Abstract
Foot pain and related problems are quite common in the community. It is reported that 24% of individuals older than 45 experienced foot pain. Also, it is stated that at least two thirds of individuals experiences moderate physical disability due to foot problems. In the absence of evaluation of risk factors such as limited ankle dorsiflexion in the early period of the diseases (Plantar fasciitis, Achilles Tendinopathy e.g.) and the lack of mobile systems with portable remote access, foot pain becomes refractory/chronic foot pain, secondary pathologies and ends with workload of 1., 2. and 3rd level healthcare services. In the literature, manuel and dijital methods have been used to analyze the ankle range of motion (ROM). These studies are generally based on placing protractors on the image and / or angle detection from inclination measurement by using the gyroscope sensor of the mobile device. Some of these applications are effective and they are designed to be suitable for measuring in a clinical setting by a physician or physiotherapist. To the best of our knowledge, there is no system developed to measure real-time ankle ROM remotely with collaboration of the patients. In this research, we proposed to develop an ankle ROM analyze system with smart phone application that can be used comfortably by subjects.
We present a case of a 22-year-old male with a symptomatic pes planus. The mobile application, which was used for data collection, was designed and implemented for Android devices. Initially, before the mobile application home page is opened, a consent page was submitted to the acceptance of individual within the scope of Law (KVKK) data privacy. Then, the participant was asked to state his sociodemographic characteristics [age, gender, height, weight] and dominant side. No history of foot-ankle injury, trauma, and surgery was recorded. Activity pain of the foot was 6 according to visual anolog scale (VAS) in the mobile application. His ankle dorsiflexion was 15 ° by manuel goniometer. Besides, server was responsible for storing the collected data and ROM measurement. ROM was calculated by processing the foot video which was sent through the mobile application. During the processing phase, a segmentation model was used which was trained with image process and deep learning methods. With the developed system, we obtained the manual goniometric measurement result with 2 degrees deviation. As the application is calibrated, it is expected to approach the actual measurement of ROM.
We can conclude that mobile app-goniometer result in dorsiflexion measurement is a novel promising evaluation method for ankle ROM. it will be easy and practical to detect and monitor risk factor of the diseases, decrease medical costs, provide health services in rural areas, and contribution to life quality and to reduce the workload on physicians and physiotherapist.
