Wearable Cardiopulmonary Function Evaluation System for Six-Minute Walking Test.

As a submaximal exercise test, a 6-min walking test (6MWT) can be considered a suitable index for the exercise capacity of patients with a respiratory problem. Traditionally, medical staff manually collect cardiopulmonary information using different devices. However, no integrated monitoring system is currently available to simultaneously record the real-time breathing sound, heart rhythm, and precise walking information (i.e., walking distance, speed, and acceleration) during the 6MWT. In this study, a wearable and wireless multiparameter monitoring system is proposed to simultaneously monitor the breathing sound, oxygen saturation (SpO2), electrocardiograph (ECG) signals, and precise walking information during the 6MWT. Here, a wearable mechanical design was successfully used to reduce the effect of motion artifacts on the breathing sound and ECG signal. A multiparameter detection algorithm was designed to effectively estimate heart and breathing rates. Finally, the cardiopulmonary function of smokers was evaluated using the proposed system. The evaluation indicated that this system could reveal dynamic changes and differences in the breathing rate, heart rate, SpO2, walking speed, and acceleration during the 6MWT. The proposed system can serve as a more integrated approach to monitor cardiopulmonary parameters and obtain precise walking information simultaneously during the 6MWT.

Tracing recent outdoor geolocation by analyzing microbiota from shoe soles and shoeprints even after indoor walking.

The microbial communities on shoe soles and shoeprints could carry microbial information about where someone walked. This is possible evidence to link a suspect in a crime case to a geographic location. A previous study had shown that the microbiota found on shoe soles depend on the microbiota of the soil on which people walk. However, there is a turnover of microbial communities on shoe soles during walking. The impact of microbial community turnover on tracing recent geolocation from shoe soles has not been adequately studied. In addition, it is still unclear whether the microbiota of shoeprints can be used to determine recent geolocation. In this preliminary study, we investigated whether the microbial characteristics of shoe soles and shoeprints can be used to trace geolocation and whether this information can be destroyed by walking on indoor floors. In this study, participants were asked to walk outdoors on exposed soil, then walk indoors on a hard wood floor. High-throughput sequencing of the 16S rRNA gene was performed to characterize the microbial communities of shoe soles, shoeprints, indoor dust, and outdoor soil. Samples of shoe soles and shoeprints were collected at steps 5, 20, and 50 while walking indoors. The PCoA result showed that the samples were clustered by geographic origin. The shoeprint showed a more rapid turnover of microbial community than the shoe sole during indoor walking. The result of FEAST showed that the microbial communities of shoe sole and shoeprint were mainly (shoe sole, 86.21∼92.34 %; shoeprint, 61.66∼90.41 %) from the soil of the outdoor ground where the individual recently walked, and a small portion (shoe sole, 0.68∼3.33 %; shoeprint, 1.43∼27.14 %) from the indoor dust. Based on the matching of microbial communities between geolocation and shoe sole or shoeprint, we were able to infer the recent geolocation of the individual with relatively high accuracy using the random forest prediction model (shoe sole: 100.00 %, shoeprint: 93.33∼100.00 %). Overall, we are able to accurately infer the geolocation of an individual's most recent outdoor walk based on the microbiota of shoe sole and shoeprint, even though these microbiotas show a turnover when walking indoor floor. The pilot study was expected to provide a potential method for tracing recent geolocation of suspects.