## 4 Discussion  
This study aimed to present a new Android application able to measure knee instability by assessing AP tibial translation and show that differences can be observed between the injured and non-injured subject knees before surgery. It was also able to detect the difference in an injured knee between pre and post-surgery. This application is based on the open-source OpenCV library used to process images. Three out of the seven subjects were under anesthesia. For those, the results were significantly different between legs for both the Lachman and Pivot-shift tests, which is in agreement with the conclusion of other studies [18]. The method presented here represents an easy, affordable and fast way of assessing the instability in ACL-deficient knees in a quantitative way. It can be used in both clinics and operating rooms, and it does not require bringing bulky devices such as the KT-1000, usually used to assess Lachman tests, into the operating room. Only three adhesive stickers and a smartphone are required to use this method, not like other methods which require sophisticated and expensive devices [19]. The user obtains the AP translation directly, and it is easy to interpret and relate to instability of the knee. This is an advantage compared to measurement with inertial systems [20,21], which usually reports absolute acceleration with respect to the ground. Despite these benefits, the results obtained with this application should be compared with the ones obtained with biplanar fluoroscopy, the current most reliable method, since it directly measures the instability between bones [22,23]. Only in this way might the reliability of the application in assessing quantitatively the knee instability be validated. Another limitation of the present work is that the given translation information is the projection on the camera plane. Therefore, the information provided by the application is not the actual value of the knee AP translation when performing Pivot-Shift tests, since the tibia and femur are not completely parallel to the camera. However, [PERSON] et al. [11] found that significant differences can also be observed for these tests. In our study, the number of analyzed ACL-deficient subjects was not high enough to arrive at strong statistical conclusions. However, the purpose here is to present the application. A clinical study with a larger sample size will be the scope of future works. The main difference with the work of [PERSON] et al. [10] is that during the marker tracking process, the image is converted to the HSV scale, which can facilitate the recognition of the markers [24]. The user should capture the videos with a proper orientation and position of the camera. Although no large differences in AP translation between \(-15^{\circ}\) and \(+15^{\circ}\) (Figure 5a) were observed and the data to evaluate are the range of AP translations during Lachman and Pivot-Shift tests instead of AP absolute translations, one should be careful of this issue when recording the video. The user should keep the plane of the camera as parallel as possible to the plane of the stickers during the Lachman tests and at the beginning of Pivot-Shift tests. The distance between the camera plane and the sagittal plane does not highly affect the AP translation (Figure 5b). Between 20 cm to 80 cm, we observed a difference in absolute AP translation of 2.2 mm. However, it is best for the videos to be captured as close as possible to the sagittal plane but within a distance that the whole movement can be seen by the camera lens. We also suggest keeping the same position of the camera during the sessions of the tests. The rounded green points in the background should be avoided to facilitate image processing and prevent confusion, and an invariant and regular level of illumination is recommended [25]. We also analyzed the influence of the marker position on the range of AP translation.