Detection and diagnosis of paralysis agitans

Humans’ daily behavior can reflect the main physiological characteristics of neurological diseases. Human gait is a complex behavior produced by the coordination of multiple physiological systems such as the nervous system and the muscular system. It can reflect the physiological state of human health, and its abnormality is an important basis for diagnosing some nervous system diseases. However, many early gait anomalies have not been effectively discovered because of medical costs and people's living customs. This paper proposes an effective, economical, and accurate non-contact cognitive diagnosis system to help early detection and diagnosis of paralysis agitans under daily life conditions. The proposed system extract data from wireless state information obtained from antenna-based data gathering module. Further, we implement data processing and gait classification systems to detect abnormal gait based on the acquired wireless data. In the experiment, the proposed system can detect the state of human gait and carries high classification accuracy up to 96.7 %. The experimental results demonstrate that the proposed technique is feasible and cost-effective for healthcare applications

Wearable Sensors for Evaluation Over Smart Home Using Sequential Minimization Optimization-based Random Forest

In our everyday life records, human activity identification utilizing MotionNode sensors is becoming more and more prominent. A difficult issue in ubiquitous computing and HCI is providing reliable data on human actions and behaviors. In this study, we put forward a practical methodology for incorporating statistical data into Sequential Minimization Optimization-based random forests. In order to extract useful features, we first prepared a 1-Dimensional Hadamard transform wavelet and a 1-Dimensional Local Binary Pattern-dependent extraction technique. Over two benchmark datasets, the University of Southern California-Human Activities Dataset, and the IM-Sporting Behaviors datasets, we employed sequential minimum optimization together with Random Forest to classify activities. Experimental findings demonstrate that our suggested model may successfully be utilized to identify strong human actions for matters related to efficiency and accuracy, and may challenge with existing cutting-edge approaches

Wind turbine drive-train condition monitoring through tower vibrations measurement and processing

A new method for wind turbine drive-train condition monitoring is proposed: the innovative idea is that vibrations are measured at the tower. The critical point is extracting knowledge about the drive-train from tower measurements: this is achieved by measuring simultaneously at the highest possible number of nearby wind turbines. One wind turbine is selected as target and the others are used as reference. The data are analyzed in the time domain basing on statistical features (root mean square, peak, crest factor, skewness, kurtosis). The data set in the feature space reduces to a matrix, from which the observations at the target wind turbine should be distinguishable. The application of this algorithm is supported by univariate statistical tests and by Principal Component Analysis. A novelty index based on the Mahalanobis distance is finally used to detect the statistical novelty of the damaged wind turbine. This work is based on field measurement campaigns, performed by the authors in 2018 and 2019 at wind farms owned by the Renvico company

Complementary Intermittently Nonlinear Filtering for Mitigation of Hidden Outlier Interference

When interference affecting various communication and sensor systems contains clearly identifiable outliers (e.g. an impulsive component), it can be efficiently mitigated in real time by intermittently nonlinear filters developed in our earlier work, achieving improvements in the signal quality otherwise unattainable. However, apparent amplitude outliers in the interference can disappear and reappear due to various filtering effects, including fading and multipass, as the signal propagates through media and/or the signal processing chain. In addition, the outlier structure of the interference can be obscured by strong non-outlier interfering signals, such as thermal noise and/or adjacent channel interference, or by the signal of interest itself. In this paper, we first outline the overall approach to using intermittently nonlinear filters for in-band, real-time mitigation of such interference with hidden outlier components in practical complex interference scenarios. We then introduce Complementary Intermittently Nonlinear Filters (CINFs) and focus on the particular task of mitigating the outlier noise obscured by the signal of interest itself. We describe practical implementations of such nonlinear filtering arrangements for mitigation of hidden outlier interference, in the process of analog-to-digital conversion, for wide ranges of interference powers and the rates of outlier generating events. To emphasize the effectiveness and versatility of this approach, in our examples we use particularly challenging waveforms that severely obscure low-amplitude outlier noise, such as broadband chirp signals (e.g. used in radar, sonar, and spread-spectrum communications) and ``bursty," high crest factor signals (e.g. OFDM).Comment: 9 pages, 14 figures. arXiv admin note: substantial text overlap with arXiv:1905.1047

Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers

Continuous heart monitoring is essential for early detection and diagnosis of cardiovascular diseases, which are key factors for the evaluation of health status in the general population. Therefore, in the future, it will be increasingly important to develop unobtrusive and transparent cardiac monitoring technologies for the population. The possible approaches are the development of wearable technologies or the integration of sensors in daily-life objects. We developed a smart bed for monitoring cardiorespiratory functions during the night or in the case of continuous monitoring of bedridden patients. The mattress includes three accelerometers for the estimation of the ballistocardiogram (BCG). BCG signal is generated due to the vibrational activity of the body in response to the cardiac ejection of blood. BCG is a promising technique but is usually replaced by electrocardiogram due to the difficulty involved in detecting and processing the BCG signals. In this work, we describe a new algorithm for heart parameter extraction from the BCG signal, based on a moving auto-correlation sliding-window. We tested our method on a group of volunteers with the simultaneous co-registration of electrocardiogram (ECG) using a single-lead configuration. Comparisons with ECG reference signals indicated that the algorithm performed satisfactorily. The results presented demonstrate that valuable cardiac information can be obtained from the BCG signal extracted by low cost sensors integrated in the mattress. Thus, a continuous unobtrusive heart-monitoring through a smart bed is now feasible

A Statistical Study of the Compressible Energy Cascade Rate in Solar Wind Turbulence: Parker Solar Probe Observations

We investigated the incompressible and compressible magnetohydrodynamic (MHD) energy cascade rates in the solar wind at different heliocentric distances. We used in situ magnetic field and plasma observations provided by the Parker Solar Probe (PSP) mission and exact relations in fully developed turbulence. To estimate the compressible cascade rate, we applied two recent exact relations for compressible isothermal and polytropic MHD turbulence, respectively. Our observational results show a clear increase of the compressible and incompressible cascade rates as we get closer to the Sun. Moreover, we obtained an increase in both isothermal and polytropic cascade rates with respect to the incompressible case as compressibility increases in the plasma. Further discussion about the relation between the compressibility and the heliocentric distance is carried out. Finally, we compared both exact relations as compressibility increases in the solar wind and although we note a slightly trend to observe larger cascades using a polytropic closure, we obtained essentially the same cascade rate in the range of compressibility observed.Comment: 23 pages. arXiv admin note: text overlap with arXiv:2102.1178

Native American Occupation of the Singer-Hieronymus Site Complex: Developing Site History by Integrating Remote Sensing and Archaeological Excavation

Located on a ridgetop in central Kentucky, the Singer-Hieronymus Site Complex consists of at least four Native American villages. The Native Americans who lived there are called the “Fort Ancient” by archaeologists. This study examined relationships between these villages, both spatially and temporally, to build a more complete history of site occupation. To do this, aerial imagery analysis, geophysical survey, and archaeological investigations were conducted. This research determined there were differences among villages in terms of their size, however other characteristics—internal village organization, village shape, radiometric dates, and material culture—overlapped significantly. Additionally, landscape-scale geophysical survey identified at least three potentially new villages. It has been suggested that Fort Ancient groups abandoned villages every 10 to 30 years due to environmental degradation, but these results suggest that native peoples did not abandon villages at Singer-Hieronymus. Current thought surrounding Fort Ancient village abandonment and reoccupation must therefore be reconsidered

Pupil responses to pitch deviants reflect predictability of melodic sequences

Humans automatically detect events that, in deviating from their expectations, may signal prediction failure and a need to reorient behaviour. The pupil dilation response (PDR) to violations has been associated with subcortical signals of arousal and prediction resetting. However, it is unclear how the context in which a deviant occurs affects the size of the PDR. Using ecological musical stimuli that we characterised using a computational model, we showed that the PDR to pitch deviants is sensitive to contextual uncertainty (quantified as entropy), whereby the PDR was greater in low than high entropy contexts. The PDR was also positively correlated with unexpectedness of notes. No effects of music expertise were found, suggesting a ceiling effect due to enculturation. These results show that the same sudden environmental change can lead to differing arousal levels depending on contextual factors, providing evidence for a sensitivity of the PDR to long-term context