Ultrasonic array sensors for monitoring of human fall detection

Nadee, Chokemongkol; Chamnongthai, Kosin

doi:10.1109/ecticon.2015.7207097

Cited by 27 publications

(13 citation statements)

References 8 publications

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“…They use the phase and amplitude from the Channel State Information (CSI) to detect falls. [6] places ultrasonic sensors on top and wall of room. It detects falls by comparing the signal between side and top signals with the threshold signals predefined.…”

Section: B Ambient Sensor Based Methodsmentioning

confidence: 99%

“…They use cameras to capture images or videos of the people in a fixed scene, and through processing these images and videos they can detect falls [4]. The ambient sensor based methods collect information of the surroundings such as Wi-Fi [5], ultrasonic [6], radar [7], infrared ray [8]. Through analyzing signals from ambient sensors, they give detection results of falls.…”

Section: Introductionmentioning

confidence: 99%

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A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning

Chen

et al. 2020

Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility

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Falls are one of the important causes of accidental or unintentional injury death worldwide. Therefore, this paper presents a reliable fall detection algorithm and a mobile cloud collaboration system for fall detection. The algorithm is an ensemble learning method based on decision tree, named Falldetection Ensemble Decision Tree (FEDT). The mobile cloud collaboration system can be divided into three stages: 1) mobile stage: use a light-weighted threshold method to filter out the activities of daily livings (ADLs), 2) collaboration stage: transmit data to cloud and meanwhile extract features in the cloud, 3) cloud stage: deploy the model trained by FEDT to give the final detection result with the extracted features.Experiments show that the performance of the proposed FEDT outperforms the others' over 1-3% both on sensitivity and specificity, and more importantly, the system can provide reliable fall detection in practical scenario.

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Section: B Ambient Sensor Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning

Chen

et al. 2020

Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility

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“…In smart homes, ultrasonic sensors are used either as part of their intelligent monitoring system, or as a standalone monitoring system. Ultrasonic sensors can be mounted on ceiling and/or wall [8]. [9] claims to give an accurate 3D tracking of human activities in a living area using mathematical models to predict the position.…”

Section: Ultrasonic Sensors In Smart Homesmentioning

confidence: 99%

Tracking a Person’s Behaviour in a Smart House

Chand

Ali

Barmada

et al. 2019

Lecture Notes in Computer Science

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This paper proposes to use machine learning techniques with ultrasonic sensors to predict the behavior and status of a person when they live solely inside their house. The proposed system is tested on a single room. A grid of ultrasonic sensors is placed in the ceiling of a room to monitor the position and the status of a person (standing, sitting, lying down). The sensors readings are wirelessly communicated through a microcontroller to a cloud. An intelligent system will read the sensors values from the cloud and analyses them using machine learning algorithms to predict the person behavior and status and decide whether it is a normal situation or abnormal. If an abnormal situation is concluded, then an alert with be risen on a dashboard, where a care giver can take an immediate action. The proposed system managed to give results with accuracy exceeding 90%. Results out of this project will help people with supported needed, for example elderly people, to live their life as independent as possible, without too much interference from the caregivers. This will also free the care givers and allows them to monitors more units at the same time.

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“…Several studies have proposed automatic bed-fall detection approaches. Depth cameras, infrared cameras, or ultrasonic sensors were earlier introduced in previous research to capture and monitor the movement patterns of patients while sleeping [3,[11][12][13]. However, the use of cameras involved sleeper privacy violations, and the accuracy of detection was reduced due to coverage of the patient by bedding.…”

Section: Introductionmentioning

confidence: 99%

A Bed-Fall Notification System Using Pressure and Ultrasonic Sensors

Sueaseenak

Pornpreedawan

Puengsiricharoen

et al. 2021

Curr. Appl. Sci. Technol.

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Falls are a major cause of unintentional injury and mortality in the senior population. Therefore, early fall notification can prevent any undesirable damage. This paper aims to present a mobile electronic device-based bed-fall notification using the combination of six thin-film pressure sensors and six ultrasonic sensors. The proposed system monitors movements, detects a bed-fall, and sends an alarm to caregivers. The Finite State Machine (FSM) is used to classify 3 states of movement patterns: the lying, sitting, and falling states. The Internet of Things (IoT) system is adopted to transmit the movement status to the application on an android mobile device. The proposed bed-fall classification system yielded promising results for accuracy, sensitivity, and specificity: 86.67%, 73.33%, and 100%, respectively. In future work, the proposed system can be improved by increasing the number of thin-film pressure sensors, modifying circuit, and applying machine learning models. Accreditation of the system is expected to benefit young and elderly patients in nursing homes, hospitals, and residential areas.

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Ultrasonic array sensors for monitoring of human fall detection

Cited by 27 publications

References 8 publications

A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning

A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning

Tracking a Person’s Behaviour in a Smart House

A Bed-Fall Notification System Using Pressure and Ultrasonic Sensors

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