Gait analysis using a single depth camera

Abstract—Gait analysis is often used as part of the rehabilitation program for post-stoke recovery assessment. Since current optical diagnostic and patient assessment tools tend to be expensive and not portable, this paper proposes a novel marker-based tracking system using a single depth camera which provides a cost-effective solution suitable for home and clinic use. The proposed system can simultaneously generate motion patterns even within a complex background using the proposed geometric model-based algorithm and autonomously provide gait analysis results. The processed rehabilitation data can be accessed by cross-platform mobile devices using cloud-based services enabling emerging telerehabilitation practices. Experimental validation shows a good agreement with state-of-the-art non-portable and expensive industrial standards

Gait phase classification for in-home gait assessment

With growing ageing population, acquiring joint measurements with sufficient accuracy for reliable gait assessment is essential. Additionally, the quality of gait analysis relies heavily on accurate feature selection and classification. Sensor-driven and one-camera optical motion capture systems are becoming increasingly popular in the scientific literature due to their portability and cost-efficacy. In this paper, we propose 12 gait parameters to characterise gait patterns and a novel gait-phase classifier, resulting in comparable classification performance with a state-of-the-art multi-sensor optical motion system. Furthermore, a novel multi-channel time series segmentation method is proposed that maximizes the temporal information of gait parameters improving the final classification success rate after gait event reconstruction. The validation, conducted over 126 experiments on 6 healthy volunteers and 9 stroke patients with handlabelled ground truth gait phases, demonstrates high gait classification accuracy

A depth camera motion analysis framework for tele-rehabilitation : motion capture and person-centric kinematics analysis

With increasing importance given to telerehabilitation, there is a growing need for accurate, low-cost, and portable motion capture systems that do not require specialist assessment venues. This paper proposes a novel framework for motion capture using only a single depth camera, which is portable and cost effective compared to most industry-standard optical systems, without compromising on accuracy. Novel signal processing and computer vision algorithms are proposed to determine motion patterns of interest from infrared and depth data. In order to demonstrate the proposed framework’s suitability for rehabilitation, we developed a gait analysis application that depends on the underlying motion capture sub-system. Each subject’s individual kinematics parameters, which are unique to that subject, are calculated and these are stored for monitoring individual progress of the clinical therapy. Experiments were conducted on 14 different subjects, 5 healthy and 9 stroke survivors. The results show very close agreement of the resulting relevant joint angles with a 12-camera based VICON system, a mean error of at most 1.75% in detecting gait events w.r.t the manually generated ground-truth, and significant performance improvements in terms of accuracy and execution time compared to a previous Kinect-based system

Kinematics analysis multimedia system for rehabilitation

Driven by recent advances in information and communications technology, tele-rehabilitation services based on multimedia processing are emerging. Gait analysis is common for many rehabilitation programs, being, for example, periodically performed in the post-stroke recovery assessment. Since current optical diagnostic and patient assessment tools tend to be expensive and not portable, this paper proposes a novel marker-based tracking system using a single depth camera which provides a cost-effective solution that enables tele-rehabilitation services from home and local clinics. The proposed system can simultaneously generate motion patterns even within a complex background using the proposed geometric model-based algorithm and autonomously provide gait analysis results using a customised user-friendly application that facilitates seamless navigation through the captured scene and multi-view video data processing, designed using feedback from practitioners to maximise user experience. The locally processed rehabilitation data can be accessed by cross-platform mobile devices using cloud-based services enabling emerging tele-rehabilitation practices

Distinct feature extraction for video-based gait phase classification

Recent advances in image acquisition and analysis have resulted in disruptive innovation in physical rehabilitation systems facilitating cost-effective, portable, video-based gait assessment. While these inexpensive motion capture systems, suitable for home rehabilitation, do not generally provide accurate kinematics measurements on their own, image processing algorithms ensure gait analysis that is accurate enough for rehabilitation programs. This paper proposes high-accuracy classification of gait phases and muscle actions, using readings from low-cost motion capture systems. First, 12 gait parameters, drawn from the medical literature, are defined to characterize gait patterns. These proposed parameters are then used as input to our proposed multi-channel time-series classification and gait phase reconstruction methods. Proposed methods fully utilize temporal information of gait parameters, thus improving the final classification accuracy. The validation, conducted using 126 experiments, with 6 healthy volunteers and 9 stroke survivors with manually-labelled gait phases, achieves state-of-art classification accuracy of gait phase with lower computational complexity compared to previous solution

Model selection-inspired coefficients optimization for polynomial-kernel graph learning

Graph learning has been extensively investigated for over a decade, in which the graph structure can be learnt from multiple graph signals (e.g., graphical Lasso) or node features (e.g., graph metric learning). Given partial graph signals, existing node feature-based graph learning approaches learn a pair-wise distance metric with gradient descent, where the number of optimization variables dramatically scale with the node feature size. To address this issue, in this paper, we propose a low-complexity model selection-inspired graph learning (MSGL) method with very few optimization variables independent with feature size, via leveraging on recent advances in graph spectral signal processing (GSP). We achieve this by 1) interpreting a finite-degree polynomial function of the graph Laplacian as a positive-definite precision matrix, 2) formulating a convex optimization problem with variables being the polynomial coefficients, 3) replacing the positive-definite cone constraint for the precision matrix with a set of linear constraints, and 4) solving efficiently the objective using the Frank-Wolfe algorithm. Using binary classification as an application example, our simulation results show that our proposed MSGL method achieves competitive performance with significant speed gains against existing node feature-based graph learning methods

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Are narcissists more satisfied with their lives? The mediating roles of general trust and positive emotions

Life satisfaction is a widely-shared goal and a core theme in the field of positive psychology. From the perspective of personality, some researchers have focused on the relationship between narcissism and life satisfaction, but there is a lack of research on the underlying process between them. This study aimed to examine the relationship between narcissism and life satisfaction based on the narcissistic admiration and rivalry concept (NARC) model and explored the mediating roles of general trust and positive emotions. In addition, to avoid confusion between the concepts of narcissism and self-esteem, this study measured and controlled for self-esteem in the mediation model test. In the current study, college students completed the Narcissistic Admiration and Rivalry Questionnaire, General Trust Scale, Positive Emotions of Scale of Positive and Negative Experience Scale, Satisfaction with Life Scale, and Rosenberg Self-esteem Scale. Ultimately, a total of 912 participants were analyzed, including 398 males (43.6%) and 506 females (55.5%) (M-age = 19.58 years, SD = 1.35 years). The results showed that narcissistic admiration had a positive indirect effect on life satisfaction, while narcissistic rivalry had a negative indirect effect on life satisfaction. General trust and positive emotions were two mediators of the relationship between narcissism and life satisfaction. These findings suggest that different dimensions of narcissism have different effects on life satisfaction. Our findings have significant practical and theoretical implications for enhancing the life satisfaction of individuals with narcissistic tendencies

Can Treating Oneself Kindly Inspire Trust? The Role of Interpersonal Responsibility

Self-compassion, as a personal psychological resource, has been proved to play an important role in coping with suffering. Based on self-determination theory, the present study attempts to establish that self-compassion can promote trust, and the sense of interpersonal responsibility mediates this relationship. Study 1 used cross-sectional data in a community sample of 322 adults to reveal that self-compassion was positively related to trust, and the mediating effects of the sense of interpersonal responsibility were significant. Study 2 used the latent cross-lagged panel model among 1304 college students at three-time points set at six-month intervals to replicate the results and proved the longitudinal mediating effects across groups. Finally, a casual chain design was used to test the mediation effect in Studies 3 and 4. The results indicated that self-compassion induced by writing task resulted in a sense of responsibility in Study 3 (N = 145), and the manipulated sense of responsibility promoted both trust behaviors and beliefs in Study 4 (N = 125). Through four studies, this study highlights a novel but unexpected viewpoint that treating oneself in a self-compassionate way can not only help individuals cope with various challenges but also motivate them to obtain interpersonal benefits. These findings can help motivate community workers and mental health researchers to increase social capital by focusing on self-compassion and interpersonal responsibility

Putting a Terbium-Monometallic Cyanide Cluster into the C₈₂ Fullerene Cage: TbCN@<i>C</i>₂(5)‑C₈₂

The first terbium (Tb)-monometallic cyanide clusterfullerene (CYCF), TbCN@C₈₂, has been successfully synthesized and isolated, whose molecular structure was determined unambiguously as TbCN@<i>C</i>₂(5)-C₈₂ by single crystal X-ray diffraction. The <i>C</i>₂(5)-C₈₂ isomeric cage represents a new cage capable of encapsulating a monometallic cyanide cluster. The C–N bond length within the encaged TbCN cluster is determined to be 0.94(5) Å, which is smaller by at least 0.17 Å than those of the reported C–N triplet bonds in traditional cyanide/nitrile compounds and cyano coordination complexes. An electronic configuration of [Tb³⁺(CN)⁻]²⁺@[C₈₂]^2– was proposed for TbCN@C₈₂