F-VESPA: A Kinematic-based Algorithm for Real-time Heel-strike Detection During Walking

With over 10 million people currently suffering from significant long-term gait disability in the United States only, robot-assisted rehabilitation and wearable devices are increasingly gaining attention as a mean to regain functional mobility. Since these devices work collaborative and synchronously with the human gait, it is necessary to be able to detect gait events, such as heel-strikes, in real-time. Although many algorithms have been proposed for detecting heel-strikes with either wearable (e.g. Inertial Measurement Units (IMUs)) or non-wearable (e.g. force plates) sensors, there is a great need for employing less obtrusive and reliable sensors that rely only on recording the kinematics of the leg motion. This work proposes a novel and efficient kinematic algorithm, called the Foot VErtical & Sagittal Position Algorithm (F-VESPA), which has several advantages over existing methods. First, it accurately estimates heel-strike events using kinematic data without requiring access to future data points, rendering it the first to our knowledge kinematic algorithm capable of real-time implementation during treadmill walking. Moreover, it does not require tuning of the utilized parameters, rendering it robust to different subjects, conditions and equipment. The algorithm is tested in a large set of subjects across various treadmill speeds, and it is shown to outperform online and offline implementations of existing prominent kinematic algorithms. Using a 150 Hz data collection system, the F-VESPA achieved a total true error of 33 ms (median) in detecting heel-strike. The F-VESPA is the first to our knowledge kinematic algorithm that can detect heel-strike events during treadmill walking in real-time, with high accuracy, robustness and fast response, enabling real-time control of a variety of assistive platforms and devices, among others.