Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics

Abstract

Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults’ foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations.

Validity Verification of Human Pose-Tracking Algorithms for Gait Analysis Capability

“…State-of-the-art clinical systems include Vicon (Vicon Nexus2; Vicon Motion Systems Ltd., Oxford, UK) and Optitrak [ 7 , 8 ]; Vicon is the gold standard, meaning that it produces the most accurate and consistent results, and its internal statistical processes are known to be valid. “Internal validity” is essentially a guarantee that the processes used to make quantitative assessments are sound, repeatable, and confident at a very high level (e.g., 95–98%, depending on the measure).…”

“…It also intakes video frames but only one at a time. Additionally, it was not developed to deal with 3D binocular image pairing [ 7 , 10 ]. OpenPose does not require markers or accelerometers.…”

Special Issue “Feature Papers in Biosensors Section 2022”

“…Infra-red thermal imaging with heart rate variability was used to obtain features related to the psychophysiology of drivers, and data were analyzed by machine learning analyzers [ 13 , 14 ]. Wearable inertial measurement units associated with machine learning algorithm were used to predict Minimum Foot Clearance (MFC) timing [ 15 ]. These non-invasive techniques can also be used for monitoring the quality of agrifood products.…”