan non stroke secara akurat. Penelitian dilakukan menggunakan data video gerakan pasien stroke dan orang sehat sebagai input model human motion detection. Keypoints hasil ekstraksi dari model human motion detection kemudian ditransformasi menjadi gambar RGB dan digunakan sebagai input model klasifikasi. Penelitian ini membandingkan tiga model human motion detection, yaitu PoseNet, BlazePose, dan MoveNet, serta dua model klasifikasi gambar, yaitu AlexNet dan SqueezeNet.Beberapa eksperimen dilakukan untuk mengklasifikasi gerakan stroke dan non stroke. Terdapat eksperimen dengan pembagian data tanpa 3-Fold Cross Validation, eksperimen dengan pembagian data 3-Fold Cross Validation, eksperimen menggunakan semua keypoints hasil ekstraksi model human motion detection, dan eksperimen menggunakan beberapa keypoints yang relevan. Model human motion detection dan model klasifikasi terbaik dari hasil penelitian ini diharapkan dapat berkontribusi kepada para pihak yang ingin mengembangkan aplikasi telehealth sebagai sarana rehabilitasi stroke. Berdasarkan hasil penelitian ini, didapatkan bahwa MoveNet adalah model human motion detection yang paling stabil dalam memantau pergerakan pasien dan AlexNet adalah model klasifikasi terbaik untuk mengklasifikasikan pasien stroke dan non stroke berdasarkan gerakan upper body dan gerakan lower body.

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Stroke is the second biggest cause of death in the world. Stroke patients must undergo rehabilitation on regular basis to exercise and restore their motor functions. Oftentimes, stroke patients find it difficult to get their treatment because of economic and mobility limitations. In addition, the current state of the COVID-19 pandemic makes patients afraid to go to the hospital. Telehealth as a long-distance health service is one of the solution for this condition. Telehealth applications for stroke rehabilitation can be developed in combination with human motion detection technology. This study aims to determine the human motion detection model that can detect movement steadily and determine the classification model that can classify stroke and non-stroke motions accurately. The study was conducted using video data of stroke patients and healthy people as input for the human motion detection model. Keypoints extracted from the human motion detection model are then transformed into RGB images and used as input for the classification model. This study compares three models of human motion detection, namely PoseNet, BlazePose, and MoveNet and two image classification models, namely AlexNet and SqueezeNet.

Several experiments were conducted to classify stroke and non-stroke motions. There are experiments without data splitting 3-Fold Cross Validation, experiments with data splitting 3-Fold Cross Validation, experiments using all keypoints extracted from the human motion detection model, and experiments using several relevant keypoints. The most steady human motion detection model and the best classification model from the results of this study are expected to contribute to those who want to develop telehealth applications as a means of stroke rehabilitation. Based on the results of this study, it was found that MoveNet is the most steady human motion detection model for monitoring the patients motions and AlexNet is the best classification model for classifying stroke and non stroke patients based on upper body and lower body movements."

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Penyakit stroke adalah penyebab kematian terbesar kedua di dunia. Pasien stroke harus menjalani perawatan berupa latihan rehabilitasi secara rutin untuk memulihkan fungsi motorik mereka. Sering kali pasien stroke kesulitan mendapatkan perawatan karena keterbatasan ekonomi dan mobilisasi. Selain itu, kondisi pandemi COVID-19 sekarang ini membuat pasien takut untuk pergi ke rumah sakit. Telehealth sebagai pelayanan kesehatan jarak jauh merupakan salah satu solusi untuk kondisi tersebut. Aplikasi telehealth untuk rehabilitasi stroke dapat dikembangkan dikombinasikan dengan teknologi human motion detection. Penelitian ini bertujuan untuk mengetahui model human motion detection yang dapat mendeteksi gerakan secara stabil serta untuk mengetahui model klasifikasi yang dapat mengklasifikasi gerakan stroke dan non stroke secara akurat. Penelitian dilakukan menggunakan data video gerakan pasien stroke dan orang sehat sebagai input model human motion detection. Keypoints hasil ekstraksi dari model human motion detection kemudian ditransformasi menjadi gambar RGB dan digunakan sebagai input model klasifikasi. Penelitian ini membandingkan tiga model human motion detection, yaitu PoseNet, BlazePose, dan MoveNet, serta dua model klasifikasi gambar, yaitu AlexNet dan SqueezeNet. Beberapa eksperimen dilakukan untuk mengklasifikasi gerakan stroke dan non stroke. Terdapat eksperimen dengan pembagian data tanpa 3-Fold Cross Validation, eksperimen dengan pembagian data 3-Fold Cross Validation, eksperimen menggunakan semua keypoints hasil ekstraksi model human motion detection, dan eksperimen menggunakan beberapa keypoints yang relevan. Model human motion detection dan model klasifikasi terbaik dari hasil penelitian ini diharapkan dapat berkontribusi kepada para pihak yang ingin mengembangkan aplikasi telehealth sebagai sarana rehabilitasi stroke. Berdasarkan hasil penelitian ini, didapatkan bahwa MoveNet adalah model human motion detection yang paling stabil dalam memantau pergerakan pasien dan AlexNet adalah model klasifikasi terbaik untuk mengklasifikasikan pasien stroke dan non stroke berdasarkan gerakan upper body dan gerakan lower body.

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Stroke is the second biggest cause of death in the world. Stroke patients must undergo rehabilitation on regular basis to exercise and restore their motor functions. Oftentimes, stroke patients find it difficult to get their treatment because of economic and mobility limitations. In addition, the current state of the COVID-19 pandemic makes patients afraid to go to the hospital. Telehealth as a long-distance health service is one of the solution for this condition. Telehealth applications for stroke rehabilitation can be developed in combination with human motion detection technology. This study aims to determine the human motion detection model that can detect movement steadily and determine the classification model that can classify stroke and non-stroke motions accurately. The study was conducted using video data of stroke patients and healthy people as input for the human motion detection model. Keypoints extracted from the human motion detection model are then transformed into RGB images and used as input for the classification model. This study compares three models of human motion detection, namely PoseNet, BlazePose, and MoveNet and two image classification models, namely AlexNet and SqueezeNet. Several experiments were conducted to classify stroke and non-stroke motions. There are experiments without data splitting 3-Fold Cross Validation, experiments with data splitting 3-Fold Cross Validation, experiments using all keypoints extracted from the human motion detection model, and experiments using several relevant keypoints. The most steady human motion detection model and the best classification model from the results of this study are expected to contribute to those who want to develop telehealth applications as a means of stroke rehabilitation. Based on the results of this study, it was found that MoveNet is the most steady human motion detection model for monitoring the patients motions and AlexNet is the best classification model for classifying stroke and non stroke patients based on upper body and lower body movements.

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"Over the past century, technology has made it possible to measure human motion in many ways and with new levels of precision, becoming a tool in many applications and professional branches including medicine, sports science and forensics. Some branches have developed in response to the technology, such as the emergence of motion-capture animation in the film industry. These different applications use the same or similar types of equipment with the general intention of making the systems as intuitive as possible for the participants. However, the methodologies necessary to produce reliable results in these areas differ enormously. The overlap of information between fields might lead to situations where small but important adaptations are required in order to produce the required result; experts with knowledge of one field might not be aware of such developments. The Handbook of Human Motion brings together contributions from many different disciplines, covering the established and emerging technologies employed, the challenges posed in each area of application and the novel solutions reached by researchers."