Fall Detection by Wearable Sensor and One-Class SVM Algorithm

2006 ◽  
pp. 858-863 ◽  
Author(s):  
Tong Zhang ◽  
Jue Wang ◽  
Liang Xu ◽  
Ping Liu
Keyword(s):  
Fall Detection ◽  
Wearable Sensor ◽  
Svm Algorithm

Wearable Sensor-Based Human Fall Detection Wireless System

2018 ◽  
pp. 217-234 ◽  
Author(s):  
Vaishna S. Kumar ◽  
Kavan Gangadhar Acharya ◽  
B. Sandeep ◽  
T. Jayavignesh ◽  
Ashvini Chaturvedi
Keyword(s):  
Fall Detection ◽  
Wearable Sensor ◽  
Wireless System

scholarly journals Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3720 ◽  
Author(s):  
Chengyin Liu ◽  
Zhaoshuo Jiang ◽  
Xiangxiang Su ◽  
Samuel Benzoni ◽  
Alec Maxwell
Keyword(s):  
Fall Detection ◽  
Support Vector ◽  
Accelerometer Signal ◽  
Svm Algorithm ◽  
Study Results ◽  
Benchmark Database ◽  
Vector Machines ◽  
Floor Vibration ◽  
Nonfatal Injuries ◽  
Peak Value

Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection will dramatically improve the health outcomes of the older population and reduce the associated healthcare cost after a fall. To achieve the goal, a multi-features semi-supervised support vector machines (MFSS-SVM) algorithm utilizing measurements from structural floor vibration obtained through accelerometers is proposed in this study to detect falling events with limited labeled samples. In this MFSS-SVM algorithm, the peak value, energy, and correlation coefficient of the accelerometer signal are used as classification features. The performance of the proposed algorithm was validated with laboratory experiments among activities including falling, walking, free jumping, rhythmic jumping, bag dropping, and ball dropping. To further illustrate the performance of the algorithm, a benchmark database was adopted and expanded to test its ability to accurately identify falling, compared with the algorithm used in the benchmark study. Results show that by using the proposed algorithm, the falling events can be identified with high accuracy and confidence, even with small training datasets and test nodes.

EldTec: Improvement on Wearable Sensor for Elderly Fall Detection

2018 ◽  
Author(s):  
Jarinya Limpanadusadee ◽  
Panyarat Kesawattana ◽  
Thitiwud Wongsawat ◽  
Damras Wongsawang
Keyword(s):  
Fall Detection ◽  
Wearable Sensor

scholarly journals Development of a Wearable-Sensor-Based Fall Detection System

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Falin Wu ◽  
Hengyang Zhao ◽  
Yan Zhao ◽  
Haibo Zhong
Keyword(s):  
Physical Fitness ◽  
Human Body ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Daily Activities ◽  
Public Healthcare ◽  
Wearable Sensor ◽  
Negative Effects ◽  
The Public

Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient’s location.

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