A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms.

Objective: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used for processing this data.

Role of arm reaching movement kinematics in friction perception at initial contact with smooth surfaces.

When manipulating objects, humans begin adjusting their grip force to friction within 100 ms of contact. During motor adaptation, subjects become aware of the slipperiness of touched surfaces. Previously, we have demonstrated that humans cannot perceive frictional differences when surfaces are brought in contact with an immobilised finger, but can do so when there is submillimeter lateral displacement or subjects actively make the contact movement.

Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography.

Objective: Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool.

Open-Source Instrumented Object to Study Dexterous Object Manipulation.

Humans use tactile feedback to perform skillful manipulation. When tactile sensory feedback is unavailable, for instance, if the fingers are anesthetized, dexterity is severely impaired. Imaging the deformation of the finger pad skin when in contact with a transparent plate provides information about the tactile feedback received by the central nervous system.

Design, Fabrication, and Characterization of a Novel Optical Six-Axis Distributed Force and Displacement Tactile Sensor for Dexterous Robotic Manipulation.

Real-time multi-axis distributed tactile sensing is a critical capability if robots are to perform stable gripping and dexterous manipulation, as it provides crucial information about the sensor-object interface. In this paper, we present an optical-based six-axis tactile sensor designed in a fingertip shape for robotic dexterous manipulation. The distributed sensor can precisely estimate the local XYZ force and displacement at ten distinct locations and provide the global XYZ force and torque measurements.

Automatic Segmentation of the Paediatric Femoral Head.

Developmental dysplasia of the hip (DDH) is a developmental deformity occurring in 0.1-3.4% of infants.

Biomechanics of the finger pad in response to torsion.

Surface skin deformation of the finger pad during partial slippage at finger-object interfaces elicits firing of the tactile sensory afferents. A torque around the contact normal is often present during object manipulation, which can cause partial rotational slippage. Until now, studies of surface skin deformation have used stimuli sliding rectilinearly and tangentially to the skin.

A Smartphone-Based Model of Care to Support Patients With Cardiac Disease Transitioning From Hospital to the Community (TeleClinical Care): Pilot Randomized Controlled Trial.

Background: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission.

Objective: This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission.

Process Evaluation of a Randomised Controlled Trial for TeleClinical Care, a Smartphone-App Based Model of Care.

Background: A novel smartphone app-based model of care (TeleClinical Care - TCC) for patients with acute coronary syndrome (ACS) and heart failure (HF) was evaluated in a two-site, pilot randomised control trial of 164 participants in Sydney, Australia. The program included a telemonitoring system whereby abnormal blood pressure, weight and heart rate readings were monitored by a central clinical team, who subsequently referred clinically significant alerts to the patients' usual general practitioner (GP, also known as primary care physician in the United States), HF nurse or cardiologist. While the primary endpoint, 30-day readmissions, was neutral, intervention arm participants demonstrated improvements in readmission rates over 6 months, cardiac rehabilitation (CR) completion and medication compliance.

Submillimeter Lateral Displacement Enables Friction Sensing and Awareness of Surface Slipperiness.

Human tactile perception and motor control rely on the frictional estimates that stem from the deformation of the skin and slip events. However, it is not clear how exactly these mechanical events relate to the perception of friction. This study aims to quantify how minor lateral displacement and speed enables subjects to feel frictional differences.

Trials and Tribulations: mHealth Clinical Trials in the COVID-19 Pandemic.

Introduction: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings.

A review of the neurobiomechanical processes underlying secure gripping in object manipulation.

O'SHEA, H. and S. J.

Friction sensing mechanisms for perception and motor control: passive touch without sliding may not provide perceivable frictional information.

Perception of the frictional properties of a surface contributes to the multidimensional experience of exploring various materials; we slide our fingers over a surface to feel it. In contrast, during object manipulation, we grip objects without such intended exploratory movements. Given that we are aware of the slipperiness of objects or tools that are held in the hand, we investigated whether the initial contact between the fingertip skin and the surface of the object is sufficient to provide this consciously perceived frictional information.

Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone.

Activity recognition can provide useful information about an older individual's activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphone accelerometry data of older people. Deep learning algorithms, including convolutional neural network (CNN) and long short-term memory (LSTM), were evaluated in this study.

Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements.

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.

Estimating Lower Limb Kinematics using Distance Measurements with a Reduced Wearable Inertial Sensor Count.

This paper presents an algorithm that makes novel use of distance measurements alongside a constrained Kalman filter to accurately estimate pelvis, thigh, and shank kinematics for both legs during walking and other body movements using only three wearable inertial measurement units (IMUs). The distance measurement formulation also assumes hinge knee joint and constant body segment length, helping produce estimates that are near or in the constraint space for better estimator stability. Simulated experiments have shown that inter-IMU distance measurement is indeed a promising new source of information to improve the pose estimation of inertial motion capture systems under a reduced sensor count configuration.

Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count.

Goal: This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors.

Methods: The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints).

Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors.

Falls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the "first fall", delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning).

Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities.

Features were developed which accounted for the changing orientation of the inertial measurement unit (IMU) relative to the body, and demonstrably improved the performance of models for human activity recognition (HAR). The method is proficient at separating periods of standing and sedentary activity (i.e.

Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei.

The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked.

Smart Triggering of the Barometer in a Fall Detector Using a Semi-Permeable Membrane.

The inclusion of a barometer in a wearable fall detector has been shown to improve the detection accuracy by measuring the altitude change associated with a fall event. However, the barometer is a power-hungry sensor, and the sensing power of barometer can be the dominant power consumption source in a wearable fall detector. In this study, we propose a triggering method that reduces barometer power consumption and prolongs the battery life.

Improved Kinematics and Motor Control in a Longitudinal Study of a Complex Therapy Movement in Chronic Stroke.

Impaired motor control post-stroke is typically measured using clinical assessments employing categorical and subjective scoring. We investigated quantitative kinematic parameters of a complex movement with therapy in chronic stroke. Tri-axial accelerometry of the more-affected arm of 24 patients was recorded during early- (day 2-3) and late- (days 12-14) therapy, and for 13 patients at 6-month follow-up.

Adaptive template matching of photoplethysmogram pulses to detect motion artefact.

Objective: The photoplethysmography (PPG) signal, commonly used in the healthcare settings, is easily affected by movement artefact leading to errors in the extracted heart rate and SpO estimates. This study aims to develop an online artefact detection system based on adaptive (dynamic) template matching, suitable for continuous PPG monitoring during daily living activities or in the intensive care units (ICUs).

Approach: Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database.

Evaluation of an mHealth-Based Adjunct to Outpatient Cardiac Rehabilitation.

A pilot study was conducted to determine if a smartphone-based adjunct to standard care could increase the completion rate of a cardiac rehabilitation program (CRP). Based on historical completion rates, 66 participants who were about to commence a hospital-based CRP were randomized so that half received three devices embedded with near-field communication, namely, a smartphone [pre-installed with an application (app) designed specifically for cardiac rehabilitation], portable blood pressure monitor, and weight scale while completing the CRP. The completion rate among participants who were randomized to the intervention group was 88%, compared to 67% in the control group ( = 0.

A Low-Power Fall Detector Balancing Sensitivity and False Alarm Rate.

Falls in older people are a major challenge to public health. A wearable fall detector can detect falls automatically based on kinematic information of the human body, allowing help to arrive sooner. To date, most studies have focused on the accuracy of an offline algorithm to distinguish real-world or simulated falls from activities of daily living, while neglecting the false alarm rate and battery life of a real device.