Gait Parameter Estimation from a Single Privacy Preserving Depth Sensor

Yale Hartmann, Jonah Klöckner, Lucas Deichsel, Rinu Elizabeth Paul, Tanja Schultz

Abstract: Recovery from a hard fall is more difficult with age, and early detection of increased fall risk can support early prevention training. The ETAP project focuses on detecting early signifiers like step length in real-time and unobtrusively in older adult life with a single privacy-preserving depth sensor. This paper highlights our efforts to estimate a healthy individual’s skeleton and stride length and outlines how this will be transferred to care facilities. The best ResNet50-based model achieved a mean precision error of 17.49 cm per skeletal joint and stride length error of 5.73 cm on the mean stride length over 727 steps and 7.52cm over 16.67 seconds. Furthermore, 80% accuracy in step classification was achieved. These results show that gait parameter estimation is accurately possible. In the future, we aim to improve these results and build an online system with our care facility partners, transferring these findings to everyday life.