Detection and diagnosis of paralysis agitans

Humans’ daily behavior can reflect the main physiological characteristics of neurological diseases. Human gait is a complex behavior produced by the coordination of multiple physiological systems such as the nervous system and the muscular system. It can reflect the physiological state of human health, and its abnormality is an important basis for diagnosing some nervous system diseases. However, many early gait anomalies have not been effectively discovered because of medical costs and people's living customs. This paper proposes an effective, economical, and accurate non-contact cognitive diagnosis system to help early detection and diagnosis of paralysis agitans under daily life conditions. The proposed system extract data from wireless state information obtained from antenna-based data gathering module. Further, we implement data processing and gait classification systems to detect abnormal gait based on the acquired wireless data. In the experiment, the proposed system can detect the state of human gait and carries high classification accuracy up to 96.7 %. The experimental results demonstrate that the proposed technique is feasible and cost-effective for healthcare applications

Automating senior fitness testing through gesture detection with depth sensors

Sedentarism has a negative impact on health, life expectancy and quality of life, especially in older adults. The assessment of functional fitness helps evaluating the effects of ageing and sedentarism, and this assessment is typically done through validated battery tests such as the Senior Fitness Test (SFT). In this paper we present a computer-based system for assisting and automating SFT administration and scoring in the elderly population. Our system assesses lower body strength, agility and dynamic balance, and aerobic endurance making use of a depth sensor for body tracking and multiple gesture detectors for the evaluation of movement execution. The system was developed and trained with optimal data collected in laboratory conditions and its performance was evaluated in a real environment with 22 elderly end-users, and compared to traditional SFT administered by an expert. Results show a high accuracy of our system in identifying movement patterns (>95%) and consistency with the traditional fitness assessment method. Our results suggest that this technology is a viable low cost option to assist in the fitness assessment of elderly that could be deployed for at home use in the context of fitness programs.info:eu-repo/semantics/publishedVersio

Gait Recognition from Motion Capture Data

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392, arXiv:1609.0693

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

Разработка средств сбора и логического анализа 3D-видеоданных на основе времяпролётной камеры и Акторного Пролога

Предложен подход к интеллектуальному 3D-видеонаблюдению на основе объектно-ориентированного логического программирования. В отличие от обычного 2D-видеонаблюдения, методы трёхмерного зрения обеспечивают надёжное распознавание частей тела, что делает возможным новые постановки задачи практическое применение методов анализа поведения людей в системах видеонаблюдения. Логический подход к интеллектуальному видеонаблюдению позволяет описывать сложное поведение людей на основе определений простых действий и поз. Цель данной работы заключается в реализации этих преимуществ логического подхода в области интеллектуального 3D-видеонаблюдения.Работа выполнена при поддержке РФФИ, грант № 16-29-09626-офи_м

Abnormal gait detection with RGB-D devices using joint motion history features

Human gait has become of special interest to health professionals and researchers in recent years, not only due to its relation to a person’s quality of life and personal autonomy, but also due to the involved cognitive process, since deviation from normal gait patterns can also be associated to neurological diseases. Vision-based abnormal gait detection can provide support to current human gait analysis procedures providing quantitative and objective metrics that can assist the evaluation of the geriatrician, while at the same time providing technical advantages, such as low intrusiveness and simplified setups. Furthermore, recent advances in RGB-D devices allow to provide low-cost solutions for 3D human body motion analysis. In this sense, this work presents a method for abnormal gait detection relying on skeletal pose representation based on depth data. A novel spatio-temporal feature is presented that provides a representation of a set of consecutive skeletons based on the 3D location of the skeletal joints and the motion’s age. The corresponding feature sequences are learned using a machine learning method, namely BagOfKeyPoses. Experimentation with different datasets and evaluation methods shows that reliable detection of abnormal gait is obtained and, at the same time, an outstandingly high temporal performance is provided

Determinación del perfil antropométrico en secuencia de video

La obtención del perfil antropométrico en secuencias de video es un problema abierto que representa un gran desafío en la visión por computadora. Esta información es útil para fines estadísticos o hasta en el reconocimiento de personas en videos de vigilancia; ya que estas características suelen utilizarse como descriptores semánticos. Hasta ahora, el mejor rendimiento se puede lograr mediante el uso de cámaras 3D, pero este enfoque requiere de hardware especial. Otros enfoques 2D logran buenos resultados en situaciones normales, pero fallan cuando la persona usa ropa suelta, lleva bolsas o el ángulo de la imagen cambia ya que se basan en el cálculo de bordes, siluetas, o la energía de la persona en la imagen. Este trabajo tiene como objetivo proporcionar una nueva metodología de obtención de características humanas basada en la creación de un esqueleto virtual para cada individuo a partir de imágenes y vídeo en 2D, luego se miden las distancias entre algunos puntos del esqueleto, y funcionan como entrada de un clasificador que determina su género. Esto mejora los resultados en comparación con otros algoritmos para el mismo fin, ya que la ropa, las bolsas y el ángulo de la imagen afectan poco al proceso de esqueletización