A New Hand-Movement-Based Authentication Method Using Feature Importance Selection with the Hotelling’s Statistic

The growing amount of collected and processed data means that there is a need to control access to these resources. Very often, this type of control is carried out on the basis of biometric analysis. The article proposes a new user authentication method based on a spatial analysis of the movement of the finger’s position. This movement creates a sequence of data that is registered by a motion recording device. The presented approach combines spatial analysis of the position of all fingers at the time. The proposed method is able to use the specific, often different movements of fingers of each user. The experimental results confirm the effectiveness of the method in biometric applications. In this paper, we also introduce an effective method of feature selection, based on the Hotelling T2 statistic. This approach allows selecting the best distinctive features of each object from a set of all objects in the database. It is possible thanks to the appropriate preparation of the input data

Development of a Gait Score for the Assessment of End-stage Ankle Osteoarthrosis and Outcome of Related Surgery

Aujourd'hui, en pratique clinique, l'évaluation fonctionnelle des patients souffrant de pathologies du pied et de la cheville se limite principalement à une approche subjective qui se base sur des questionnaires cliniques. Ceci est dû au fait que la plupart des analyses objectives de référence ne peuvent être conduites que dans des laboratoires de recherche en raison de la taille et du coût des équipements. Néanmoins, grâce au développement récent d'un système ambulatoire d'analyse de la marche qui se compose de capteurs inertiels 3D et de capteurs de pression embarqués, l'incorporation de l'analyse de la marche dans la pratique clinique est maintenant possible. Le but de cette thèse est de montrer la capacité de ce système portable à étudier une pathologie spécifique du pied et de la cheville et présente un score d'évaluation quantitatif basé sur l'analyse de la marche. L'arthrose de cheville terminale (AOA) a été sélectionnée car c'est une pathologie progressive et invalidante dont les traitements dépendent de la sévérité. Les chirurgies les plus fréquemment appliquées sont l'arthrodèse de cheville (AA), l'arthroplastie totale de cheville (TAR) et, dans des cas trop sévères ou des situations d'échec, l'arthrodèse calcanéo-talo-tibiale (TTCA). En général l'évaluation subjective montre que les résultats sont, somme toute, assez semblables quelle que soit la technique chirurgicale appliquée. Néanmoins, l'analyse objective de la marche montre que des différences significatives peuvent être observées et ce, pas seulement pour le pied malade ou opéré, mais aussi pour le pied opposé. Le travail de thèse se base sur l'étude de 89 participants dont des sujets contrôles, des patients souffrant d'arthrose de cheville terminale, des patients après arthrodèse de cheville, prothèse de cheville et arthrodèse TTCA. Les participants ont été évalués fonctionnellement en se basant sur des scores cliniques (AOFAS, FAAM et EQ-5D) ainsi que par analyse ambulatoire de la marche. Les analyses de marche ont été réalisées dans un espace ouvert permettant aux participants de marcher naturellement. Pour chaque participant on a examiné le côté affecté et le côté sain. Dans le but de rendre le système d'analyse de la marche assez simple pour être utilisable en pratique cette thèse s'est basée sur l'analyse des composantes principales pour identifier les paramètres pertinents pour la clinique tout en maintenant l'information importante. Un modèle de marche prédictif a pu ensuite être développé qui puisse être utilisé comme un score pour analyser les patients et identifier des information qui ne sont pas détectables par l'analyse subjective habituelle. La réduction du nombre de paramètres pertinents de 48 à 17 a montré sa cohérence avec le maintien d'une corrélation forte (>0.7) entre tous les groupes et le set complet de paramètres. Des scores paramétriques individuels ont ensuite pu être attribués et un score total a pu être calculé. Les scores finaux, qui montrent notamment la supériorité fonctionnelle de l'arthroplastie totale de cheville, suivie de la TTCA et de l'AA, s'alignent avec les résultats connus d'analyse de marche. Ce travail de thèse est un pas important en direction d'une plus large utilisation de l'analyse de marche ambulatoire en pratique clinique. En effet, un système ambulatoire d'analyse de marche a été appliqué avec succès puis a servi au développement d'un score de marche à la fois précis et simplifié qui offre le potentiel d'utiliser l'analyse de la marche beaucoup plus facilement en pratique clinique et en recherche. -- Today's clinical practice for determining the functional status of patients presenting with foot and ankle pathologies and to assess the efficacy of their surgical treatment is mainly restricted to subjective functional assessment based on questionnaires. This is due to the current gold standard for objective assessment being restricted to research laboratories as a result of the size and cost of the equipment. However, with the recent development of a cost effective, portable, ambulatory gait analysis (AGA) system, which uses wearable 3-D inertial sensors and pressure insoles, the incorporation of an objective gait assessment as part of clinical practice is now a possibility. The goal of the present thesis is to show the capability of such a system in reference to a selected foot and ankle pathology, and to introduce a quantitative functional gait based outcome score. The selected pathology was end-stage ankle osteoarthrosis (AOA) because it is a progressive debilitating disease which can be addressed by various surgical treatments, depending on its severity. The most common treatments include ankle arthrodesis (AA) and total ankle replacement (TAR) and, failing that, tibiotalocalcaneal arthrodesis (TTCA). In general, subjective assessment finds the outcome of all surgeries to be fairly similar. However, objective gait assessment found that significant differences are to be seen, not only in a patients affected / operated side, but also the contra lateral unaffected / un-operated side. The present work enrolled 89 participants, including healthy controls, end-stage AOA patients, AA patients, TAR patients and TTCA patients. The participants were examined using a functional assessment based on clinical scores (AOFAS, FAAM and EQ-5D) as well as an ambulatory gait analysis system. Trials were performed in an open space to allow participants to walk naturally. Both sides, for each participant, were tested. To simplify objective assessment for clinical practice, it was the aim to utilize the ambulatory system to establish clinically relevant gait parameters and to subsequently develop a predictive gait model which can be used as a score in assessing patients and identifying information missed by current subjective assessments. For the development of a simple, yet meaningful gait score for AOA and its surgical corrections, robust parameter reduction using principal component analysis was carried out to minimize the number of relevant parameters, whilst maintaining the majority of important information. The resultant reduction to 17 out of 48 parameter set was consistent in showing strong correlation with the full parameter set across all groups (>0.7). Individual parameter scores were then given to each parameter based on an established outlier classification and a total gait score was calculated accordingly. Final scores align with all previous gait analyses with TAR patients receiving the highest scores, followed by TTCA, and AA each showing improvement over AOA patients. The work presented here is an important step towards promoting the use of ambulatory gait analysis in clinical practice. Hence, a validated AGA system was successfully applied and developed to a simplified and accurate gait score which offers the potential to use AGA more easily in clinical practice and for research purposes

Generalisable FPCA-based Models for Predicting Peak Power in Vertical Jumping using Accelerometer Data

Peak power in the countermovement jump is correlated with various measures of sports performance and can be used to monitor athlete training. The gold standard method for determining peak power uses force platforms, but they are unsuitable for field-based testing favoured by practitioners. Alternatives include predicting peak power from jump flight times, or using Newtonian methods based on body-worn inertial sensor data, but so far neither has yielded sufficiently accurate estimates. This thesis aims to develop a generalisable model for predicting peak power based on Functional Principal Component Analysis applied to body-worn accelerometer data. Data was collected from 69 male and female adults, engaged in sports at recreational, club or national levels. They performed up to 16 countermovement jumps each, with and without arm swing, 696 jumps in total. Peak power criterion measures were obtained from force platforms, and characteristic features from accelerometer data were extracted from four sensors attached to the lower back, upper back and both shanks. The best machine learning algorithm, jump type and sensor anatomical location were determined in this context. The investigation considered signal representation (resultant, triaxial or a suitable transform), preprocessing (smoothing, time window and curve registration), feature selection and data augmentation (signal rotations and SMOTER). A novel procedure optimised the model parameters based on Particle Swarm applied to a surrogate Gaussian Process model. Model selection and evaluation were based on nested cross validation (Monte Carlo design). The final optimal model had an RMSE of 2.5 W·kg-1, which compares favourably to earlier research (4.9 ± 1.7 W·kg-1 for flight-time formulae and 10.7 ± 6.3 W·kg-1 for Newtonian sensor-based methods). Whilst this is not yet sufficiently accurate for applied practice, this thesis has developed and comprehensively evaluated new techniques, which will be valuable to future biomechanical applications

New Training Strategies and Evaluation Methods for Improving Health and Physical Performance

The aim of this Special Issue was to propose, on the basis of the evidence that the current literature provides, new training techniques and specific evaluation methods for the different populations practicing physical activity

Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals

The analysis of knee kinematic data, which come in the form of a small sample of discrete curves that describe repeated measurements of the temporal variation of each of the knee three fundamental angles of rotation during a subject walking cycle, can inform knee pathology classification because, in general, different pathologies have different kinematic data patterns. However, high data dimensionality and the scarcity of reference data, which characterize this type of application, challenge classification and make it prone to error, a problem Duda and Hart refer to as the curse of dimensionality. The purpose of this study is to investigate a sample-based classifier which evaluates data proximity by the two-sample Hotelling T2 statistic. This classifier uses the whole sample of an individual’s measurements for a better support to classification, and the Hotelling T2 hypothesis testing made applicable by dimensionality reduction. This method was able to discriminate between femero-rotulian (FR) and femero-tibial (FT) knee osteoarthritis kinematic data with an accuracy of 88.1% , outperforming significantly current state-of-the-art methods which addressed similar problems. Extended to the much harder three-class problem involving pathology categories FR and FT, as well as category FR-FT which represents the incidence of both diseases FR and FT in a same individual, the scheme was able to reach a performance that justifies its further use and investigation in this and other similar applications

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations