Toe-off Detection Research Articles

Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running.

The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and takes the distal tibia anteroposterior velocity, ankle dorsiflexion/plantar flexion angle and the anteroposterior and vertical velocities of the foot centre of mass as input features to predict the contact phase within a given gait cycle. The chosen model architecture underwent 5-fold cross-validation and the final model was tested in a subset of participants from each dataset (30%). Non-parametric Bland-Altman analyses (bias and [95% limits of agreement]) and root mean squared error (RMSE) were used to compare FootNet against the force data step event detection method. The association between detection errors and running speed, foot-strike angle and incline were also investigated. FootNet outperformed previously published algorithms (foot-strike bias = 0 [-10, 7] ms, RMSE = 5 ms; toe-off bias = 0 [-10, 10] ms, RMSE = 6 ms; and contact time bias = 0 [-15, 15] ms, RMSE = 8 ms) and proved robust to different running speeds, foot-strike angles and inclines. We have made FootNet's source code publicly available for step event detection in treadmill running when force data are not available.

Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System.

The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method. Then, detection of Toe-Off and Mid-Stance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman filter. As a result, the Toe-Off detection algorithm reached an F-score between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for non-stopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standing.

Gait Event Detection of a Lower Extremity Exoskeleton Robot by an Intelligent IMU

To control an exoskeleton robot, gait event (phase) of the robot needs to be identified. This paper presents a novel gait event detection method for a lower extremity exoskeleton robot based on an intelligent inertial measurement unit (iIMU). The iIMU is designed as a gait monitor to independently accomplish data sampling, data processing, and wireless transmission. It also has good portability that can be easily attached on the surface of a shoe. Moreover, an online detection algorithm is proposed to detect the gait events by local search windows and fixed thresholds, resulting in a minimal time delay and small computational burden. The accuracy and detection rate of the iIMU are experimentally verified by 10 healthy subjects walking on a force plate and treadmill. The mean time errors of heel-strike and toe-off detection are −10 and 19 ms when compared to the force plate. Gait events of a total 478 steps, collected from a treadmill with various walking speeds, are all detected. When applied to a lower extremity exoskeleton robot, the iIMU successfully detects the gait events of the human–robot synchronous walk.