Multi sensor system for automatic fall detection

Nadee, Chokemongkol; Chamnongthai, Kosin

doi:10.1109/apsipa.2015.7415408

Cited by 16 publications

(4 citation statements)

References 9 publications

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“…To prevent ultrasonic signal interference, coverage areas must not overlap. Te possible number of ultrasonic nodes for the simultaneous distance measurement can be determined by the minimum distance between working ultrasonic nodes, as shown in equation (7). To efciently scan ultrasonic nodes in an array by maintaining a minimum distance, zigzag scanning is recommended to balance the horizontal and vertical directions; notably, zigzag scanning is demonstrated for a pair of ultrasonic nodes in Figure 8.…”

Section: Rotation Of Active Ultrasonic Sensorsmentioning

confidence: 99%

“…Te basic conditions of this system include trust, privacy, and safety. We attempted to utilize ultrasonic sensor arrays installed on the ceiling and walls of a room to detect human falls, and experiments were performed with a static model [6][7][8]. A small room (30 × 30 cm 2 ) with nine ultrasonic sensors was used to test the static model [6].…”

Section: Introductionmentioning

confidence: 99%

“…Ultrasonic interference between neighboring sensors and sensor calibration were found to be problems. These problems were considered, and the sensors were adjusted to mitigate interference [ 7 ]. However, coverage gaps between neighboring sensors in the array were found.…”

Section: Introductionmentioning

confidence: 99%

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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: Rotation Of Active Ultrasonic Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Nadee

Chamnongthai

2022

Journal of Healthcare Engineering

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“…Guan et al (26) presented a novel method of detecting a fall using a pyroelectric infrared (PIR) sensor network similar to that of Yun and Song. (27) Nadee and Chamnongthai (28) proposed a method of automatically detecting a falling person using an array of ultrasonic waves connected to a field-programmable gate array (FPGA) processor. The ultrasonic sensor should be placed directly towards objects to acquire the highest measurement accuracy.…”

Section: Devices and Wearablesmentioning

confidence: 99%

Recognizing Falls, Daily Activities, and Health Monitoring by Smart Devices

Sukreep¹,

Elgazzar²,

Chu³

et al. 2019

Sensors and Materials

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One of the biggest challenges in ageing societies is to improve life, health, safety, and support of the elderly population in their daily life. Currently, the number of elderly people living alone is increasing every year. Living alone allows more freedom but raises the risk of serious injuries or fatal accidents. Falls are the key cause of significant health problems, especially for an elderly person who lives alone. Moreover, vital signs such as heart rate, balancing activities, and environmental context are crucial in relation to the user's condition. To assist people living alone and improve their health quality, we firmly believe that the advances in Smart Devices, Smart Environment, and Internet of Things paradigms are very helpful for developing a fall and activity recognition system. We propose a system using an unobtrusive device consisting of a smartwatch and a smartphone to identify falls and thirteen daily activities (e.g., walking, running, typing, and waving the hand). The events leading to a fall, the speed of falling down, the heart rate while doing an activity, and the time passed since the fall are important data that we store to help a doctor diagnose and rehabilitate a patient. Environment sensors are used to indicate the indices of ambient conditions such as temperature, humidity, brightness, and motion detected. Suitable machine learning techniques are used for daily activity recognition, and the processing time for classification was compared on the basis of a smartwatch and an Amazon Web Services (AWS) cloud server. Threshold-based health risk analysis models are utilized for abnormal activity recognition and heart rate and heat index (temperature and humidity) determination. The system issues different types of notifications such as warning messages, sounding alarms, and phone calls to related persons such as family members, caregivers, or doctors. Various easy-to-understand visualizations are presented to track and monitor the subjects in real time, including heart rate, daily activity summary, health risk status, and environmental information.

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