Pengembangan Aplikasi Pose Detection untuk Asesmen Kemajuan Fisioterapi Pasien Pasca Stroke dari Jarak Jauh

Febry Putra Rochim; Anan Nugroho; Sri Sukamta; Ahmad Zein Al Wafi; Muhammad Fathurrahman; Amelia Damayanti; Hildatul Wardah

doi:10.35746/jtim.v5i4.415

Febry Putra Rochim Universitas Negeri Semarang
Anan Nugroho Universitas Negeri Semarang
Sri Sukamta Universitas Negeri Semarang
Ahmad Zein Al Wafi Universitas Negeri Semarang
Muhammad Fathurrahman Universitas Negeri Semarang
Amelia Damayanti Universitas Negeri Semarang
Hildatul Wardah Universitas Negeri Semarang

DOI: https://doi.org/10.35746/jtim.v5i4.415

Keywords: physiotherapy, rehabilitation, telemedicine

Abstract

Assessment has an important role in determining the diagnosis and subsequent treatment plan. In an effort to increase access and effectiveness of rehabilitation, this research aims to develop a mobile application that is able to report the results of post-stroke patient pose assessment remotely. Telemedicine approaches in post-stroke rehabilitation have become increasingly popular, allowing patients to access rehabilitation services remotely. This is especially beneficial for patients who live in remote areas or have limited mobility. Telemedicine also allows for real-time patient monitoring, allowing adjustments to rehabilitation plans as needed. The mobile app is designed to provide easy access to rehabilitation programs that can be tailored to individual patient needs. In addition to making access easier, this application is equipped with a monitoring feature that allows health professionals to follow patient progress in detail. Data collected from patients' daily exercise and activities provides valuable insight into their progress, which can be used in tailoring rehabilitation plans in real-time. The development of this mobile application technology has great potential to improve rehabilitation outcomes for post-stroke patients. Testing by three experts with two experts as healthy patients and stroke patients, as well as one patient who acts as a medical personel to monitor, shows that from the graph, healthy patients tend to be consistent. On the other hand, post-stroke patients tend to be inconsistent. These results indicate that this application is effective for identifying patient movements during the rehabilitation process. Although there are several obstacles, such as delays in predictions on some devices, this application has great potential to improve the quality of life of post-stroke patients. Thus, the development of a pose detection application for remotely assessing the progress of physiotherapy in post-stroke patients has great potential in improving rehabilitation outcomes. The app facilitates patient access to appropriate, personalized and effective care, while providing medical personnel with objective and accurate data for monitoring and adjusting rehabilitation plans. This is a significant step in advancing the care of post-stroke patients.

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