Marker-less systems are becoming popular to detect a human skeleton in an image automatically. However, these systems have difficulties in tracking points when part of the body is hidden, or there is an artifact that does not belong to the subject (e.g., a bicycle). We present a low-cost tracking system combined with economic force-measurement sensors that allows the calculation of individual joint moments and powers affordable for anybody. The system integrates OpenPose (deep-learning based C++ library to detect human skeletons in an image) in a system of two webcams, to record videos of a cyclist, and seven resistive sensors to measure forces at the pedals and the saddle. OpenPose identifies the skeleton candidate using a convolution neural network. A corrective algorithm was written to automatically detect the hip, knee, ankle, metatarsal and heel points from webcam-recorded motions, which overcomes the limitations of the marker-less system. Then, with the information of external forces, an inverse dynamics analysis is applied in OpenSim to calculate the joint moments and powers at the hip, knee, and ankle joints. The results show that the obtained moments have similar shapes and trends compared to the literature values. Therefore, this represents a low-cost method that could be used to estimate relevant joint kinematics and dynamics, and consequently follow up or improve cycling training plans.
There are widely used standard clinical tests to estimate the instability of an anterior cruciate ligament (ACL) deficient knee by assessing the translation of the tibia with respect to the femur. However, the assessment of those tests could be quite subjective. The goal of this study is to present a universally affordable open-source Android application that is easy and quick. Moreover, it provides the possibility for a quantitative and objective analysis of that instability. The anterior–posterior knee translation of seven subjects was assessed using the open-source Android application developed. A single Android smartphone and the placement of three green skin adhesives are all that is required to use it. The application was developed using the image-processing features of the open-source OpenCV Library. An open-source Android application was developed to measure anterior–posterior (AP) translation in ACL-deficient subjects. The application identified differences in the AP translation between the ipsilateral and the contralateral legs of seven ACL-deficient subjects during Lachman and Pivot–Shift tests. Three out of seven subjects were under anesthesia. Those three were also the ones with significant differences. The application detected differences in the AP translation between the ipsilateral and contralateral legs of subjects with ACL deficiency. The use of the application represents an easy, low-cost, reliable and quick way to assess knee instability quantitatively.
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