Search

Background and Objective: Motion analysis is crucial for effective and timely rehabilitative interventions on people with motor disorders. Conventional marker-based (MB) gait analysis is highly time-consuming and calls for expensive equipment, dedicated facilities and personnel. Markerless (ML) systems may pave the way to less demanding gait monitoring, also in unsupervised environments (i.e., in telemedicine). However,scepticism on clinical usability of relevant outcome measures has hampered its use. ML is normally used to analyse treadmill walking, which is significantly different from the more physiological overground walking. This study aims to provide end-users with instructions on using a single-camera markerless system to obtain reliable motion data from overground walking, while clinicians will be instructed on the reliability of obtained quantities., Methods: The study compares kinematics obtained from ML systems to those concurrently obtained from marker-based systems, considering different stride counts and subject positioning within the capture volume., Results: The findings suggest that five straight walking trials are sufficient for collecting reliable kinematics with ML systems. Precision on joint kinematics decreased at the boundary of the capture volume. Excellent correlation was found between ML and MB systems for hip and knee angles (0.922 <0.96), with slightly lower correlations observed for ankle plantar-dorsiflexion. The Bland-Altman analysis indicated the largest bias for hip flexion/extension ([0.2 ∘ ,10.9 ∘ ]) and the smallest for knee joint ([0.1 ∘ ,0.8 ∘ ]) when comparing MB-PiG and MB-JC approaches. For MB-JC vs. ML-JC comparison, the largest bias was for the ankle joint ([1.2 ∘ ,11.8 ∘ ]), while the smallest was for the hip joint ([0.2 ∘ ,7.3 ∘ ])., Conclusion: Single-camera markerless motion capture systems have great potential in assessing human joint kinematics during overground walking. Clinicians can confidently rely on estimated joint kinematics while walking, enabling personalized interventions and improving accessibility to remote evaluation and rehabilitation services, as long as: (i) the camera is positioned to capture someone walking back and forth at least five times with good visibility of the entire body silhouette; (ii) the walking path is at least 2 m long; and (iii) images captured at the boundaries of the camera image plane should be discarded., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Human pose estimation (HPE) through deep learning-based software applications is a trend topic for markerless motion analysis. Thanks to the accuracy of the state-of-the-art technology, HPE could enable gait analysis in the telemedicine practice. On the other hand, delivering such a service at a distance requires the system to satisfy multiple and different constraints like accuracy, portability, real-time, and privacy compliance at the same time. Existing solutions either guarantee accuracy and real-time (e.g., the widespread OpenPose software on well-equipped computing platforms) or portability and data privacy (e.g., light convolutional neural networks on mobile phones). We propose a portable and low-cost platform that implements real-time and accurate 3D HPE through an embedded software on a low-power off-the-shelf computing device that guarantees privacy by default and by design. We present an extended evaluation of both accuracy and performance of the proposed solution conducted with a marker-based motion capture system (i.e., Vicon) as ground truth. The results show that the platform achieves real-time performance and high-accuracy with a deviation below the error tolerance when compared to the marker-based motion capture system (e.g., less than an error of 5 ∘ on the estimated knee flexion difference on the entire gait cycle and correlation 0.91<ρ<0.99). We provide a proof-of-concept study, showing that such portable technology, considering the limited discrepancies with respect to the marker-based motion capture system and its working tolerance, could be used for gait analysis at a distance without leading to different clinical interpretation., Competing Interests: Declaration of Competing Interest We would like to confirm no conflict of interest, financial or other, exists. This manuscript is entirely original, has not been copyrighted, published, submitted, or accepted for publication elsewhere., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In the last years there have been significant improvements in the accuracy of real-time 3D skeletal data estimation software. These applications based on convolutional neural networks (CNNs) can playa key role in a variety of clinical scenarios, from gait analysis to medical diagnosis. One of the main challenges is to apply such intelligent video analytic at a distance, which requires the system to satisfy, beside accuracy, also data privacy. To satisfy privacy by default and by design, the software has to run on "edge" computing devices, by which the sensitive information (i.e., the video stream) is elaborated close to the camera while only the process results can be stored or sent over the communication network. In this paper we address such a challenge by evaluating the accuracy of the state-of-the-art software for human pose estimation when run "at the edge". We show how the most accurate platforms for pose estimation based on complex and deep neural networks can become inaccurate due to subs amp ling of the input video frames when run on the resource constrained edge devices. In contrast, we show that, starting from less accurate and "lighter" CNNs and enhancing the pose estimation software with filters and interpolation primitives, the platform achieves better real-time performance and higher accuracy with a deviation below the error tolerance of a marker-based motion capture system.

We assessed differences in long-term all-cause and cardiovascular (CV) mortality in heart failure (HF) outpatients based on the etiology of HF. Consecutive patients admitted to the HF Clinic from August 2001 to September 2019 ( N = 2587) were considered for inclusion. HF etiology was divided into ischemic heart disease (IHD), dilated cardiomyopathy (DCM), hypertensive heart disease, alcoholic cardiomyopathy, drug-induced cardiomyopathy (DICM), valvular heart disease, and hypertrophic cardiomyopathy. All-cause death and CV death were the primary end points. Among 2387 patients included in the analysis (mean age 66.5 ± 12.5 years, 71.3% men), 1317 deaths were recorded (731 from CV cause) over a maximum follow-up of 18 years (median 4.1 years, interquartile range (IQR) 2-7.8). Considering IHD as the reference, only DCM had a lower risk of all-cause death (adjusted hazard ratio (aHR) 0.68, 95% confidence interval (CI) 0.56-0.83, p < 0.001), and only DICM had a higher risk of all-cause death (aHR 1.47, 95% CI 1.02-2.11, p = 0.04). However, almost all etiologies had a significantly lower risk of CV death than IHD. Among the studied HF etiologies, DCM and DICM have the lowest and highest risk of all-cause death, respectively, whereas IHD has the highest adjusted risk of CV death.

Aims: Several heart failure (HF) web-based risk scores are currently used in clinical practice. Currently, we lack head-to-head comparison of the accuracy of risk scores. This study aimed to assess correlation and mortality prediction performance of Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, which includes clinical variables + medications; Seattle Heart Failure Model (SHFM), which includes clinical variables + treatments + analytes; PARADIGM Risk of Events and Death in the Contemporary Treatment of Heart Failure (PREDICT-HF) and Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, which also include biomarkers, like N-terminal pro B-type natriuretic peptide (NT-proBNP)., Methods and Results: A total of 1166 consecutive patients with HF from different aetiologies that had NT-proBNP measurement at first visit were included. Discrimination for all-cause mortality was compared by Harrell's C-statistic from 1 to 5 years, when possible. Calibration was assessed by calibration plots and Hosmer-Lemeshow test and global performance by Nagelkerke's R 2 . Correlation between scores was assessed by Spearman rank test. Correlation between the scores was relatively poor (rho value from 0.66 to 0.79). Discrimination analyses showed better results for 1-year mortality than for longer follow-up (SHFM 0.817, MAGGIC-HF 0.801, PREDICT-HF 0.799, BCN-Bio-HF 0.830). MAGGIC-HF showed the best calibration, BCN-Bio-HF overestimated risk while SHFM and PREDICT-HF underestimated it. BCN-Bio-HF provided the best discrimination and overall performance at every time-point., Conclusions: None of the contemporary risk scores examined showed a clear superiority over the rest. BCN-Bio-HF calculator provided the best discrimination and overall performance with overestimation of risk. MAGGIC-HF showed the best calibration, and SHFM and PREDICT-HF tended to underestimate risk. Regular updating and recalibration of online web calculators seems necessary to improve their accuracy as HF management evolves at unprecedented pace., (© 2021 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)