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The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.
Background
Elderly patients are a growing population in cardiac rehabilitation (CR). As postural control declines with age, assessment of impaired balance is important in older CR patients in order to predict fall risk and to initiate counteracting steps. Functional balance tests are subjective and lack adequate sensitivity to small differences, and are further subject to ceiling effects. A quantitative approach to measure postural control on a continuous scale is therefore desirable. Force plates are already used for this purpose in other clinical contexts, therefore could be a promising tool also for older CR patients. However, in this population the reliability of the assessment is not fully known.
Research question
Analysis of test-retest reliability of center of pressure (CoP) measures for the assessment of postural control using a force plate in older CR patients.
Methods
156 CR patients (> 75 years) were enrolled. CoP measures (path length (PL), mean velocity (MV), and 95% confidence ellipse area (95CEA)) were analyzed twice with an interval of two days in between (bipedal narrow stance, eyes open (EO) and closed (EC), three trials for each condition, 30 s per trial), using a force plate. For test-retest reliability estimation absolute differences (& UDelta;: T0-T1), intraclass correlation coefficients (ICC) with 95% confidence intervals, standard error of measurement and minimal detectable change were calculated.
Results
Under EO condition ICC were excellent for PL and MV (0.95) and good for 95CEA (0.88) with & UDelta; of 10.1 cm (PL), 0.3 cm/sec (MV) and 1.5 cm(2 )(95CEA) respectively. Under EC condition ICC were excellent (> 0.95) for all variables with larger & UDelta; (PL: 21.7 cm; MV: 0.7 cm/sec; 95CEA: 2.4 cm(2))
Significance
In older CR patients, the assessment of CoP measures using a force plate shows good to excellent test retest reliability.