Tehran University of Medical Sciences Journal of Modern Rehabilitation 2538-385X 20 1 2025 11 11 47 55 Fitri Anestherita Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. & Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. Angela Tulaar Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. Maria Rachmawati Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. Em Yunir Division of Endocrinology, Metabolism, and Diabetes, Department of Internal Medicine, Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. Dante Harbuwono Division of Endocrinology, Metabolism, and Diabetes, Department of Internal Medicine, Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. Retno Asti Werdhani Department of Community Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia. Ahmad Safri Department of Neurology, Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. Muhammad Rachmadi Medical Technology Cluster, Indonesian Medical Education and Research Institute, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia. & Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia. Muhammad Nadhif Medical Technology Cluster, Indonesian Medical Education and Research Institute, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia. & Department of Medical Physiology and Biophysics, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia. Azwien Hawalie Marzuki Medical Technology Cluster, Indonesian Medical Education and Research Institute, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia. Luh Wahyuni Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. Nelfidayani . Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Dr. Cipto Mangunkusumo National General Hospital, Universitas Indonesia, Jakarta, Indonesia. Boya Nugraha Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Hannover Medical School, Hannover, Germany. 2025 06 13 2025 10 21 Introduction: Altered foot kinematics during walking, including reduced tibial inclination (the angle between the tibia and a vertical line during gait), as well as medial longitudinal arch (MLA) flattening and first metatarsophalangeal (MTP1) extension angle, have been linked to various musculoskeletal disorders. Such abnormalities can have significant clinical implications; therefore, it is crucial to accurately identify them. Materials and Methods: We aimed to assess the diagnostic accuracy of KineFeet, a web-based application that employs a depth camera technique to detect foot kinematic changes for human gait analysis. KineFeet software, version 1.3 and Kinovea gait analysis software, version 2023.1.2 were used to diagnose altered foot kinematics in 89 healthy participants in this cross-sectional study. The main kinematic parameters investigated were the ankle inclination angle at terminal stance (AI_TSt), the MLA angle at terminal stance (MLA_TSt), and the MTP1 angle 1 at maximal hallux extension (MTP_HE). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the receiver operating characteristic (ROC) area under the curve (AUC) were computed. Results: KineFeet showed excellent diagnostic performance. AI_TSt had a sensitivity of 88.23% and a specificity of 95.83%, with PPV and NPV values of 83.33% and 97.18%, respectively (AUC=0.97). MLA_TSt and MTP_HE also had high discriminative abilities, with sensitivities of 79.54% and 79.00%, specificities of 95.55% and 91.30%, and attributed AUCs of 0.94 and 0.91, respectively. Conclusion: KineFeet can accurately detect foot kinematic deformities during human gait. Its high diagnostic accuracy makes it a promising screening and evaluation tool. Further studies on human gait pathologies are warranted. https://jmr.tums.ac.ir/index.php/jmr/article/view/1367