Research Article Estimation of Gait Parameters for Transfemoral Amputees Using Lower Limb Kinematics and Deterministic Algorithms Zohaib Aftab , 1,2 Gulraiz Ahmed , 1 Asad Ali , 1 and Nazia Gillani 3 1 Faculty of Engineering, University of Central Punjab, Lahore 54000, Pakistan 2 Human-Centered Robotics Lab, National Center of Robotics and Automation, University of Central Punjab, Lahore, Pakistan 3 School of Engineering, University of Edinburgh, Edinburgh, UK Correspondence should be addressed to Zohaib Aftab; dr.zohaib@ucp.edu.pk Received 9 February 2022; Revised 5 August 2022; Accepted 1 September 2022; Published 19 October 2022 Academic Editor: Francesca Cordella Copyright © 2022 Zohaib Aftab et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Accurate estimation of gait parameters depends on the prediction of key gait events of heel strike (HS) and toe-off (TO). Kinematics-based gait event estimation has shown potential in this regard, particularly using leg and foot velocity signals and gyroscopic sensors. However, existing algorithms demonstrate a varying degree of accuracy for different populations. Moreover, the literature lacks evidence for their validity for the amputee population. The purpose of this study is to evaluate this paradigm to predict TO and HS instants and to propose a new algorithm for gait parameter estimation for the amputee population. An open data set containing marker data of 12 subjects with unilateral transfemoral amputation during treadmill walking was used, containing around 3400 gait cycles. Five deterministic algorithms detecting the landmarks (maxima, minima, and zero-crossings [ZC]) in the foot, shank, and thigh angular velocity data indicating HS and TO events were implemented and their results compared against the reference data. Two algorithms based on foot and shank velocity minima performed exceptionally well for the HS prediction, with median accuracy in the range of 6–13 ms. However, both these algorithms produced inferior accuracy for the TO event with consistent early prediction. The peak in the thigh velocity produced the best result for the TO prediction with <25 ms median error. By combining the HS prediction using shank velocity and TO prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters (step, stride times, stance, and double support timings) with a median error of less than 25 ms. In conclusion, combined shank and thigh velocity-based prediction leads to improved gait parameter estimation than traditional algorithms for the amputee population. 1. Introduction Gait analysis is a valuable tool in assessing various patholo- gies as well as in quantifying outcomes of an intervention. A pre-requisite to effective gait analysis is the identification of key gait events of heel strike (HS) and toe-off (TO), which represent the moments the foot is placed and removed from the ground, respectively [1]. Incorrect gait event detection leads to inaccurate gait segmentation into stance and swing phases, leading to erroneous spatio-temporal parameters. Moreover, it is also important for objective evaluation of clinical outcomes for amputees [2]. The gold standard for event prediction is the ground reaction force (GRF) obtained from specialized force plat- forms in research laboratories. However, due to high cost and space constraints, this method is limited to research studies. Moreover, it only detects events from a limited number of steps (usually one or two) depending upon the number of force platforms. In the absence of reliable force data, algorithm-based event detection methods using optoelectronic (marker) data [4], [5] or inertial sensors are exploited. These methods rely on leg or foot kinematics and rule-based algorithms to esti- mate gait events. Many authors have validated this approach for healthy subjects [5]–[12] as well as for subjects with gait disorders [3], [13]–[16] with varying degrees of accuracy. The kinematic methods require an algorithm to identify observable features in the velocity/acceleration data of body Hindawi Applied Bionics and Biomechanics Volume 2022, Article ID 2883026, 11 pages https://doi.org/10.1155/2022/2883026