Automatic segmentation of MR depicted carotid arterial boundary based on local priors and constrained global optimisation

Zhang, Jianhua; Zhang, Teng; Qin, Guan; He, Junli; Abutaleb, Wafa; Patterson, Andrew J.; Graves, Martin J.; Gillard, Jonathan H.; Chen, Shengyong

doi:10.1049/iet-ipr.2018.5330

Cited by 5 publications

(1 citation statement)

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“…It can be seen from the above processing that the direct trapper adaptively filters abnormal behavior signals in dynamic sensing data flow, which can minimize the root mean square error of output signals. 30 Anti-interference ability of isolation segmentation and detection for signal regions is improved. Filtering design structure of abnormal behavior signals in the dynamic sensing data flow is shown in Figure 4.…”

Section: Interference Component Filtering Of Abnormal Behavior Signal...mentioning

confidence: 99%

The security of vulnerable senior citizens through dynamically sensed signal acquisition

Wang

Srivastava

2020

Trans Emerging Tel Tech

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Traditional signal recognition methods generally use biosensors for signal acquisition. With senior citizens, sensor signal acquisition will be affected by their movements. These signal fluctuations are large, and if the signal area cannot be fixed, it may result in problems such as data loss. The most important issue caused by data loss is the safety for vulnerable seniors. Therefore, here we study abnormal behavior recognition based on dynamic sensing. In this paper, we look to improve the problems that exist in traditional methods. Using the SW-520D sensor, activity signals of the elderly are first collected. By comparing the received signal strength sets, dynamic sensor data flow of the abnormal behavior for senior citizens can be determined. A multiple linear regression estimation method is used to solve the problem of data loss in dynamic sensor data flow environments. We obtain system parameter thresholds in both area isolation and segmentation using the stochastic resonance method. From this, a direct notch is constructed that enters the dynamic sensor data stream, and the interference component filtering of abnormal behavior signals is processed. The amplitude-frequency response feature extraction method is used for high-precision isolation and segmentation of abnormal behavior signal areas such as falls, improving the accuracy of senior behavior signal recognition, and realizing safety monitoring for the elderly. The improved method was used to identify the signals of abnormal behaviors of young people. The minimum recognition error rate was only 2%, the recognition accuracy rate was as high as 98%, and the calculation time was only 19 ms.

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Section: Interference Component Filtering Of Abnormal Behavior Signal...mentioning

confidence: 99%