Human fall detection based on event pattern matching with ultrasonic array sensors

Chang, Yuan-Tsung; Shih, Timothy K.

doi:10.1109/umedia.2017.8074149

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…This method also utilized the speed and level of falling in each sensor so that it was not matched with complicated fall patterns. Chang and Shih [ 68 ] proposed human fall detection based on event pattern matching with ultrasonic array sensors. This method may be used to detect many human fall patterns.…”

Section: Related Workmentioning

confidence: 99%

An Ultrasonic-Based Sensor System for Elderly Fall Monitoring in a Smart Room

Nadee

Chamnongthai

2022

Journal of Healthcare Engineering

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To reduce the risk of elderly people falling in a private room without relying on a closed-circuit television system that results in serious privacy and trust concerns, a fall monitoring system that protects the privacy and does not monitor a person’s activities is needed. An ultrasonic-based sensor system for elderly fall monitoring in a smart room is proposed in this study. An array of ultrasonic sensors, whose ranges are designed to cover the room space, are initially installed on a wall of the room, and the sensors are rotated to transmit and receive ultrasonic signals to measure the distances to a moving object while preventing ultrasonic signal interference. Distance changes measured by ultrasonic sensors are used as time-independent patterns to recognize when an elderly person falls. To evaluate the performance of the proposed system, a sensor system prototype using long short-term memory was constructed, and experiments with 25 participants were performed. An accuracy of approximately 98% was achieved in this experiment using the proposed method, which was a slight improvement over that of the conventional method.

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Section: Related Workmentioning

confidence: 99%

An Ultrasonic-Based Sensor System for Elderly Fall Monitoring in a Smart Room

Nadee

Chamnongthai

2022

Journal of Healthcare Engineering

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“…In the existing research, fall detection was carried out through several ultrasonic sensors and event pattern matching. Although it is preferred that they are utilized in bathrooms and toilets because the sensor does not need to be directly attached to the body, and there are fewer concerns about privacy invasion of an individual compared with cameras, it has the disadvantage of increased misclassification probability in the presence of other moving objects, such as pets or robotic vacuums, as the sensor is installed at an altitude close to the floor [15].…”

Section: Ultrasonic-basedmentioning

confidence: 99%

S3D: Squeeze and Excitation 3D Convolutional Neural Networks for a Fall Detection System

et al. 2022

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Because of the limitations of previous studies on a fall detection system (FDS) based on wearable and ambient devices and visible light and depth cameras, the research using thermal cameras has recently been conducted. However, they also have the problem of deteriorating the accuracy of FDS depending on various environmental changes. Given these facts, in this study, we newly propose an FDS method based on the squeeze and excitation (SE) 3D convolutional neural networks (S3D). In our method, keyframes are extracted from input thermal videos using the optical flow vectors, and the fall detection is carried out based on the output of the proposed S3D, using the extracted keyframes as input. Comparative experiments were carried out on three open databases of thermal videos with different image resolutions, and our proposed method obtained F1 scores of 97.14%, 95.30%, and 98.89% in the Thermal Simulated Fall, Telerobotics and Control Lab fall detection, and eHomeSeniors datasets, respectively (the F1 score is a harmonic mean of recall and precision; it was confirmed that these are superior results to those obtained using the state-of-the-art methods of a thermal camera-based FDS.

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“…It is well functional under low-light environments and free of privacy concerns [40]. In [41], an array of six ultrasonic sensors is used to monitor the posture of the person in a fall event. The sensors radiate an eight-pulse signal waveform at 40 Hz.…”

Section: Systems Based On Ambient Sensor Systemsmentioning

confidence: 99%

Possible Life Saver: A Review on Human Fall Detection Technology

et al. 2020

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Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

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Human fall detection based on event pattern matching with ultrasonic array sensors

Cited by 8 publications

References 13 publications

An Ultrasonic-Based Sensor System for Elderly Fall Monitoring in a Smart Room

An Ultrasonic-Based Sensor System for Elderly Fall Monitoring in a Smart Room

S3D: Squeeze and Excitation 3D Convolutional Neural Networks for a Fall Detection System

Possible Life Saver: A Review on Human Fall Detection Technology

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