Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm’s low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.
Background Falls among older adults have become a global concern. While previous studies have established associations between autonomic function indicator; heart rate variability (HRV) and blood pressure variability (BPV) with fall recurrence, as well as physical inactivity and psychological disorders as risk factors for falls, the influence of physical activity and psychological status on autonomic dysfunction observed among older fallers has not been adequately investigated. The aim of this study was to evaluate the relationship between psychological disorder and physical performance on the autonomic nervous system (ANS) in older fallers. We hypothesised that older fallers have poorer autonomic function, greater dependency on others and were associated with psychological disorders. Furthermore, we hypothesised that both physical performance and psychological status can contribute to the worsening of the autonomic function among the elderly. Methods In this cross-sectional survey, adults aged ≥ 60 years were recruited. Continuous non-invasive BP was monitored over 5 min of supine and 3 min of standing. Psychological status was assessed in terms of depression, anxiety, stress, and concern about falling, while functional status was measured using time-up-and-go, functional reach, handgrip and Lawton’s Instrumental Activities of Daily Life (IADL) scale. Results A total of 62 participants were recruited consisting of 37 fallers and 25 non-fallers. Multivariate analysis revealed that Lawton IADL was independently associated with systolic blood pressure variability (SBPV) and diastolic blood pressure variability (DBPV) during both supine (SBPV: r2 = 0.080, p = 0.025; DBPV: r2 = 0.064, p = 0.046) and standing (SBPV: r2 = 0.112, p = 0.008; DBPV: r2 = 0.105, p = 0.011), while anxiety score was independently associated with SBPV and DBPV during standing (SBPV: r2 = 0.112, p = 0.009; DBPV: r2 = 0.105, p = 0.011) as compared to the other parameters. Conclusion Our findings suggest that fallers had poorer ANS, greater dependence in IADLs, and were more anxious. IADL dependency and anxiety were the most predictive of autonomic dysfunction, and can be used in practice to identify poor autonomic function for the prevention of falls and cardiovascular diseases among older adults.
Control for dual rotary left ventricular assist devices (LVADs) used as a biventricular assist device (BiVAD) is challenging. If the control system fails, flow imbalance between the systemic and the pulmonary circulations would result, subsequently leading to ventricular suction or pulmonary congestion. With the expectation that advanced control approaches such as model predictive control could address the challenges naturally and effectively, we developed a synergistic first principles model predictive controller (MPC) for the BiVAD. The internal model of the MPC is a simplified state-space model that has been developed and validated in a previous study. A single Frank-Starling (FS) control curve was used to define the target pump flow corresponding to the preload on each side of the heart. The MPC was evaluated in a validated numerical model using three clinical scenarios: blood loss, myocardial recovery, and exercise. Simulation results showed that the MPC was effective in adapting to changes in physiological states without causing ventricular suction or pulmonary congestion. The use of MPC for a BiVAD eliminates the need for two controllers of dual LVADs thus making the task of controller tuning easier.
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