Development of an algorithm to discriminate between valid and false alarms in a loose-parts monitoring system

Purpose To face the challenges raised by the high incidence of falls among older adults, the intelligent video-monitoring system (IVS), a fall detection system that respects privacy, was developed. Most fall detection systems are tested only in laboratories. The purpose of this paper is to test the IVS in a simulation context (apartment-laboratory), then at home. Design/methodology/approach This study is a proof of concept including two phases: a simulation study to test the IVS in an apartment-laboratory (29 scenarios of activities including falls); and a 28-day pre-test at home with two young occupants. The IVS’s sensitivity (Se), specificity (Sp), accuracy (A) and error rate (E) in the apartment-laboratory were calculated, and functioning at home was documented in a logbook. Findings For phase 1, results are: Se =91.67 per cent, Sp =99.02 per cent, A=98.25 per cent, E=1.75. For phase 2, the IVS triggered four false alarms and some technical dysfunctions appeared (e.g. computer screen never turning off) that are easily overcome. Practical implications Results show the IVS’s efficacy at automatically detecting falls at home. Potential issues related to future installation in older adults’ homes were identified. This proof of concept led to recommendations about the installation and calibration of a camera-based fall detection system. Originality/value This paper highlights the potentialities of a camera-based fall detection system in real-world contexts and supports the use of the IVS to help older adults age in place.

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Algorithm automation for nuclear power plant Loose Parts Monitoring System

Nuclear Engineering and Design ◽

10.1016/s0029-5493(03)00232-2 ◽

2004 ◽

Author(s):

Y CHANG

Keyword(s):

Power Plant ◽

Nuclear Power Plant ◽

Monitoring System ◽

Nuclear Power ◽

Loose Parts

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The Development of Automatic Tracking Filter for Data Acquisition in Fault Diagnosis System Based on FPGA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.614.335 ◽

2014 ◽

Vol 614 ◽

pp. 335-338

Author(s):

Yong Bin Gu ◽

Zhi Nong Jiang ◽

Qi Luo

Keyword(s):

Fault Diagnosis ◽

Data Acquisition ◽

Monitoring System ◽

Rotational Speed ◽

Negative Influence ◽

Poor Quality ◽

False Alarms ◽

Automatic Tracking ◽

Tracking Filter

The running speed of rotating machinery will have a negative influence on the quality of acquired data used in fault diagnosis. Poor-quality signal may cause misinterpretation of monitoring system, and even lead to the false alarms or failure of detection. To improve the quality of the signal and enhance the accuracy of the fault monitoring system, a novel automatic tracking filter for data acquisition based on FPGA was developed. This newly developed filter can adjust to its real-time cut-off frequency relying on the detected rotational speed. Moreover, the introduction of the Ping-Pong operation realized the non-disturbance shifting of output data. The results obtained from the simulated and pragmatic experiments revealed that this filter could achieve automatic tracking for rotational speed and ameliorate the quality of sampling signal utilized in fault diagnosis.

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A MONITORING SYSTEM TO DETECT INCREASED RATES OF CANCER INCIDENCE

American Journal of Epidemiology ◽

10.1093/oxfordjournals.aje.a115773 ◽

1990 ◽

Vol 132 (supp1) ◽

pp. 123-130 ◽

Cited By ~ 8

Author(s):

RANDY R. SITTER ◽

LAWRENCE P. HANRAHAN ◽

DAVID DEMETS ◽

HENRY A. ANDERSON

Keyword(s):

Monitoring System ◽

Cancer Incidence ◽

Cancer Mortality ◽

New Method ◽

Mortality Data ◽

Monitoring Systems ◽

False Alarms ◽

Health Events ◽

Expected Time

Abstract A new method for monitoring occurrences of rare health events is proposed. This proposed method is similar in principle to the sets technique but is shown to have much better expected time to alarm properties. This is illustrated for a number of hypothetical situations, and then both methods are applied to cancer mortality data. The proposed method will allow the use of large monitoring systems consisting of implementations of the method in a number of subregions (i.e., counties) of a larger region (i.e., state) independently, and still maintain reasonable expected times between false alarms.

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An Alarm Method for a Loose Parts Monitoring System

Shock and Vibration ◽

10.1155/2012/891085 ◽

2012 ◽

Vol 19 (4) ◽

pp. 753-761 ◽

Cited By ~ 4

Author(s):

Yanlong Cao ◽

Yuanfeng He ◽

Huawen Zheng ◽

Jiangxin Yang

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Monitoring System ◽

Power Plants ◽

Detection Rate ◽

Recognition Rate ◽

Support Vector ◽

Linear Predictive Coding ◽

Loose Parts ◽

The Impact

In order to reduce the false alarm rate and missed detection rate of a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid method combining Linear Predictive Coding (LPC) and Support Vector Machine (SVM) together to discriminate the loose part signal is proposed. The alarm process is divided into two stages. The first stage is to detect the weak burst signal for reducing the missed detection rate. Signal is whitened to improve the SNR, and then the weak burst signal can be detected by checking the short-term Root Mean Square (RMS) of the whitened signal. The second stage is to identify the detected burst signal for reducing the false alarm rate. Taking the signal's LPC coefficients as its characteristics, SVM is then utilized to determine whether the signal is generated by the impact of a loose part. The experiment shows that whitening the signal in the first stage can detect a loose part burst signal even at very low SNR and thusly can significantly reduce the rate of missed detection. In the second alarm stage, the loose parts' burst signal can be distinguished from pulse disturbance by using SVM. Even when the SNR is −15 dB, the system can still achieve a 100% recognition rate

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