Defining a score based on gait analysis for the longitudinal follow-up of MS patients

peer reviewedBACKGROUND. The project GAIMS [ECTRIMS 2013 P800] aims at developing a gait measuring system particularily suited for the clinical routine, and providing a reference database with the gait characteristics of many MS patients (MSP) and healthy people (HP). As the gait impairments are related to the disease progression, defining an objective and quantitative score based on the gait characteristics would be useful for the longitudinal follow-up. Based on the dataset of GAIMS and machine learning techniques (MLT), a score, well correlated with the EDSS, can be defined [Azrour et al. ESANN 2014]. OBJECTIVE. Burggraaff et al. [ECTRIMS 2014 P033] showed that paired comparisons can help human raters to better judge the state of the patients. In the same spirit, we aim at predicting the difference of EDSS between two persons or between two visits of a same person, based on clinical gait measures. We show that the pairwise comparison strategy leads to a score (Gait-Score) well correlated with the EDSS and sensitive to small modifications of the gait. METHODS. The gait of 162 HP and 72 MSP (44 with EDSS>3) has been recorded and analyzed with GAIMS. The Gait-Score is defined using the MLT of [Geurts et al. 2006]. We can compute the Gait-Score of a person by comparing him to others with known EDSS, and compute the difference of Gait-Score of a same person at two different moments. We measure the merits of the Gait-Score by the correlation between the predicted Gait-Score and the EDSS, as well as the ability to detect subtle gait deteriorations among people with ataxia induced by a low dose of alcohol (data of [Piérard et al. ESANN 2014]). RESULTS. The Gait-Score is well correlated with the EDSS (Pearson’s correlation=0.8743). Moreover, it manages to detect a gait deterioration after a small alcohol intake for 19 persons out of 24 (79% correct) which is much better than what was obtained by visual inspection of neurologists (62% according to [Piérard et al. ESANN 2014]). CONCLUSIONS. Based on the accurate gait measures provided by GAIMS, we are able to derive a Gait-Score, automatically, that is well correlated with the EDSS. Moreover, this score is able to detect subtle deteriorations of the gait caused by a low dose of alcohol. These results reinforce our conviction that the use of an automatic method based on gait analysis is very promising for the longitudinal follow-up of MS patients and the assessment of the impact of new drugs and rehabilitation programs.GAIM

Diagnosing multiple sclerosis with a gait measuring system, an analysis of the motor fatigue, and machine learning

GAIM

Design of a reliable processing pipeline for the non-intrusive measurement of feet trajectories with lasers

peer reviewedReliable measurements of feet trajectories are needed in some applications, such as biomedical applications. This paper describes the data processing pipeline used in GAIMS, which is a non-intrusive system that measures feet trajectories based on multiple range laser scanners. Our processing pipeline relies on a new tracking paradigm, and it is based on two innovative algorithms: the first algorithm localizes the feet directly from the observed point cloud without any clustering, and the other algorithm identifies the feet. After reviewing the various types of noise affecting the point cloud, this paper explains the limitations of the classical processing approach and gives an overview of our new pipeline. The effectiveness of the proposed approach is established by discussing the results that have been obtained in several studies based on GAIMS.GAIM

Improving pose estimation by building dedicated datasets and using orientation

Markerless pose estimation systems are useful for various applications including human- computer interaction, activity recognition, security, gait analysis, and computer-assisted medical interventions. They have attracted much interest since the release of low-cost depth cameras such as Microsoft’s Kinect camera. Shotton et al. and Girshick et al. pioneered tractable methods that infer a full-body pose reconstruction in real-time. Despite this technological breakthrough, the accuracy of human pose estimation from single depth images remains insufficient for some applications. Our work aims at building a simulation environment to create images databases suited for any camera position and improving the mainstream machine learning-based pose estimation algorithms

Using GAit Measuring System (GAIMS) to discriminate patients with multiple sclerosis from healthy person

Among voluntary movements, gait is the most affected by multiple sclerosis. Gait impairment is also a good indicator of the disease progression. However, measurement of gait character- istics made by neurologists is usually limited to the use of a stopwatch. The GAit Measuring System (GAIMS), provides a wider range of measurements that allow the definition of several relevant gait descriptors. The work presented here shows the effectiveness of these gait descriptors and machine learning techniques to discriminate between healthy persons and patients with multiple sclerosis

Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis

peer reviewedGait impairment is considered as an important feature of disability in multiple sclerosis but its evaluation in the clinical routine remains limited. In this paper, we assess, by means of supervised learning, the condition of patients with multiple sclerosis based on their gait descriptors obtained with a gait analysis system. As the morphological characteristics of individuals influence their gait while being in first approximation independent of the disease level, an original strategy of data normalization with respect to these characteristics is described and applied beforehand in order to obtain more reliable predictions. In addition, we explain how we address the problem of missing data which is a common issue in the field of clinical evaluation. Results show that, based on machine learning combined to the proposed data handling techniques, we can predict a score highly correlated with the condition of patients

Detection and characterization of gait modifications, for the longitudinal follow-up of patients with neurological diseases, based on the gait analyzing system GAIMS

GAIM

Measuring feet trajectories: challenges and applications

Measuring reliable feet trajectories is needed in many applications. This paper provides the principles used in GAIMS, which is a non-intrusive system that measures feet trajectories based on multiple range laser scanners. We present the technical challenges that we had to address, as well as an overview of the implemented processing pipeline of GAIMS.GAIM

GAIMS: A Reliable Non-Intrusive Gait Measuring System

peer reviewedGait observation and analysis can provide invaluable information about an individual [1]. Studies that have interpreted gait using traditional imaging devices have demonstrated that it is difficult to make reliable measurements with colour cameras. GAIMS, our new system resulting from a multidisciplinary project born from collaboration between engineers and neurologists, aims at developing non-intrusive and reliable tools to provide quantitative measures of gait and interpretations of the acquired data. Following a current trend in imaging, it takes advantage of imaging sensors that measure distance instead of colour. While its principles are general, GAIMS is currently used for the diagnosis of multiple sclerosis (MS) and the continued evaluation of disease progression [2]. It is the first available system to fully satisfy the clinical routine and its associated constraints.GAIM