Using deep learning to predict minimum foot–ground clearance event from toe-off kinematics

[thumbnail of sensors-22-06960-v2.pdf]
sensors-22-06960-v2.pdf - Published Version (2MB) | Preview
Available under license: Creative Commons Attribution

Asogwa, Clement ORCID: 0000-0001-8454-9356, Nagano, Hanatsu ORCID: 0000-0003-4119-4289, Wang, Kai and Begg, Rezaul ORCID: 0000-0002-3195-8591 (2022) Using deep learning to predict minimum foot–ground clearance event from toe-off kinematics. Sensors, 22 (18). ISSN 1424-8220


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.

Dimensions Badge

Altmetric Badge

Item type Article
DOI 10.3390/s22186960
Official URL
Subjects Current > FOR (2020) Classification > 4207 Sports science and exercise
Current > Division/Research > Institute for Health and Sport
Keywords deep learning, foot ground clearance, kinetics, locomotor function, health, fall risk, fall related injury
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login