Falls are one of the most serious health risk issues facing the elderly worldwide, which have a serious impact on the physical and mental health and quality of life of the elderly. Fall risk prediction can help develop targeted fall prevention programs that can help reduce the incidence of falls in the elderly. This paper proposed a fall risk prediction method based on electrostatic gait signals, measured the electrostatic gait signals of three types of people with different fall risks, and extracted gait time parameters, gait symmetry features, and gait variability features. The dimensionality of the dataset was reduced through the hybrid feature dimensionality reduction method based on the particle swarm optimization algorithm, and a fall risk prediction model was constructed based on the SVM algorithm, with the model accuracy reaching 96.77%. Methods of this paper have the advantages of simple equipment layout and non-invasive measurement, and can effectively predict the risk of falls, reduce the incidence of falls in the elderly, and improve the survival rate and quality of life of the elderly.
KEYWORDS: Signal to noise ratio, Modulation, Time-frequency analysis, Radar signal processing, Education and training, Interference (communication), Continuous wavelet transforms, Signal analyzers, Frequency modulation, Windows
With the rapid advancement of information technology, the modern battlefield is characterized by a highly complex electromagnetic environment. Radar radiation sources exhibit wide-ranging parameter variations and strong random characteristics, presenting formidable challenges to the signal selection of radar radiation sources in missile-borne countermeasure systems. This paper addresses the issue of reliable identification and selection of radar source signals by on-board countermeasures systems. Through the analysis of source signal characteristics, the Smooth Pseudo Wigner-Ville Distribution (SPWVD) method is employed for time-frequency analysis to extract the time-frequency features of the source signals. Furthermore, a lightweight network based on SqueezeNet is implemented to achieve high-precision source signal selection. The results demonstrate that, when the SNR of the source signals is greater than 0dB, the network model achieves a recognition accuracy above 94.59%. The selection accuracy is comparable to that of the Convolutional Neural Network (CNN), thereby meeting the requirements of on-board countermeasure systems for reliable selection of radar source signals. The analysis confirms that under low signal-to-noise ratio conditions, noise significantly affects the network's selection accuracy by impacting the time-frequency clarity of the modulation signals.
Gait measurement is an objective analysis method that can detect abnormal gait, facilitate early disease identification, and support doctors in formulating rehabilitation treatment plans. In recent years, cognitive scales and biomarkers have been widely used to detect Parkinson's cognitive impairment (PD-MCI). However, a lack of objective and reliable detection methods for PD-MCI exists. This study aims to identify patients with Parkinson's cognitive impairment utilizing two approaches: gait and executive function. Non-contact measurement was employed to obtain the gait signal, while the clock drawing test was utilized to obtain the executive function score of the patients. Ten PD-MCI patients and 10 ordinary PD were tested, and the kinematics data of the subjects during walking were recorded. Using patients' gait parameters and executive function scores as data characteristics, machine learning methods were utilized to classify and identify patients. The most suitable machine learning method for PD-MCI patient detection was discovered utilizing this approach. The results indicate that the XGBoost algorithm can identify and classify PD-MCI patients with more than 90% accuracy, providing effective support for doctors in identifying Parkinson's cognitive impairment early and customizing the most suitable rehabilitation treatment plan. These findings have significant research significance.
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