A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features

Abstract

In the human-robot hybrid system, due to the error recognition of the pattern recognition system, the robot may perform erroneous motor execution, which may lead to falling-risk. While, the human can clearly detect the existence of errors, which is manifested in the central nervous activity characteristics. To date, the majority of studies on falling-risk detection have focused primarily on computer vision and physical signals. There are no reports of falling-risk detection methods based on neural activity. In this study, we propose a novel method to monitor multi erroneous motion events using electroencephalogram (EEG) features. There were 15 subjects who participated in this study, who kept standing with an upper limb supported posture and received an unpredictable postural perturbation. EEG signal analysis revealed a high negative peak with a maximum averaged amplitude of −14.75 ± 5.99 μV, occurring at 62 ms after postural perturbation. The xDAWN algorithm was used to reduce the high-dimension of EEG signal features. And, Bayesian linear discriminant analysis (BLDA) was used to train a classifier. The detection rate of the falling-risk onset is 98.67%. And the detection latency is 334ms, when we set detection rate beyond 90% as the standard of dangerous event onset. Further analysis showed that the falling-risk detection method based on postural perturbation evoked potential features has a good generalization ability. The model based on typical event data achieved 94.2% detection rate for unlearned atypical perturbation events. This study demonstrated the feasibility of using neural response to detect dangerous fall events.

Dental Shade Matching Method Based on Hue, Saturation, Value Color Model with Machine Learning and Fuzzy Decision

“…Accordingly, the substep pertaining to the format is the conversion of the RGB image to the HSV format, which is advantageous since it simplifies the colorimetric analysis. Equations (1)- (3) show the conversion from the RGB format to the HSV format.…”

“…The techniques of big data analysis and machine learning are used widely in medicine [detecting pneumonia, (1) autism, (2) and risk of falling, (3) the classification of cancers, (4) and the analysis of cell pseudo-color images (5) ]. A method of dental image analysis is developed in this study.…”