Estimation de cartes d'énergie de hautes fréquences ou d'irrégularité de périodicité de la marche humaine par caméra de profondeur pour la détection de pathologies

Ce travail présente deux nouveaux systèmes simples d'analyse de la marche humaine grâce à une caméra de profondeur (Microsoft Kinect) placée devant un sujet marchant sur un tapis roulant conventionnel, capables de détecter une marche saine et celle déficiente. Le premier système repose sur le fait qu'une marche normale présente typiquement un signal de profondeur lisse au niveau de chaque pixel avec moins de hautes fréquences, ce qui permet d'estimer une carte indiquant l'emplacement et l'amplitude de l'énergie de haute fréquence (HFSE). Le second système analyse les parties du corps qui ont un motif de mouvement irrégulier, en termes de périodicité, lors de la marche. Nous supposons que la marche d'un sujet sain présente partout dans le corps, pendant les cycles de marche, un signal de profondeur avec un motif périodique sans bruit. Nous estimons, à partir de la séquence vidéo de chaque sujet, une carte montrant les zones d'irrégularités de la marche (également appelées énergie de bruit apériodique). La carte avec HFSE ou celle visualisant l'énergie de bruit apériodique peut être utilisée comme un bon indicateur d'une éventuelle pathologie, dans un outil de diagnostic précoce, rapide et fiable, ou permettre de fournir des informations sur la présence et l'étendue de la maladie ou des problèmes (orthopédiques, musculaires ou neurologiques) du patient. Même si les cartes obtenues sont informatives et très discriminantes pour une classification visuelle directe, même pour un non-spécialiste, les systèmes proposés permettent de détecter automatiquement les individus en bonne santé et ceux avec des problèmes locomoteurs.

Estimation de cartes d'énergie de hautes fréquences ou d'irrégularité de périodicité de la marche humaine par caméra de profondeur pour la détection de pathologies

Ce travail présente deux nouveaux systèmes simples d'analyse de la marche humaine grâce à une caméra de profondeur (Microsoft Kinect) placée devant un sujet marchant sur un tapis roulant conventionnel, capables de détecter une marche saine et celle déficiente. Le premier système repose sur le fait qu'une marche normale présente typiquement un signal de profondeur lisse au niveau de chaque pixel avec moins de hautes fréquences, ce qui permet d'estimer une carte indiquant l'emplacement et l'amplitude de l'énergie de haute fréquence (HFSE). Le second système analyse les parties du corps qui ont un motif de mouvement irrégulier, en termes de périodicité, lors de la marche. Nous supposons que la marche d'un sujet sain présente partout dans le corps, pendant les cycles de marche, un signal de profondeur avec un motif périodique sans bruit. Nous estimons, à partir de la séquence vidéo de chaque sujet, une carte montrant les zones d'irrégularités de la marche (également appelées énergie de bruit apériodique). La carte avec HFSE ou celle visualisant l'énergie de bruit apériodique peut être utilisée comme un bon indicateur d'une éventuelle pathologie, dans un outil de diagnostic précoce, rapide et fiable, ou permettre de fournir des informations sur la présence et l'étendue de la maladie ou des problèmes (orthopédiques, musculaires ou neurologiques) du patient. Même si les cartes obtenues sont informatives et très discriminantes pour une classification visuelle directe, même pour un non-spécialiste, les systèmes proposés permettent de détecter automatiquement les individus en bonne santé et ceux avec des problèmes locomoteurs.

Gait Dynamics for Recognition and Classification

This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that our gait dynamics representation is rich enough for the task of recognition and classification. The use of our feature representation is demonstrated in the task of person recognition from video sequences of orthogonal views of people walking. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. In addition, preliminary results are shown on gender classification using our gait dynamics features.

Gait Analysis for Classification

This thesis describes a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple localized image features such as moments extracted from orthogonal view video silhouettes of human walking motion. A suite of time-integration methods, spanning a range of coarseness of time aggregation and modeling of feature distributions, are applied to these image features to create a suite of gait sequence representations. Despite their simplicity, the resulting feature vectors contain enough information to perform well on human identification and gender classification tasks. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times and under varying lighting environments. Each of the integration methods are investigated for their advantages and disadvantages. An improved gait representation is built based on our experiences with the initial set of gait representations. In addition, we show gender classification results using our gait appearance features, the effect of our heuristic feature selection method, and the significance of individual features.

3D ellipsoid fitting for multi-view gait recognition

Gait recognition approaches continue to struggle with challenges including view-invariance, low-resolution data, robustness to unconstrained environments, and fluctuating gait patterns due to subjects carrying goods or wearing different clothes. Although computationally expensive, model based techniques offer promise over appearance based techniques for these challenges as they gather gait features and interpret gait dynamics in skeleton form. In this paper, we propose a fast 3D ellipsoidal-based gait recognition algorithm using a 3D voxel model derived from multi-view silhouette images. This approach directly solves the limitations of view dependency and self-occlusion in existing ellipse fitting model-based approaches. Voxel models are segmented into four components (left and right legs, above and below the knee), and ellipsoids are fitted to each region using eigenvalue decomposition. Features derived from the ellipsoid parameters are modeled using a Fourier representation to retain the temporal dynamic pattern for classification. We demonstrate the proposed approach using the CMU MoBo database and show that an improvement of 15-20% can be achieved over a 2D ellipse fitting baseline.

Histogram of weighted local directions for gait recognition

In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.

Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

Direct measurement of inner prosthesis loading during activities of daily living: Proposal for classification of functional outcome

Background The purpose of this presentation is to outline the relevance of the categorization of the load regime data to assess the functional output and usage of the prosthesis of lower limb amputees. The objectives are • To highlight the need for categorisation of activities of daily living • To present a categorization of load regime applied on residuum, • To present some descriptors of the four types of activity that could be detected, • To provide an example the results for a case. Methods The load applied on the osseointegrated fixation of one transfemoral amputee was recorded using a portable kinetic system for 5 hours. The load applied on the residuum was divided in four types of activities corresponding to inactivity, stationary loading, localized locomotion and directional locomotion as detailed in previously publications. Results The periods of directional locomotion, localized locomotion, and stationary loading occurred 44%, 34%, and 22% of recording time and each accounted for 51%, 38%, and 12% of the duration of the periods of activity, respectively. The absolute maximum force during directional locomotion, localized locomotion, and stationary loading was 19%, 15%, and 8% of the body weight on the anteroposterior axis, 20%, 19%, and 12% on the mediolateral axis, and 121%, 106%, and 99% on the long axis. A total of 2,783 gait cycles were recorded. Discussion Approximately 10% more gait cycles and 50% more of the total impulse than conventional analyses were identified. The proposed categorization and apparatus have the potential to complement conventional instruments, particularly for difficult cases.

Energy efficiency in gait, activity, participation, and health status in children with cerebral palsy

The aim of the study was to establish if a relationship exists between the energy efficiency of gait, and measures of activity limitation, participation restriction, and health status in a representative sample of children with cerebral palsy (CP). Secondary aims were to investigate potential differences between clinical subtypes and gross motor classification, and to explore other relationships between the measures under investigation. A longitudinal study of a representative sample of 184 children with ambulant CP was conducted (112 males, 72 females; 94 had unilateral spastic C P, 84 had bilateral spastic C P, and six had non-spastic forms; age range 4-17y; Gross Motor Function Classification System Level I, n=57; Level II, n=91; Level III, n=22; and Level IV, n=14); energy efficiency (oxygen cost) during gait, activity limitation, participation restriction, and health status were recorded. Energy efficiency during gait was shown to correlate significantly with activity limitations; no relationship between energy efficiency during gait was found with either participation restriction or health status. With the exception of psychosocial health, all other measures showed significant differences by clinical subtype and gross motor classification. The energy efficiency of walking is not reflective of participation restriction or health status. Thus, therapies leading to improved energy efficiency may not necessarily lead to improved participation or general health.

Age-related changes in energy efficiency of gait, activity, and participation in children with cerebral palsy

Aim
The aim of this study was to use a prospective longitudinal study to describe age-related trends in energy efficiency during gait, activity, and participation in ambulatory children with cerebral palsy (CP).

Method
Gross Motor Function Measure (GMFM), Paediatric Evaluation of Disability Inventory (PEDI), and Lifestyle Assessment Questionnaire-Cerebral Palsy (LAQ-CP) scores, and energy efficiency (oxygen cost) during gait were assessed in representative sample of 184 children (112 male; 72 female; mean age 10y 9mo; range 4–16y) with CP. Ninety-four children had unilateral spastic CP, 84 bilateral spastic CP, and six had other forms of CP. Fifty-seven were classified as Gross Motor Function Classification System (GMFCS) level I, 91 as level II, 22 as level III, and 14 as level IV). Assessments were carried out on two occasions (visit 1 and visit 2) separated by an interval of 2 years and 7 months. A total of 157 participants returned for reassessment.

Results
Significant improvements in mean raw scores for GMFM, PEDI, and LAQ-CP were recorded; however, mean raw oxygen cost deteriorated over time. Age-related trends revealed gait to be most inefficient at the age of 12 years, but GMFM scores continued to improve until the age of 13 years, and two PEDI subscales to age 14 years, before deteriorating (p<0.05). Baseline score was consistently the single greatest predictor of visit 2 score. Substantial agreement in GMFCS ratings over time was achieved (?lw=0.74–0.76).

Interpretation
These findings have implications in terms of optimal provision and delivery of services for young people with CP to maximize physical capabilities and maintain functional skills into adulthood.

A classification system for delirium subtyping with the use of a commercial mobility monitor

The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24 h with a commercial accelerometer-based activity monitor. Accelerometry data from patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive and mixed motor subtypes, were used to create classification trees that were Subsequently applied to the remaining cohort to define motoric subtypes. The classification trees used the periods of sitting/lying, standing, stepping and number of postural transitions as measured by the activity monitor as determining factors from which to classify the delirious cohort. The use of a classification system shows how delirium subtypes can be categorised in relation to overall activity and postural changes, which was one of the most discriminating measures examined. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behaviour differ in electronically measured activity levels. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.