Estimation and validation of temporal gait features using a markerless 2D video system

Background and Objective: Estimation of temporal gait features, such as stance time, swing time and gait cycle time, can be used for clinical evaluations of various patient groups having gait pathologies, such as Parkinson’s diseases, neuropathy, hemiplegia and diplegia. Most clinical laboratories employ an optoelectronic motion capture system to acquire such features. However, the operation of these systems requires specially trained operators, a controlled environment and attaching reflective markers to the patient’s body. To allow the estimation of the same features in a daily life setting, this paper presents a novel vision based system whose operation does not require the presence of skilled technicians or markers and uses a single 2D camera. Method: The proposed system takes as input a 2D video, computes the silhouettes of the walking person, and then estimates key biomedical gait indicators, such as the initial foot contact with the ground and the toe off instants, from which several other temporal gait features can be derived. Results: The proposed system is tested on two datasets: (i) a public gait dataset made available by CASIA, which contains 20 users, with 4 sequences per user; and (ii) a dataset acquired simultaneously by a marker-based optoelectronic motion capture system and a simple 2D video camera, containing 10 users, with 5 sequences per user. For the CASIA gait dataset A the relevant temporal biomedical gait indicators were manually annotated, and the proposed automated video analysis system achieved an accuracy of 99% on their identification. It was able to obtain accurate estimations even on segmented silhouettes where, the state-of-the-art markerless 2D video based systems fail. For the second database, the temporal features obtained by the proposed system achieved an average intra-class correlation coefficient of 0.86, when compared to the "gold standard" optoelectronic motion capture system. Conclusions: The proposed markerless 2D video based system can be used to evaluate patients’ gait without requiring the usage of complex laboratory settings and without the need for physical attachment of sensors/markers to the patients. The good accuracy of the results obtained suggests that the proposed system can be used as an alternative to the optoelectronic motion capture system in non-laboratory environments, which can be enable more regular clinical evaluations.info:eu-repo/semantics/acceptedVersio

The knee prosthesis constraint dilemma: Biomechanical comparison between varus-valgus constrained implants and rotating hinge prosthesis. A cadaver study

The real degree of constriction of rotating hinge knee (RHK) and condylar constrained prostheses (CCK) is a matter of discussion in revision knee arthroplasty. The objectives of this study are to compare the tibial rotation of both implants and validate the use of inertial sensors with optical tracking system as movement measurement tools. A total of 16 cadaver knees were used. Eight knees were replaced using a RHK (Endomodel LINK), and the remaining eight received a CCK prosthesis (LCCK, Zimmer). Tibial rotation range of motion was measured in full extension and at 30°, 60°, and 90° of flexion, with four continuous waveforms for each measurement. Measurements were made using two inertial sensors with specific software and compared with measurements obtained using the gold standard technique - the motion capture camera. The comparison of the accuracy of both measurement methods showed no statistically significant differences between inertial sensors and motion capture cameras, with p > .1; the mean error for tibial rotation was 0.21°. Tibial rotation in the RHK was significantly greater than in the CCK (5.25° vs. 2.28°, respectively), p < .05. We have shown that RHK permit greater tibial rotation, being closer to physiological values than CCKs. Inertial sensors have been validated as an effective and accurate method of measuring knee movement. The clinical significance: RHK appears to represent a lower constriction degree than CCK systems.This study wassupported by Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III and European Regional Development Fund "Una manera de hacer Europa" (grant number PI18/01625

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation

Towards the use of 2-dimensional video-based markerless motion capture to objectively evaluate kinematics during functional capacity evaluation

Backgrounds Workability, the capacity to perform work, is a concept highly regarded in a return-to-work context and typically measured using functional capacity evaluation (FCE). The aim of FCE is to determine the capacity of an individual such that they can be appropriately matched with job demands. Further, the usage of FCE is popularly used in attempt to proactively reduce the risk of injury in the workplace. Capacity is measured using tests, including maximum safe load tests and manual materials handling tolerance tests. However, the ability to predict workability using FCE has been questioned. Functional capacity evaluation administrators determine workability, in part, by using a priori parameters such as “the subject maintains balance," that are determined via subjective observations. Use of subjective observation may explain why the ability to reliably determine effort and capacity by using FCE remain in debate (Trippolini et al., 2014). However, a markerless motion-capture based solution may permit direct measurement of important movement features, and in turn, may improve the predictive utility and reliability of FCE outcomes. But, first, it remains important to evaluate if a 2-dimensional (2D) video-based markerless motion capture solution can generate objective outcomes that match with those generated using existing 3-dimensional (3D) motion capture. Objective. To determine the agreement of kinematic outputs calculated from motion data collected via a 2D video-based pose-estimation (markerless motion capture) software and a laboratory-based 3D motion capture for floor-to-waist height lifting task. The kinematic outputs calculated include peak knee flexion angle, peak trunk flexion angle, peak shoulder flexion and abduction angles, functional stability limits in the anterior-posterior and medial-lateral direction, the distance of the load relative to the center of gravity and mean absolute relative phase angles. Methods. Three floor-to-waist height lifts were used for analysis for each participant (N = 20). Participants’ lifts were captured using 3D motion capture (Vicon, Oxford, UK) and simultaneously recorded using 2D video (camcorder) in the sagittal plane. The participants lifts were each completed using a light, medium and heavy load dependent on the participants’ individual subjective capacities. Post-collection, motion data from 3D motion capture and video-based markerless motion capture were used separately to calculate the specific kinematic metrics of interest. The outcome measures calculated were peak knee flexion angle, peak trunk flexion angle, peak shoulder flexion and abduction angles, center of gravity relative to the load handled, base of support relative to the center of gravity in the anterior-posterior and medial-lateral directions, as well as mean absolute relative phase angles of the hip-knee for the flexion and extension phases of the lift separately. Bland-Altman analysis and plots were used to calculate agreement as a form of concurrent validity between the two methods. Results. For all outcome measures, Bland-Altman analysis did not suggest agreement between outcomes calculated using the 2D pose-estimation method and 3D motion capture method. Conclusions. Due to the lack of agreement between the two methods, it is advised that video-based markerless motion capture and 2D pose-estimation be further enhanced prior to use in calculating objective measures of FCE performance