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      Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network

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          Abstract

          An infrared ceiling sensor network system is reported in this study to realize behavior analysis and fall detection of a single person in the home environment. The sensors output multiple binary sequences from which we know the existence/non-existence of persons under the sensors. The short duration averages of the binary responses are shown to be able to be regarded as pixel values of a top-view camera, but more advantageous in the sense of preserving privacy. Using the “pixel values” as features, support vector machine classifiers succeeded in recognizing eight activities (walking, reading, etc.) performed by five subjects at an average recognition rate of 80.65%. In addition, we proposed a martingale framework for detecting falls in this system. The experimental results showed that we attained the best performance of 95.14% ( F 1 value), the FAR of 7.5% and the FRR of 2.0%. This accuracy is not sufficient in general but surprisingly high with such low-level information. In summary, it is shown that this system has the potential to be used in the home environment to provide personalized services and to detect abnormalities of elders who live alone.

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          Most cited references53

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          A survey on vision-based human action recognition

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            Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.

            Using simulated falls performed under supervised conditions and activities of daily living (ADL) performed by elderly subjects, the ability to discriminate between falls and ADL was investigated using tri-axial accelerometer sensors, mounted on the trunk and thigh. Data analysis was performed using MATLAB to determine the peak accelerations recorded during eight different types of falls. These included; forward falls, backward falls and lateral falls left and right, performed with legs straight and flexed. Falls detection algorithms were devised using thresholding techniques. Falls could be distinguished from ADL for a total data set from 480 movements. This was accomplished using a single threshold determined by the fall-event data-set, applied to the resultant-magnitude acceleration signal from a tri-axial accelerometer located at the trunk.
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              A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor.

              A threshold-based algorithm, to distinguish between Activities of Daily Living (ADL) and falls is described. A gyroscope based fall-detection sensor array is used. Using simulated-falls performed by young volunteers under supervised conditions onto crash mats and ADL performed by elderly subjects, the ability to discriminate between falls and ADL was achieved using a bi-axial gyroscope sensor mounted on the trunk, measuring pitch and roll angular velocities, and a threshold-based algorithm. Data analysis was performed using Matlab to determine the angular accelerations, angular velocities and changes in trunk angle recorded, during eight different fall and ADL types. Three thresholds were identified so that a fall could be distinguished from an ADL: if the resultant angular velocity is greater than 3.1 rads/s (Fall Threshold 1), the resultant angular acceleration is greater than 0.05 rads/s(2) (Fall Threshold 2), and the resultant change in trunk-angle is greater than 0.59 rad (Fall Threshold 3), a fall is detected. Results show that falls can be distinguished from ADL with 100% accuracy, for a total data set of 480 movements.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                December 2012
                07 December 2012
                : 12
                : 12
                : 16920-16936
                Affiliations
                Division of Computer Science, Hokkaido University, Kita 8 Nishi 5, Kita-ku, Sapporo 060-0808, Japan; E-Mails: mine@ 123456main.ist.hokudai.ac.jp (M.K.); nonaka@ 123456main.ist.hokudai.ac.jp (H.N.)
                Author notes
                [* ] Author to whom correspondence should be addressed; E-Mail: taoshuai@ 123456main.ist.hokudai.ac.jp ; Tel.: +81-011-706-6854.
                Article
                sensors-12-16920
                10.3390/s121216920
                3571818
                23223150
                144cbc2a-4034-4507-a9d1-d1fdd13aff76
                © 2012 by the authors; licensee MDPI, Basel, Switzerland

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 30 September 2012
                : 19 November 2012
                : 03 December 2012
                Categories
                Article

                Biomedical engineering
                behavior analysis,fall detection,privacy-preserved,ceiling sensor network,infrared sensors

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