A simple vision-based fall detection technique for indoor video surveillance

Chua, Jia Luen; Chang, Yoong Choon; Lim, W. L.

doi:10.1007/s11760-013-0493-7

Cited by 124 publications

(39 citation statements)

References 5 publications

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“…The first stage consists of doctors, patients and caretakers in a real-time system lead to real-time physical observations leading to vital signs monitoring where the patient's HR, SPO2, body temp, BPS is measured and compared with patient"s history, consulted with doctors. For observing the physical, mental and emotional health condition of a user a sensing layer is designated; in a group of sensors can be fixed [22], the RFID sensors for identification, the GPS sensor to predict the positioning and localization with an ECG and blood pressure sensors to collect the biomedical parameters are the best examples.In a smart home, the sensors used in appliances are used for recognizing the surroundings depending on the condition and items used in a home [27].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…An internal 1GB flash drive stores the captured data. The effectiveness of an algorithm depends on a successful evaluation, many techniques have been written in the literature [22][23][24][25][26][27][28][29][30][31] depending on extracting motion capture from the tri-axial accelerometer, however, only those techniques are implemented whose success rate is high under a similar accelerometer.…”

Section: Nxtgeuh-vitafall Motion Data Analysismentioning

confidence: 99%

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VitaFALL: NXTGeUH System for Well-Being Monitoring with Fall Recognition and Real-Time Vital Sign Monitoring

Patel*,

Patel,

Ramani

et al. 2020

IJRTE

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The blend of computerized data processing with the existing engineering and medic techniques has enabled explorers in the betterment of controlling of patients concerning the two at homes along with at clinics. In this work, numerous fall assessment for fall prediction and detection with vital signs monitoring techniques and methods particularly to establish a research gap and its allied research problems has been reviewed and incorporated using a triple-axis accelerometer and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the ancient people with a Internet of Medical Things based Vital Signs and Fall Detection (VitaFALL) is proposed which is well-timed and gives an effective judgment of the fall. The four layers comprise sensing, network, data processing and application layer. A caretaker and doctor can be notified by sending alert using a GSM and GPRS module in order that elder can be helped on time, however, a delay in the time is noticed when comparing the gradient and minimum value to predetermine the state of the old person. From a few decades, vital signs have been important parameters to find out the patient’s health level. Vital signs estimation has always been the initial step for the evaluation of the patient and this is also possible by checking the pulse rate or checking the palpation of their forehead for high temperature. ADXL335 Three-Axis Accelerometer Module, tri-axial 14-bit ± 8g accelerometer collects motion information in the VitaFall device. The basic idea is to avoid falls and not to detect them after the loss is done. Walking, stumbling, sitting, falling (right, forward, backward and left) and all other normal motion data patters in the daily life of an older adult (who did no longer have any records or walking issues) are collected. The proposed VitaFall Fall detection model has achieved 85% accuracy, specificity of 100%, and sensitivity of 96% when detecting directional falls. The model uses motion data, real-time vital signs values, falls history to foresee the lows, medians and the highs of falls risks in hospitalized elderly people. When compared with the manual falls risk tools known as the Morse Falls scale, the system got an accuracy of 85%, predictability of 100%, and a sensitivity of 100% too.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Nxtgeuh-vitafall Motion Data Analysismentioning

confidence: 99%

VitaFALL: NXTGeUH System for Well-Being Monitoring with Fall Recognition and Real-Time Vital Sign Monitoring

Patel*,

Patel,

Ramani

et al. 2020

IJRTE

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show abstract

“…Due to camera's inherent weakness, that is, privacy preserving, it has been used less in the indoor environment recently. Chua and coworkers' work [24] is the only one of twenty most cited the papers published after 2014 that employed the camera to detect a fall.…”

Section: Evolution Of Sensorsmentioning

confidence: 99%

New Advances and Challenges of Fall Detection Systems: A Survey

2018

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Falling, as one of the main harm threats to the elderly, has drawn researchers' attentions and has always been one of the most valuable research topics in the daily health-care for the elderly in last two decades. Before 2014, several researchers reviewed the development of fall detection, presented issues and challenges, and navigated the direction for the study in the future. With smart sensors and Internet of Things (IoT) developing rapidly, this field has made great progress. However, there is a lack of a review and discussion on novel sensors, technologies and algorithms introduced and employed from 2014, as well as the emerging challenges and new issues. To bridge this gap, we present an overview of fall detection research and discuss the core research questions on this topic. A total of 6830 related documents were collected and analyzed based on the key words. Among these documents, the twenty most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance. The findings would assist researchers in understanding current developments and barriers in the systems of fall detection. Although researchers achieve fruitful work and progress, this research domain still confronts challenges on theories and practice. In the near future, the new solutions based on advanced IoT will sustainably urge the development to prevent falling injuries.

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“…Mastorakis and Makris [8] designed a video approach for fall detection with no pre-knowledge. Chua and his two colleagues [9] designed a vision system to detect accidental falls and performed well with high detection results.…”

Section: Introductionmentioning

confidence: 99%

Fall Detection System for Monitoring an Elderly Person Based on Six-Axis Gyroscopes

Tang¹,

Ou²

2018

Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018)

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Abstract-Accidental falls are crucial causes of death due to injury among the elderly. Many researches about fall detection applied complex algorithms and required heavy equipment. However, these approaches can hardly apply to the elderly's daily life. In this paper, we employ a six-axis gyroscope that integrated in a small smart bracelet. Users who wear the smart bracelet can get the information including acceleration for X, Y, and Z movement, and the rate of rotation in space. Then, we introduce three feature vector generation methods based on the information and feed these three vectors into support vector machine (SVM) algorithm for fall detection. From a dataset of 66 people, we show that the geometric parameters method is the best of the three with a high accuracy (100%), low false alarm rate (0%) and low missing alarm rate (0%) in a simulated home environment.

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A simple vision-based fall detection technique for indoor video surveillance

Cited by 124 publications

References 5 publications

VitaFALL: NXTGeUH System for Well-Being Monitoring with Fall Recognition and Real-Time Vital Sign Monitoring

VitaFALL: NXTGeUH System for Well-Being Monitoring with Fall Recognition and Real-Time Vital Sign Monitoring

New Advances and Challenges of Fall Detection Systems: A Survey

Fall Detection System for Monitoring an Elderly Person Based on Six-Axis Gyroscopes

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