2012
DOI: 10.1007/978-3-642-29347-4_31
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Fuzzy Inference-Based Reliable Fall Detection Using Kinect and Accelerometer

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Cited by 33 publications
(10 citation statements)
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“…In [27], Nyan et al describe an architecture that employs the signals of a gyroscope to monitor the sagittal plane of the body, while the user is simultaneously monitored by a video-camera. Following a similar approach, Kepski et al present a fuzzy system for fall detection in [28], grounded on the joint analysis of the signals captured by a Kinect device, an accelerometer and a gyroscope. In spite of the small-but not negligible-number of papers that have addressed the use of the gyroscope in FDSs, the possible incremental benefits of introducing this element (or other sensors) in the detection process (when compared to the case in which only the accelerometer is contemplated) have been discussed in only a few studies.…”
Section: Related Workmentioning
confidence: 99%
“…In [27], Nyan et al describe an architecture that employs the signals of a gyroscope to monitor the sagittal plane of the body, while the user is simultaneously monitored by a video-camera. Following a similar approach, Kepski et al present a fuzzy system for fall detection in [28], grounded on the joint analysis of the signals captured by a Kinect device, an accelerometer and a gyroscope. In spite of the small-but not negligible-number of papers that have addressed the use of the gyroscope in FDSs, the possible incremental benefits of introducing this element (or other sensors) in the detection process (when compared to the case in which only the accelerometer is contemplated) have been discussed in only a few studies.…”
Section: Related Workmentioning
confidence: 99%
“…Fuzzy inference is a process of obtaining new knowledge through existing knowledge using fuzzy logic [18]- [20]. The fuzzy rules are applied are as follows [21] shown in Figure 4.…”
Section: Fuzzy Inference Of Each Dna Profile Locusmentioning
confidence: 99%
“…Mubashir et al [5] divided the approaches for fall detection in three categories: wearable device-based, ambience sensor based, and vision based. There are few currently publicly available datasets for fall detection based in sensors [7,[9][10][11][12], vision based [13,14], and multimodal datasets [15][16][17]. In this section, we present an overview of datasets based in sensors or multimodal approaches.…”
Section: Fall Detection Databases Overviewmentioning
confidence: 99%