Estimating Lower Limb Kinematics using a Lie Group Constrained EKF and a Reduced Wearable IMU Count

This paper presents an algorithm that makes novel use of a Lie group representation of position and orientation alongside a constrained extended Kalman filter (CEKF) to accurately estimate pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors. The algorithm iterates through the prediction update (kinematic equation), measurement update (pelvis height, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The paper also describes a novel Lie group formulation of the assumptions implemented in the said measurement and constraint updates. Evaluation of the algorithm on nine healthy subjects who walked freely within a $4 \times 4$ m$^2$ room shows that the knee and hip joint angle root-mean-square errors (RMSEs) in the sagittal plane for free walking were $10.5 \pm 2.8^\circ$ and $9.7 \pm 3.3^\circ$, respectively, while the correlation coefficients (CCs) were $0.89 \pm 0.06$ and $0.78 \pm 0.09$, respectively. The evaluation demonstrates a promising application of Lie group representation to inertial motion capture under reduced-sensor-count configuration, improving the estimates (i.e., joint angle RMSEs and CCs) for dynamic motion, and enabling better convergence for our non-linear biomechanical constraints. To further improve performance, additional information relating the pelvis and ankle kinematics is needed.Comment: 6 pages. arXiv admin note: text overlap with arXiv:1910.0091

From A to Z: Wearable technology explained

Wearable technology (WT) has become a viable means to provide low-cost clinically sensitive data for more informed patient assessment. The benefit of WT seems obvious: small, worn discreetly in any environment, personalised data and possible integration into communication networks, facilitating remote monitoring. Yet, WT remains poorly understood and technology innovation often exceeds pragmatic clinical demand and use. Here, we provide an overview of the common challenges facing WT if it is to transition from novel gadget to an efficient, valid and reliable clinical tool for modern medicine. For simplicity, an A–Z guide is presented, focusing on key terms, aiming to provide a grounded and broad understanding of current WT developments in healthcare

Peripheral photoplethysmography variability analysis of sepsis patients

Sepsis is associated with impairment in autonomic regulatory function. This work investigates the application of heart rate and photoplethysmogram (PPG) waveform variability analysis in differentiating two categories of sepsis, namely systemic inflammatory response syndrome (SIRS) and severe sepsis. Electrocardiogram-derived heart period (RRi) and PPG waveforms, measured from fingertips (Fin-PPG) and earlobes (Ear-PPG), of Emergency Department sepsis patients (n = 28) with different disease severity, were analysed by spectral technique, and were compared to control subjects (n = 10) in supine and 80° head-up tilted positions. Analysis of covariance (ANCOVA) was applied to adjust for the confounding factor of age. Low-frequency (LF, 0.04-0.15 Hz), mid-frequency (MF, 0.09-0.15 Hz) and high-frequency (HF, 0.15-0.60 Hz) powers were computed. The normalised MF power in Ear-PPG (MFnu Ear) was significantly reduced in severe sepsis patients with hyperlactataemia (lactate > 2 mmol/l), compared to SIRS patients (P 0.05), suggesting that there may be a link between 0.1 Hz ear blood flow oscillation and tissue metabolic changes in sepsis, in addition to autonomic factors. The study highlighted the value of PPG spectral analysis in the non-invasive assessment of peripheral vascular regulation in sepsis patients, with potential implications in monitoring the progression of sepsis

Optimisation of Ionic Models to Fit Tissue Action Potentials: Application to 3D Atrial Modelling

A 3D model of atrial electrical activity has been developed with spatially heterogeneous electrophysiological properties. The atrial geometry, reconstructed from the male Visible Human dataset, included gross anatomical features such as the central and peripheral sinoatrial node (SAN), intra-atrial connections, pulmonary veins, inferior and superior vena cava, and the coronary sinus. Membrane potentials of myocytes from spontaneously active or electrically paced in vitro rabbit cardiac tissue preparations were recorded using intracellular glass microelectrodes. Action potentials of central and peripheral SAN, right and left atrial, and pulmonary vein myocytes were each fitted using a generic ionic model having three phenomenological ionic current components: one time-dependent inward, one time-dependent outward, and one leakage current. To bridge the gap between the single-cell ionic models and the gross electrical behaviour of the 3D whole-atrial model, a simplified 2D tissue disc with heterogeneous regions was optimised to arrive at parameters for each cell type under electrotonic load. Parameters were then incorporated into the 3D atrial model, which as a result exhibited a spontaneously active SAN able to rhythmically excite the atria. The tissue-based optimisation of ionic models and the modelling process outlined are generic and applicable to image-based computer reconstruction and simulation of excitable tissue

Abstract 12373: Process Evaluation of a Vanguard Phase of a Trial for a Smartphone- App Based Model Of Care

Background: Home-based monitoring and timely intervention of patients with acute coronary syndrome (ACS) and heart failure (HF) can improve patient outcomes. TeleClinical Care (TCC-Cardiac) is a smartphone app-based model of care designed to improve community-based care. We conducted a process evaluation within a multi-centre RCT to elucidate barriers and facilitators to implementation. Methods Patients were enrolled during their hospital stay and provided with an app and Bluetooth observation monitors. Patients were encouraged to engage with the app regularly (i.e. exercise program, record their observations). Data were monitored by a dedicated Remote Monitoring Solution team (RMS), and information was made available to the care team. Interviews were conducted with patients at 6 months, and with 3 RMS members, 2 HF nurses and 1 cardiologist. Results Of the 35 enrolled patients, 27 (77%) had a diagnosis of ACS and 8 (23%) of HF. Patient engagement with the app was good overall, with adherence tailing off over time, demonstrated by median (IQI) patient self-BP recordings of 93.3% (90-100%) at 1-month compared to 70% (43-90%) at 6-months. Older patients had higher levels of adoption (>50%), with a median age amongst high adopters of 67-years (IQI 40-82) compared to 63-years (IQI 50-87) for medium adopters (50-70%) and 44-years (IQI 33-53) amongst low adopters (<50%) (p=0.0184) (Fig 1). Interviews revealed good acceptance of Telehealth and remote monitoring. Higher motivation and health and tech literacy facilitated adherence. 21% of participants meeting inclusion criteria but not enrolled did not possess a smartphone. GPs did not engage. Other health professionals identified the time requirements of supporting less tech-literate patients and responding to alerts as potential barriers to adoption. However, most valued the potential for early detection and prevention of deterioration of this cohort of patients. Conclusion TCC-cardiac was generally considered an acceptable and viable model of care by patients and health professionals. Adherence was best amongst older patients with the highest potential to benefit. Designing strategies to overcome barriers to adherence in younger patients and GPs, combined with adequate tech support is recommended

Time-Frequency Based Features for Classification of Walking Patterns

The analysis of gait data has been a challenging problem and several new approaches have been proposed in recent years. This paper describes a novel front-end for classification of gait patterns using data obtained from a tri-axial accelerometer. The novel features consist of delta features, low and high frequency signal variations and energy variations in both frequency bands. The back-end of the system is a Gaussian mixture model based classifier. Using Bayesian adaptation, an overall classification accuracy of 96.1 % was achieved for five walking patterns in six subjects. Index Terms—Gait patterns, accelerometry, ambulatory monitoring, Gaussian mixture model