https://www.um.edu.mt/library/oar/handle/123456789/145209 Fri, 17 Apr 2026 02:55:20 GMT 2026-04-17T02:55:20Z https://www.um.edu.mt/library/oar/handle/123456789/145215 Title: Foot trajectory estimation methods using inertial sensors and multisegment modelling approaches for kinematic gait analysis Abstract: During the last two decades, considerable progress has been made in the use of foot mounted inertial sensors and other auxiliary sensors such as magnetometers, laser range-finder sensors, ultrasonic sensors and cameras for the monitoring of a person’s locomotion. Although these sensors still give a lower accuracy compared to optical motion capture systems, their compactness and reduced cost have made them more convenient for the purpose of estimation of kinematic gait parameters in both laboratory conditions and during daily activities. Most of the wearable setups used in the literature consider the foot, from the heel to the toes, as one rigid body represented by a single-segment foot model. Very few studies have conducted kinematic gait analysis of multi-segment foot models using multiple inertial measurement units (IMUs). For each of these cases and in contrast to the work in this thesis, methods used in single-segment foot models have simply been adapted to a multi-segment foot model scenario. This thesis presents a novel approach for the estimation of the trajectories of a multi-segment foot model by means of multiple IMUs working as auxiliary sensors for the measurement of the trajectory of each foot segment of interest, i.e. hallux, forefoot, hindfoot, and tibia. Two novel multi-segment methods have been proposed achieving higher accuracy in almost all trajectory directions of all segments as compared to the state-of-the-art methods in literature. The first part of this work presents a literature review of the state-of-the-art methods for kinematic gait analysis employing inertial and auxiliary wearable sensors. In contrast to the existing reviews, a comprehensive approach is presented breaking down each method into three main parts instead of two: (1) zero velocity intervals detection; (2) assumptions and considerations for different sensor types; and (3) pose and trajectory methods. Furthermore, the foot pose and trajectory estimation methods considered for the kinematic gait analysis have been divided into three main categories: (1) de-drifting procedures; (2) Kalman filtering methods; and (3) complementary filtering methods. An analysis of a single-segment foot model has been conducted for the estimation of the foot trajectory and pose employing and evaluating these three state-of-the-art methods using a custom-made wearable device with a single low-cost IMU. The objective of this analysis was to identify the best-performing method for low-cost sensors. The results obtained indicated that a de-drifting procedure may be more appropriate for this type of IMU sensor due to the sensor’s high drift errors. The second part of this work presents two novel approaches involving a multisegment analysis method for the estimation of the trajectories of a foot’s segments using multiple IMUs. Instead of tracking each segment separately, i.e. a separate segment approach, a fusion of all accelerometer measurements using kinematic equations was employed, i.e. a linked-segment approach. To the author’s knowledge, this kind of fusion approach for the improvement of the IMU’s accuracy has never been proposed and tested for such an application. Therefore, a comparative analysis of these two proposed linked-segment approaches with respect to a separate-segment approach took place and validated by means of an optical motion capture system used as a reference. The results obtained show that the novel approaches considerably improve the estimated trajectories of each foot segment. Finally, a wearable system relying on the same multi-segment sensor placement and processing methods was implemented and evaluated by participants in two sessions, being shod and unshod. The vertical displacements of all segments among the two sessions were further evaluated and exhibited a high degree of correlation. This suggests that the proposed linked-segment approach and setup may be useful for use in daily life where the user is typically expected to be shod. Hence, the research work in this thesis presents a review on the state-of-the-art methods using IMUs with or without auxiliary sensors for kinematic gait analysis, and two novel approaches for trajectory estimations of multi-segment foot models able to be used outside a laboratory. Description: M.Phil.(Melit.) Sun, 01 Jan 2023 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/145215 2023-01-01T00:00:00Z