Measures of heart rate variability (HRV) are widely used to assess autonomic nervous system (ANS) function. The signal from which they are derived requires accurate determination of the interval between successive heartbeats; it can be recorded via electrocardiography (ECG), which is both non-invasive and widely available. However, methodological problems inherent in the recording and analysis of ECG traces have motivated a search for alternatives. Photoplethysmography (PPG) constitutes another means of determining the timing of cardiac cycles via continuous monitoring of changes in blood volume in a portion of the peripheral microvasculature. This technique measures pulse waveforms, which in some instances may prove a practical basis for HRV analysis. We investigated the feasibility of using earlobe PPG to analyse HRV by applying the same analytic process to PPG and ECG recordings made simultaneously. Comparison of 5-minute recordings demonstrated a very high degree of correlation in the temporal and frequency domains and in nonlinear dynamic analyses between HRV measures derived from PPG and ECG. Our results confirm that PPG provides accurate interpulse intervals from which HRV measures can be accurately derived in healthy subjects under ideal conditions, suggesting this technique may prove a practical alternative to ECG for HRV analysis. This finding is of particular relevance to the care of patients suffering from peripheral hyperkinesia or tremor, which make fingertip PPG recording impractical, and following clinical interventions known to introduce electrical artefacts into the electrocardiogram.

Robust person detection in aerial images under all-weather conditions stands as a fundamental technology pivotal to the efficacy of intelligent search and rescue (SaR) tasks. However, the challenges stem from the varied postures, sparsity, diminutiveness, and faintness of personnel objects when viewed from an air-to-ground perspective, leading to issues with insufficient feature representation and suboptimal detection accuracy. This survey commences by underscoring the extensive potential applications and the prevailing limitations associated with aerial person detection (APD) within the scope of drone-assisted SaR scenarios. To meet the requirement of APD applications, we thoroughly investigate advancements and challenges in 4 related methodologies, including object-aware methods for size and perspective variability, sample-oriented methods with sparse distribution, information-fusion methods for the issue of lighting or visibility, and lightweight methods on constrained devices. Furthermore, to foster advancements in APD, we have conducted a comprehensive APD dataset labeled as “VTSaR”, which stands out from the existing publicly accessible APD datasets by offering a greater diversity of scenes, varying personnel behaviors, flexible capture angles, differing capture heights, and an inclusion of aligned visible and infrared samples along with synthetic samples. Finally, we evaluate the performance of mainstream detection methods on VTSaR benchmarks, advocating for APD’s broader application across various domains.