Lateral Inhibition in Accumulative Computation and Fuzzy Sets for Human Fall Pattern Recognition in Colour and Infrared Imagery

Fernández-Caballero, Antonio; Sokolova, Marina V.; Serrano-Cuerda, Juan

doi:10.1155/2013/935026

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Finally, they transformed a fuzzy output set into a crisp value in the defuzzification phase. The authors in [42] used FL to identify the range and type of fall, which can include the position before fall, fall direction, fall velocity, and post-fall inactivity. The authors in [43] looked beyond the traditional threshold-based approaches and implemented a fuzzy inference technique for precise decision making.…”

Section: B Fuzzy Logic-based Fall Detectionmentioning

confidence: 99%

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

Kchouri¹,

Harum²,

Obeid³

et al. 2022

IJACSA

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Falling is a major health issue that can lead to both physical and mental injuries. Detecting falls accurately can reduce the severe effects and improve the quality of life for disabled people. Therefore, it is critical to develop a smart fall detection system. Many approaches have been proposed in wearable-based systems. In these approaches, machine learning techniques have been conducted to provide automatic classification and to improve accuracy. One of the most commonly used algorithms is Support Vector Machine (SVM). However, classical SVM can neither use prior knowledge to process accurate classifications nor solve problems characterized by ambiguity. More specifically, some values of falls are inaccurate and similar to the features of normal activities, which can also greatly impact the performance of the learning ability of SVMs. Hence, it became necessary to look for an effective fall detection method based on a combination of Fuzzy Logic (FL) and SVM algorithms so as to reduce false positive alarms and improve accuracy. In this paper, various training data are assigned to the corresponding membership degrees. Some data points with a high chance of falling are assigned a high degree of membership, yielding a high contribution for SVM decisionmaking. This does not only achieve accurate fall detection, but also reduces the hesitation in labeling the outcomes and improves the heuristic transparency of the SVM. The experimental results achieved 100% specificity and precision, with an overall accuracy of 99.96%. Consequently, the experiment proved to be effective and yielded better results than the conventional approaches.

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Section: B Fuzzy Logic-based Fall Detectionmentioning

confidence: 99%

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

Kchouri¹,

Harum²,

Obeid³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…However, the radar method has the following limitations and challenges: (i) it responds to any movement from other humans or non-human sources; (ii) the radar is exposed to jitter, which generates time-dependent additional noise and false person tracking; (iii) the detected person must be within the position of the antenna beam width; (iv) the reflected signal caused by the target is blocked by furniture; and (v) the person detection distance might represent a restriction, where the reflected signal is weak when the distance is increased [56]. A novel approach was presented in Reference [57]. It used fuzzy logic to identify the range and type of elderly fall, which includes position before fall, fall direction, fall velocity, and post-fall inactivity.…”

Section: Related Workmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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Falls are the main source of injury for elderly patients with epilepsy and Parkinson's disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.Energies 2018, 11, 2866 2 of 32 to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People's rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people's falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed ...

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IoT-based hybrid optimized fuzzy threshold ELM model for localization of elderly persons

Ghorpade

Zennaro

Chaudhari

2021

Expert Systems with Applications

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Lateral Inhibition in Accumulative Computation and Fuzzy Sets for Human Fall Pattern Recognition in Colour and Infrared Imagery

Cited by 5 publications

References 26 publications

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

IoT-based hybrid optimized fuzzy threshold ELM model for localization of elderly persons

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