Optimal threshold selection for threshold-based fall detection algorithms with multiple features

Razum, D.; Šeketa, Goran; Vugrin, Jurica; Lacković, Igor

doi:10.23919/mipro.2018.8400272

Cited by 18 publications

(6 citation statements)

References 8 publications

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“…Due to the low computational complexity, fixed thresholdbased method has been widely applied by current fall detection and fall prevention studies. For example, Razum et al [140] selected optimal threshold values for two features of sum vector magnitude and euler angle to distinguish falls from ADLs based on the receiver operating characteristics (ROC) curve, which has been commonly applied in former studies. Two optimal thresholds were selected and analyzed for three situations of each feature separately and the combination of two features, respectively.…”

Section: ) Threshold-based Fall Detection and Fall Preventionmentioning

confidence: 99%

Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

2019

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Falls are abnormal activity events that occur infrequently; however, they are serious health problems among elderly individuals. With the advancements of technologies, falls have been widely studied by scientific researchers to minimize serious consequences and negative impacts. Fall detection and fall prevention are two strategies to tackle fall issues with a variety of sensing techniques and classifier models. Currently, many reviews on fall-related technologies have been presented and analyzed; however, most of them give surveys on the subfield of fall-related systems, while others are not extensive and comprehensive reviews. In fact, the latest researches have a new trend of fusion-based methods to improve the performance of the fall-related systems based on a combination of different sensors or classifier models. Adaptive threshold and radio frequency-based systems are also researched and proposed recently, which are seldom mentioned in other reviews. Therefore, a global taxonomy for current fall-related studies from four aspects, including current literature reviews, fall detection, and prevention systems based on different sensor apparatus and analytic algorithm, low power techniques, and sensor placements for fall-related systems are conducted in this paper. Several research challenges and issues in the fall-related field are also discussed and analyzed. The objective of this review paper is to conclude and provide a good position of current fall-related studies to inspire researchers in this field. INDEX TERMSAdaptive algorithm, classification algorithms, fall detection, fall prevention, low power techniques, sensing techniques. LINGMEI REN received the Ph.D. degree from the

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Section: ) Threshold-based Fall Detection and Fall Preventionmentioning

confidence: 99%

Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

2019

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“…To differentiate these 11 movements, the study utilized a threshold method based on acceleration, angle, and angular acceleration [14]. This method involves analyzing the total acceleration, total orientation values of normal motion or activity of daily living (ADL) and determining the maximum value as the threshold [15], [16]. A formula is used to calculate the acceleration relative (AR) to gravity because the orientation of falling motion is always downward, regardless of whether the fall is forward, backward, or to the side.…”

Section: Methodsmentioning

confidence: 99%

Design and build an airbag system for elderly fall protection using the MPU6050 sensor module

Suprapto,

Kusuma,

Firdaus

et al. 2024

IJRES

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The use of technology has a significant impact to reduce the consequences of accidents. Sensors, small components that detect interactions experienced by various components, play a crucial role in this regard. This study focuses on how the MPU6050 sensor module can be used to detect the movement of people who are falling, defined as the inability of the lower body, including the hips and feet, to support the body effectively. An airbag system is proposed to reduce the impact of a fall. The data processing method in this study involves the use of a threshold value to identify falling motion. The results of the study have identified a threshold value for falling motion, including an acceleration relative (AR) value of less than or equal to 0.38 g, an angle slope of more than or equal to 40 degrees, and an angular velocity of more than or equal to 30 °/s. The airbag system is designed to inflate faster than the time of impact, with a gas flow rate of 0.04876 m3 /s and an inflating time of 0.05 s. The overall system has a specificity value of 100%, a sensitivity of 85%, and an accuracy of 94%.

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“…2) UniZg activ2 dataset: The second dataset used in this study for classification of activity recognition methods was recorded by the Faculty of Electrical Engineering and Computing, University of Zagreb by Razum, Šeketa, Vugrin and Lacković [3]. The activities were recorded using Shimmer3 inertial measurement unit (IMU), using its built-in triaxial wide range accelerometer with a range of +/-8g, triaxial magnetometer and triaxial gyroscope sensors with a sampling frequency f s of 204.8 Hz [4].…”

Section: Methodsmentioning

confidence: 99%

Physical Activity Recognition Based on Machine Learning

Jurčić¹,

Magjarević²

2022

Proceedings of the 29th Minisymposium

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The following paper presents a comparison study of various machine learning techniques in recognition of activities of daily living (ADL), with special attention being given to movements during human falling and the distinction among various types of falls. The motivation for the development of physical activity recognition algorithm includes keeping track of users' activities in real-time, and possible diagnostics of unwanted and unexpected movements and/or events. The activities recorded and processed in this study include various types of daily activities, such as walking, running, etc., while fall activities include falling forward, falling backward, falling left and right (front fall, back fall and side fall). The algorithm was trained on two publicly available datasets containing signals from an accelerometer, a magnetometer and a gyroscope.

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Optimal threshold selection for threshold-based fall detection algorithms with multiple features

Cited by 18 publications

References 8 publications

Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

Design and build an airbag system for elderly fall protection using the MPU6050 sensor module

Physical Activity Recognition Based on Machine Learning

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