Bias Analysis in Healthcare Time Series (BAHT) Decision Support Systems from Meta Data.

One of the hindrances in the widespread acceptance of deep learning-based decision support systems in healthcare is bias. Bias in its many forms occurs in the datasets used to train and test deep learning models and is amplified when deployed in the real world, leading to challenges such as model drift. Recent advancements in the field of deep learning have led to the deployment of deployable automated healthcare diagnosis decision support systems at hospitals as well as tele-medicine through IoT devices.

CEFEs: A CNN Explainable Framework for ECG Signals.

In the healthcare domain, trust, confidence, and functional understanding are critical for decision support systems, therefore, presenting challenges in the prevalent use of black-box deep learning (DL) models. With recent advances in deep learning methods for classification tasks, there is an increased use of deep learning in healthcare decision support systems, such as detection and classification of abnormal Electrocardiogram (ECG) signals. Domain experts seek to understand the functional mechanism of black-box models with an emphasis on understanding how these models arrive at specific classification of patient medical data.

Personalized 3D exergames for in-home rehabilitation after stroke: a pilot study.

Purpose: To describe a novel 3-dimensional (3D) exergames system and the results of a clinical feasibility study of stroke survivors needing in-home rehabilitation.

Materials And Methods: The customisable Personalized In-home eXErgames for Rehabilitation (PIXER) system captures the user's image, generates a live model, and incorporates it into a virtual exergame. PIXER provides a recording system for home exercise programs (HEPs) by adapting virtual objects, customizes the exergame and creates a digital diary.

Using Biometric Technology for Telehealth and Telerehabilitation.

This article discusses the use of physical and biometric sensors in telerehabilitation. It also discusses synchronous tele-physical assessment using haptics and augmented reality and asynchronous physical assessment using remote pose estimation. The article additionally focuses on computational models that have the potential to monitor and evaluate changes in kinematic and kinetic properties during telerehabilitation using biometric sensors such as electromyography and other wearable and noncontact sensors based on force and speed.

Usability of an Immersive Augmented Reality Based Telerehabilitation System with Haptics (ARTESH) for Synchronous Remote Musculoskeletal Examination.

This study describes the features and utility of a novel augmented reality based telemedicine system with haptics that allows the sense of touch and direct physical examination during a synchronous immersive telemedicine consultation and physical examination. The system employs novel engineering features: (a) a new force enhancement algorithm to improve force rendering and overcoming the "just-noticeable-difference" limitation; (b) an improved force compensation method to reduce the delay in force rendering; (c) use of the "haptic interface point" to reduce disparity between the visual and haptic data; and (d) implementation of efficient algorithms to process, compress, decompress, transmit and render 3-D tele-immersion data. A qualitative pilot study (n=20) evaluated the usability of the system.

Discovering Multidimensional Motifs in Physiological Signals for Personalized Healthcare.

Personalized diagnosis and therapy requires monitoring patient activity using various body sensors. Sensor data generated during personalized exercises or tasks may be too specific or inadequate to be evaluated using supervised methods such as classification. We propose multidimensional motif (MDM) discovery as a means for patient activity monitoring, since such motifs can capture repeating patterns across multiple dimensions of the data, and can serve as conformance indicators.

Motion Capture With Ellipsoidal Skeleton Using Multiple Depth Cameras.

This paper introduces a novel motion capturing framework which works by minimizing the fitting error between an ellipsoid based skeleton and the input point cloud data captured by multiple depth cameras. The novelty of this method comes from that it uses the ellipsoids equipped with the spherical harmonics encoded displacement and normal functions to capture the geometry details of the tracked object. This method is also integrated with a mechanism to avoid collisions of bones during the motion capturing process.

Distributed haptic interactions with physically based 3D deformable models over lossy networks.

Researchers have faced great challenges when simulating complicated 3D volumetric deformable models in haptics-enabled collaborative/cooperative virtual environments (HCVEs) due to the expensive simulation cost, heavy communication load, and unstable network conditions. When general network services are applied to HCVEs, network problems such as packet loss, delay, and jitter can cause severe visual distortion, haptic instability, and system inconsistency. In this paper, we propose a novel approach to support haptic interactions with physically based 3D deformable models in a distributed virtual environment.

Mining pattern sequences in respiratory tumor motion data.

Management of respiration induced tumor motion during radiation therapy is crucial to effective treatment. Pattern sequences in the tumor motion signals can be valuable features in the analysis and prediction of irregular tumor motion. In this study, we put forward an approach towards mining pattern sequences in respiratory tumor motion data.

Point-based manifold harmonics.

This paper proposes an algorithm to build a set of orthogonal Point-Based Manifold Harmonic Bases (PB-MHB) for spectral analysis over point-sampled manifold surfaces. To ensure that PB-MHB are orthogonal to each other, it is necessary to have symmetrizable discrete Laplace-Beltrami Operator (LBO) over the surfaces. Existing converging discrete LBO for point clouds, as proposed by Belkin et al.

A body sensor network with electromyogram and inertial sensors: multimodal interpretation of muscular activities.

The evaluation of the postural control system (PCS) has applications in rehabilitation, sports medicine, gait analysis, fall detection, and diagnosis of many diseases associated with a reduction in balance ability. Standing involves significant muscle use to maintain balance, making standing balance a good indicator of the health of the PCS. Inertial sensor systems have been used to quantify standing balance by assessing displacement of the center of mass, resulting in several standardized measures.

Indexing 3-D human motion repositories for content-based retrieval.

Content-based retrieval of the similar motions for the human joints has significant impact in the fields of physical medicine, biomedicine, rehabilitation, and motion therapy. In this paper, we propose an efficient indexing approach for 3-D human motion capture data, supporting queries involving both subbody motions as well as whole-body motions.