Fuzzy Inference-Based Reliable Fall Detection Using Kinect and Accelerometer

Kępski, Michał; Kwolek, Bogdan; Austvoll, Ivar

doi:10.1007/978-3-642-29347-4_31

Cited by 31 publications

(10 citation statements)

References 9 publications

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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%

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

2020

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Due to the serious impact of falls on the quality of life of the elderly and on the economical sustainability of health systems, the study of new monitoring systems capable of automatically alerting about falls has gained much research interest during the last decade. In the field of Human Activity Recognition, Fall Detection Systems (FDSs) can be contemplated as pattern recognition architectures able to discriminate falls from ordinary Activities of Daily Living (ADLs). In this regard, the combined application of cellular communications and wearable devices that integrate inertial sensors offers a cost-efficient solution to track the user mobility almost ubiquitously. Inertial Measurement Units (IMUs) typically utilized for these architectures, embed an accelerometer and a gyroscope. This paper investigates if the use of the angular velocity (captured by the gyroscope) as an input feature of the movement classifier introduces any benefit with respect to the most common case in which the classification decision is uniquely based on the accelerometry signals. For this purpose, the work assesses the performance of a deep learning architecture (a convolutional neural network) which is optimized to differentiate falls from ADLs as a function of the raw data measured by the two inertial sensors (gyroscope and accelerometer). The system is evaluated against on a well-known public dataset with a high number of mobility traces (falls and ADL) measured from the movements of a wide group of experimental users.

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Section: Related Workmentioning

confidence: 99%

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

2020

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“…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%

Family relation and STR-DNA matching using fuzzy inference

Anggreainy¹,

Widyanto²,

Widjaja³

et al. 2019

IJECE

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Deoxyribose Nucleic Acid (DNA) are the basic elements that make up a whole section of an individual. The basic elements store information that is unique to each individual and will be passed down the generations. DNA also helps in identifying the father in paternity testing, locating missing person investigations, identifying victims in mass disasters. Identiﬁcation of the victims has a problem if the comparison the father and mother no instance the victim’s parents have died or are very far away from where the victim. Therefore, it is necessary to try to identify Short Tandem Repeat (STR) Inference of live family such as sibling, grandfather/grandmother, uncle/aunt, cousin and nephew. In this paper, we performed a method to measure the similarity of human DNA proﬁles using fuzzy similarity. In this fuzzy system, DNA profile data is used as an input that stores human identity along with its DNA profile. The data entered is the result of polymerase chain reaction (PCR) identification which is an electropherogram consisting of 16 loci with two alleles for each locus. Output in this fuzzy system is the value of individual similarity with reference and with similarity levels, namely small, medium and high.

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“…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%

Multimodal Database for Human Activity Recognition and Fall Detection

2018

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Fall detection can improve the security and safety of older people and alert when fall occurs. Fall detection systems are mainly based on wearable sensors, ambient sensors, and vision. Each method has commonly known advantages and limitations. Multimodal and data fusion approaches present a combination of data sources in order to better describe falls. Publicly available multimodal datasets are needed to allow comparison between systems, algorithms and modal combinations. To address this issue, we present a publicly available dataset for fall detection considering Inertial Measurement Units (IMUs), ambient infrared presence/absence sensors, and an electroencephalogram Helmet. It will allow human activity recognition researchers to do experiments considering different combination of sensors.

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Fuzzy Inference-Based Reliable Fall Detection Using Kinect and Accelerometer

Cited by 31 publications

References 9 publications

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

Family relation and STR-DNA matching using fuzzy inference

Multimodal Database for Human Activity Recognition and Fall Detection

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