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Background Heart rate variability (HRV) has been investigated previously in autonomic nervous system-related clinical settings. In these settings, HRV is determined by the time-series heartbeat peak-to-peak intervals using electrocardiography (ECG). To reduce patient discomfort, we designed a Doppler radar-based autonomic nervous activity monitoring system (ANMS) that allows cardiopulmonary monitoring without using ECG electrodes or spirometry monitoring. Case summary Using our non-contact ANMS, we observed a bedridden 80-year-old female patient with terminal phase sepsis developed the daytime Cheyne-Stokes respiration (CSR) associated with the attenuation of the low frequency (LF) and high frequency (HF) of HRV components 20 days prior to her death. The patient developed a marked linear decrease in the LF and the HF of HRV components for over 3 days in a row. Furthermore, after the decrease both the LF and the HF showed low and linear values. Around the intersection of the two lines, the decreasing LF and HF lines and the constant LF and HF lines, the ANMS automatically detected the daytime CSR pathogenesis. The attenuation rate of HF (1340 ms2/day) was higher than that of LF (956 ms2/day). Heart rate increased by ∼10 b.p.m. during these 3 days. Discussion We detected CSR-associated LF and HF attenuation in a patient with terminal phase sepsis using our ANMS. The proposed system without lead appears promising for future applications in clinical settings, such as remote cardiac monitoring of patients with heart failure at home or in long-term acute care facilities.

Downwind turbines have favorable characteristics such as effective energy capture in up-flow wind conditions over complex terrains. They also have reduced risk of severe accidents in the event of disruptions to electrical networks during strong storms due to the free-yaw effect of downwind turbines. These favorable characteristics have been confirmed by wind-towing tank experiments and computational fluid dynamics (CFD) simulations. However, these advantages have not been fully demonstrated in field experiments on actual wind farms. In this study—although the final objective was to demonstrate the potential advantages of downwind turbines through field experiments—field measurements were performed using a vertical-profiling light detection and ranging (LiDAR) system on a wind farm with downwind turbines installed in complex terrains. To deduce the horizontal wind speed, vertical-profiling LiDARs assume that the flow of air is uniform in space and time. However, in complex terrains and/or in wind farms where terrain and/or wind turbines cause flow distortion or disturbances in time and space, this assumption is not valid, resulting in erroneous wind speed estimates. The magnitude of this error was evaluated by comparing LiDAR measurements with those obtained using a cup anemometer mounted on a meteorological mast and detailed analysis of line-of-sight wind speeds. A factor that expresses the nonuniformity of wind speed in the horizontal measurement plane of vertical-profiling LiDAR is proposed to estimate the errors in wind speed. The possibility of measuring and evaluating various wind characteristics such as flow inclination angles, turbulence intensities, wind shear and wind veer, which are important for wind turbine design and for wind farm operation is demonstrated. However, additional evidence of actual field measurements on wind farms in areas with complex terrains is required in order to obtain more universal and objective evaluations.