Development of an Enhanced Threshold-Based Fall Detection System Using Smartphones With Built-In Accelerometers

Lee, Jin‐Shyan; Tseng, Hsuan-Han

doi:10.1109/jsen.2019.2918690

Cited by 67 publications

(31 citation statements)

References 22 publications

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“…On the other hand, some systems defined the specificity of their system for every position of the device. The threshold based system using accelerometer and gyroscope [27] and the threshold based system employing accelerometer [30] achieved 100% specificity for specific positions. However, the specificity for other positions was comparatively low.…”

Section: Discussion and Recommendation For Future Workmentioning

confidence: 97%

“…However, fall type specification or device position in the human body is not specified. The threshold based system using accelerometer readings [30] supports fall detection even if the smartphone is put in a purse, which is very exceptional from other systems.…”

Section: Discussion and Recommendation For Future Workmentioning

confidence: 99%

“…Like the other reviewed systems, this system only worked if the smartphone was put in the waist position or waist pocket. Jin-Shyan et al [30] introduced an upgraded threshold based fall detection mechanism. It was capable of differentiating falls from ADLs.…”

Section: Figure 3 General Block Diagram Of Threshold Based Fall Detementioning

confidence: 99%

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A Review on Fall Detection Systems Using Data from Smartphone Sensors

Islam¹,

Neom²,

Imtiaz³

et al. 2019

ISI

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Accidental falls have become one of the most frequent general health issues in recent years due to the rate of occurrence. Individuals aged above 65 are more prone to accidental falls. Accidental falls result in severe injuries such as concussion, head trauma, physical disabilities even to deaths in serious cases if the patients are not rescued in time. Thus, researchers are focusing on developing fall detection systems that facilitate the detection and quick rescue of fall victims. The smartphone-based fall detection systems use various built-in sensors of smartphones mostly Tri-axial accelerometer, magnetometer, gyroscope, and camera. The majority of the systems employ threshold based algorithms (TBA). Some systems use machine learning (ML) based algorithms or a combination of ML and TBA based algorithms to detect falls. Each of these types of systems has its trade-offs. The goal of this paper is to review fall detection systems based on data from smartphone sensors that employ either one of TBA, ML or combination of both. We also present the taxonomy based on systematic comparisons of existing studies for smartphone-based fall detection solutions.

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Section: Discussion and Recommendation For Future Workmentioning

confidence: 97%

Section: Discussion and Recommendation For Future Workmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Fall Detection Systems Using Data from Smartphone Sensors

Islam¹,

Neom²,

Imtiaz³

et al. 2019

ISI

View full text Add to dashboard Cite

show abstract

“…Though this system was able to classify five different types of fall, the algorithms were validated while down sampling the signals at a rate of 10-20HZ in order to scale down CPU speed and to avoid power consumption which may lead to loss of accuracy in identifying falls. Jin-Shvan [5] et al proposed a system to detect and predict falls using triaxial accelerometer present in smartphones by placing them in pant pocket. This system uses Support vector machines or Cascade-Ada boost classifier, Hidden Markov Model, Nearest Neighbour for Classification.…”

Section: Literature Surveymentioning

confidence: 99%

Survey on Fall Detection System CNN based Fall Detection and Health Monitoring System using IOT

Geetha¹

2020

IJRASET

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Falls are the common problem faced by elderly population. Fall may happen due to fainting. The reason for faint involves sudden changes in the heart rate. Falls are the common cause of traumatic brain injuries in elderly people and also cause severe injuries such as fracture of the hip. This kind of injuries can create negative impact on their quality of life. In most of the cases the elderly who lay on the floors for more than an hour after falls usually results in serious trauma, also leads to death of the individuals. There are some systems exist for detecting the falls of the elderly people. These ambient sensors involve video-based methods for fall detection using rule based and machine learning based methods. The wearable sensor mainly include accelerometer, gyroscope, barometric pressure sensor, infrared sensor and kinetic RGBD camera. The existing systems are based upon the ambient sensors faces some risks such as storage and processing of video is a complex task and it also affects the privacy of the people. It is necessary to propose a system that can overcome the above drawbacks. The system proposed involves capturing the motion of the elderly people using accelerometer sensors. Those captured data via sensors are processed using CNN, which involve steps such as convolution, it results in feature map then it has to be down sampled using the max pooling methods and based on the resultant layers, the captured motion can be determined whether it is a day to day activities or a fall. Once the fall is detected the information will be sent to the family members and neighbours. Additionally, it is provided with sensor to monitor the health conditions of the elderly people, when heart rate deteriorates, an alert message will be sent to neighbours and family members to provide medical assistance.

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“…Many studies have been undertaken in the field of fall detection. The existing technologies can be classified into mainly three types: ambient device-based method [5] [6], wearable sensor-based method [7]~ [12] and computer vision-based method [13]~ [24].…”

Section: Introductionmentioning

confidence: 99%

Fall Detection Based on Dual-Channel Feature Integration

Wang

Kuo

et al. 2020

IEEE Access

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Falls have caught great harm to the elderly living alone at home. This paper presents a novel visual-based fall detection approach by Dual-Channel Feature Integration. The proposed approach divides the fall event into two parts: falling-state and fallen-state, which describes the fall events from dynamic and static perspectives. Firstly, the object detection model (Yolo) and the human posture detection model (OpenPose) are used for preprocessing to obtain key points and the position information of a human body. Then, a dual-channel sliding window model is designed to extract the dynamic features of the human body (centroid speed, upper limb velocity) and static features (human external ellipse). After that, MLP (Multilayer Perceptron) and Random Forest are applied to classify the dynamic and static feature data separately. Finally, the classification results are combined for fall detection. Experimental results show that the proposed approach achieves an accuracy of 97.33% and 96.91% when tested with UR Fall Detection Dataset and Le2i Fall Detection Dataset. INDEX TERMS Computer vision, fall detection, dual channel, machine learning, classification.

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Development of an Enhanced Threshold-Based Fall Detection System Using Smartphones With Built-In Accelerometers

Cited by 67 publications

References 22 publications

A Review on Fall Detection Systems Using Data from Smartphone Sensors

A Review on Fall Detection Systems Using Data from Smartphone Sensors

Survey on Fall Detection System CNN based Fall Detection and Health Monitoring System using IOT

Fall Detection Based on Dual-Channel Feature Integration

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