Gait analysis | EPFL Graph Search

Gait analysis is the systematic study of animal locomotion, more specifically the study of human motion, using the eye and the brain of observers, augmented by instrumentation for measuring body movements, body mechanics, and the activity of the muscles. Gait analysis is used to assess and treat individuals with conditions affecting their ability to walk. It is also commonly used in sports biomechanics to help athletes run more efficiently and to identify posture-related or movement-related problems in people with injuries. The study encompasses quantification (introduction and analysis of measurable parameters of gaits), as well as interpretation, i.e. drawing various conclusions about the animal (health, age, size, weight, speed etc.) from its gait pattern. History The pioneers of scientific gait analysis were Aristotle in De Motu Animalium (On the Gait of Animals) and much later in 1680, Giovanni Alfonso Borelli also called De Motu Animalium (I et II). In the 1890s, the

Patient MoCap: Human Pose Estimation under Blanket Occlusion for Hospital Monitoring Applications

Huseyin Coskun, Alexandru Eugen Ichim

Motion analysis is typically used for a range of diagnostic procedures in the hospital. While automatic pose estimation from RGB-D input has entered the hospital in the domain of rehabilitation medicine and gait analysis, no such method is available for bed-ridden patients. However, patient pose estimation in the bed is required in several fields such as sleep laboratories, epilepsy monitoring and intensive care units. In this work, we propose a learning-based method that allows to automatically infer 3D patient pose from depth images. To this end we rely on a combination of convolutional neural network and recurrent neural network, which we train on a large database that covers a range of motions in the hospital bed. We compare to a state of the art pose estimation method which is trained on the same data and show the superior result of our method. Furthermore, we show that our method can estimate the joint positions under a simulated occluding blanket with an average joint error of 7.56 cm.

2016

Gait analysis in children with cerebral palsy: bridging the gap between the laboratory and real life

Lena Carcreff

Like 17 million people worldwide, an individual with cerebral palsy (CP) does not have the opportunity to walk harmoniously in society due to long-life impairments in movement and posture. The natural course of CP can be modulated by treatments and therapies that are nowadays mostly decided on the basis of assessments performed in clinical settings. The Clinical Gait Analysis (CGA) consists of a set of instrumented assessments aiming to obtain precise and quantitative information about a patientâs gait deviations, in order to better identify his motor disorders and their possible causes. However, it is not clear whether gait assessments in clinics (âcapacityâ) are representative of daily-life behaviors (âperformanceâ). In this context, the present thesis aimed at exploring the gap between gait assessed in the laboratory and in real life for children with CP, as compared to children with typical development (TD). Two main objectives were settled: (i) to propose an objective and validated tool for gait assessments in a daily-life context with the highest possible accuracy as compared to clinical standard references; and (ii) to compare gait characteristics between both environments, the laboratory and the real life. Considering the immense progress in the design of wearable sensors, notably inertial measurement units (IMU), great enthusiasm recently arose for their use in ambulatory monitoring. IMUs, including 3D accelerometers and gyroscopes, were thus exploited in this work as a solution to measure gait features in the childrenâs daily life. A comparative study was first carried out to determine the most appropriate wearable system to be used for children with CP and for long-term measurements. Sensors located on the shanks and thighs and associated algorithms revealed to be the best solution. Next, a proof-of-concept study was completed and emphasized the need for personalized data processing for children with CP but also with TD. The second part (ii), dealing with the comparisons of walking capacity versus performance, adopted a progressive approach. The first step was the comparison between standardized walking, i.e. during a CGA protocol, and walking under challenging situations, such as dual tasks, i.e. thinking and talking while walking, in the laboratory. We found that dual tasks were responsible for lower motor gait capacities. The next step was the effective comparison of gait characteristics measured in the laboratory with the same gait characteristics measured in real-life settings, using the previously determined wearable system. First, walking speed, a global indicator of gait, and second, multiple gait parameters were compared between laboratory and daily life. Through two studies, we evidenced that children with CP have highly heterogenous behaviors but tend to perform better in clinical settings. Besides, we have highlighted that capacity is associated with performance in children with CP when they are evaluated with the same metrics. Through this doctoral work, the great challenges of using IMUs for gait analysis of children with CP have been highlighted. The proposed solutions reached a compromise between accuracy, number of outcomes, and acceptance. Furthermore, the presented clinical results proved with objective and quantitative evidence the existence of a gap between gait assessed in the laboratory and gait in real life, which could help clinicians to devise therapeutic plans better tailored to each childâs needs.

EPFL2020

Assessment of Foot Signature Using Wearable Sensors for Clinical Gait Analysis and Real-Time Activity Recognition

Benoît Mariani

Locomotion is one of the most important abilities of humans. Actually, gait locomotion provides mobility, and symbolizes freedom and independence. However, gait can be affected by several pathologies, due to aging, neurodegenerative disease, or trauma. The evaluation and treatment of mobility diseases thus requires clinical gait assessment, which is commonly done by using either qualitative analysis based on subjective observations and questionnaires, or expensive analysis established in complex motion laboratories settings. This thesis presents a new wearable system and algorithmic methods for gait assessment in natural conditions, addressing the limitations of existing methods. The proposed system provides quantitative assessment of gait performance through simple and precise outcome measures. The system includes wireless inertial sensors worn on the foot, that record data unobtrusively over long periods of time without interfering with subject's walking. Signal processing algorithms are presented for the automatic calibration and online virtual alignment of sensor signals, the detection of temporal parameters and gait phases, and the estimation of 3D foot kinematics during gait based on fusion methods and biomechanical assumptions. The resulting 3D foot trajectory during one gait cycle is defined as Foot Signature, by analogy with hand-written signature. Spatio-temporal parameters of interest in clinical assessment are derived from foot signature, including commonly parameters, such as stride velocity and gait cycle time, as well as original parameters describing inner-stance phases of gait, foot clearance, and turning. Algorithms based on expert and machine learning methods have been also adapted and implemented in real-time to provide input features to recognize locomotion activities including level walking, stairs, and ramp locomotion. Technical validation of the presented methods against gold standard systems was carried out using experimental protocols on subjects with normal and abnormal gait. Temporal aspects and quantitative estimation of foot-flat were evaluated against pressure insoles in subjects with ankle treatments during long-term gait. Furthermore, spatial parameters and foot clearance were compared in young and elderly persons to data obtained from an optical motion capture system during forward gait trials at various speeds. Finally, turning was evaluated in children with cerebral palsy and people with Parkinson's disease against optical motion capture data captured during timed up and go and figure-of-8 tests. Overall, the results demonstrated that the presently proposed system and methods were precise and accurate, and showed agreement with reference systems as well as with clinical evaluations of subjects' mobility disease using classical scores. Currently, no other methods based on wearable sensors have been validated with such precision to measure foot signature and subsequent parameters during unconstrained walking. Finally, we have used the proposed system in a large-scale clinical application involving more than 1800 subjects from age 7 to 77. This analysis provides reference data of common and original gait parameters, as well as their relationship with walking speed, and allows comparisons between different groups of subjects with normal and abnormal gait. Since the presented methods can be used with any foot-worn inertial sensors, or even combined with other systems, we believe our work to open the door to objective and quantitative routine gait evaluations in clinical settings for supporting diagnosis. Furthermore, the present studies have high potential for further research related to rehabilitation based on real-time devices, the investigation of new parameters' significance and their association with various mobility diseases, as well as for the evaluation of clinical interventions.