An automatic in-home fall detection system using Doppler radar signatures

2016 ◽  
Vol 8 (4) ◽  
pp. 453-466 ◽  
Author(s):  
Liang Liu ◽  
Mihail Popescu ◽  
Marjorie Skubic ◽  
Marilyn Rantz ◽  
Paul Cuddihy
Keyword(s):  
Doppler Radar ◽  
Detection System ◽  
Fall Detection ◽  
Radar Signatures

scholarly journals Intelligent fall detection system for eldercare

2014 ◽  
Author(s):  
◽  
Liang Liu
Keyword(s):  
Doppler Radar ◽  
Detection System ◽  
Fall Detection ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Training Dataset ◽  
Support Vector ◽  
Features Selection ◽  
Radar Sensor ◽  
Mel Frequency Cepstral Coefficients

Fall among elders is a main reason to cause accidental death among the population over the age 65 in United States. The fall detection methods have been brought into scene by implemented on different fall monitoring devices. For the advantages in privacy protection and non-invasive, independent of light, I design the fall detection system based on Doppler radar sensor. This dissertation explores different Doppler radar sensor configurations and positioning in both of the lab and real senior home environment, signal processing and machine learning algorithms. Firstly, I design the system based on the data collected with three configurations: two floor radars, one ceiling and one wall radars, one ceiling and one floor radars in lab. The performance of the sensor positioning and features are evaluated with classifiers: support vector machine, nearest neighbor, naïve Bayes, hidden Markov model. In the real senior home, I investigate the system by evaluating the detection variances caused by training dataset due to the variable subjects and environment settings. Moreover, I adjust the automatic fall detection system for the actual retired community apartment. I examine different features: Mel-frequency cepstral coefficients (MFCCs), local binary patterns (LBP) and the combined version of features with RELIEF algorithm. I also improve the detection performance with both pre-screener and features selection. I fuse the radar fall detection system with motion sensors. I develop a standalone fall detection system and generate a result to display on a designed webpage.

Development of Fall Detection System Using Ultrasound Sensors

2011 ◽  
Vol 131 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Takuya Tajima ◽  
Takehiko Abe ◽  
Haruhiko Kimura
Keyword(s):  
Detection System ◽  
Fall Detection

scholarly journals Deep learning for vision‐based fall detection system: Enhanced optical dynamic flow

2020 ◽  
Author(s):  
Sagar Chhetri ◽  
Abeer Alsadoon ◽  
Thair Al‐Dala'in ◽  
P. W. C. Prasad ◽  
Tarik A. Rashid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Fall Detection ◽  
Dynamic Flow

scholarly journals Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

2017 ◽  
Vol 34 ◽  
pp. 3-13 ◽  
Author(s):  
Miguel Ángel Álvarez de la Concepción ◽  
Luis Miguel Soria Morillo ◽  
Juan Antonio Álvarez García ◽  
Luis González-Abril
Keyword(s):  
Elderly People ◽  
Activity Recognition ◽  
Detection System ◽  
Fall Detection
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