Research of Humen Fall Detection Algorithm Based on Tri-Axis Accelerometer

2012 ◽  
Vol 500 ◽  
pp. 623-628
Author(s):  
Yang Gao ◽  
Jing Min Gao ◽  
Jiu He Wang
Keyword(s):  
Human Movement ◽  
Threshold Value ◽  
Fall Detection ◽  
Detection Algorithm ◽  
General Level ◽  
Accelerometer Sensor ◽  
Characteristic Quantity ◽  
Detection Technology ◽  
Level Of Activity ◽  
Body Activity

The real-time monitoring of human movement can provide valuable information regarding an individual’s degree of functional ability and general level of activity. This paper presents a fall-detection technology based on tri-axial accelerometer sensor was introduced. The acceleration data of the activities with the characteristic quantity SVM and SMA was analyzed. A method based on SVM and SMA, that takes the human body activity (erectness/lies down) as the auxiliary criterion to distinguish fall and ADL is proposed, and concrete threshold value and related parameters are summarized in this article. The experiment results proved that this scheme can obtain highly rate of accuracy and this algorithm has very good timeliness.

Accelerometer Sensor Based Fall Sensing for Elderly

2015 ◽  
Vol 73 (1) ◽  
Author(s):  
Anas Mohd Noor ◽  
Hafizudin Zainudin ◽  
Normaheran Hanafi ◽  
Siti Aishah Baharuddin ◽  
Mohamad Aliff Abdul Rahim
Keyword(s):  
Mobile Communications ◽  
Signal Transmission ◽  
Cost Effective ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Prototype System ◽  
Accelerometer Sensor ◽  
Wireless Signal ◽  
Overall Performance ◽  
Key Parameter

Fall can be recognized as an abnormal or action of losing an upright motion which will cause people especially elderly to suffer from pain and more seriously can affect one’s health. Being able to detect fall is key parameter to decrease the risk of severe injury to the seniors. There are such existing fall detection products on the market to assist elderly so that immediate response could be taken. However, due to complexity system, high cost and employing outside technology, these products initiate limitations such as maintenance and system enhancement. In this project, a fall detection device and system is developed using local technology, simple and cost effective. The prototype system consist of accelerometer sensing circuit, microcontroller with wireless signal transmission, Global System for Mobile Communications (GSM) notification alert for mobile phone and graphical user interface (GUI) to obtain real-time monitoring. The simple fall detection algorithm is developed to ensure false detection could be minimized. The overall performance of the developed device and system is proven reliable and practical. 

Fall detection smart-shoe enabled with wireless IoT device

Circuit World ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abanah Shirley J. ◽  
Esther Florence Sundarsingh ◽  
Saraswathi V. ◽  
Sankareshwari S. ◽  
Sona S.
Keyword(s):  
Design Methodology ◽  
Threshold Value ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Medical Attention ◽  
Hardware Complexity ◽  
Rule Based ◽  
Content Type ◽  
Force Sensitivity ◽  
Compact Module

Purpose Fall detection is a primary necessity for elderly people with medically tested nervous problems. This paper aims important to detect fall and prevent fatal injuries and untreated attention for long hours. Design/methodology/approach The project is focused on developing a smart shoe with force-sensitive resistors placed at plantar pressure points to detect fall. This could draw immediate medical attention to the patient. The device is developed using sensors, microcontroller and accelerometer integrated into a compact module. A rule-based detection algorithm helps in transmitting the alert to an Internet of Things device when a fall is detected. Findings Based on the pressure applied, there is a change in resistive value of force sensitivity resistor. When it reaches the threshold value, fall gets detected and alert gets triggered through telegram bot with latitude and longitude details of the location. Originality/value The challenge in developing this device is to make it wearable reducing the overall hardware complexity. The entire module placed inside the sole of the shoe avoids inconvenience to the patients.

DEVELOPMENT OF FALL DETECTION ALGORITHM FOR TELEMONITORING SYSTEM

2019 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
C. A. MEBRIM ◽  
O. C. UBADIKE ◽  
A. M. AIBINU ◽  
I. I. ALEGBELEYE ◽  
A. J. ONUMANYI ◽  
...  
Keyword(s):  
Fall Detection ◽  
Detection Algorithm

scholarly journals A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaheen Syed ◽  
Bente Morseth ◽  
Laila A. Hopstock ◽  
Alexander Horsch
Keyword(s):  
Threshold Value ◽  
Detection Algorithm ◽  
Classification Model ◽  
Type I ◽  
Accelerometer Data ◽  
Deep Convolutional Neural Networks ◽  
Major Drawback ◽  
Wear Time ◽  
Detection Algorithms ◽  
Type Ii Errors

AbstractTo date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.

scholarly journals Development of Wireless Sensor Device for Machine English Oral Pronunciation Noise Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yaning Zhu
Keyword(s):  
Closed Loop ◽  
Detection Algorithm ◽  
Training System ◽  
Circuit Board ◽  
Wireless Sensor ◽  
Noise Detection ◽  
Traditional System ◽  
Spoken English ◽  
Loop Design ◽  
Detection Technology

There is often noise in spoken machine English, which affects the accuracy of pronunciation. Therefore, how to accurately detect the noise in machine English spoken language and give standard spoken pronunciation is very important and meaningful. The traditional machine-oriented spoken English speech noise detection technology is limited to the improvement of software algorithm, mainly including speech enhancement technology and speech endpoint detection technology. Based on this, this paper will develop a wireless sensor network based on machine English oral pronunciation noise based on air and nonair conduction, reasonably design and configure air sensors, and nonair conduction sensors to deal with machine English oral pronunciation noise, so as to improve the naturalness and intelligibility of machine English speech. At the hardware level, this paper mainly optimizes the AD sampling, sensor matching layout, and internal hardware circuit board layout of the two types of sensors, so as to solve the compatibility problem between them and further reduce the hardware power consumption. In order to further verify or evaluate the performance of the machine spoken English speech noise detection sensor designed in this paper, a machine spoken English training system based on Android platform is designed. Compared with the traditional system, the training system can improve the intelligence of machine oriented oral English noise detection algorithm, so as to continuously improve the accuracy of system detection. The machine English pronunciation is adjusted and corrected by combining the data sensed by the sensor, so as to form a closed-loop design. The experimental results show that the wireless sensor sample proposed in this paper has obvious advantages in detecting the accuracy of machine English oral pronunciation, and its good closed-loop system is helpful to further improve the accuracy of machine English oral pronunciation.

scholarly journals Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment

2020 ◽  
Vol 32 (4) ◽  
pp. 1209 ◽  
Author(s):  
Junsuo Qu ◽  
Chen Wu ◽  
Qian Li ◽  
Ting Wang ◽  
Abdel Hamid Soliman
Keyword(s):  
Sensor Fusion ◽  
Algorithm Design ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Comprehensive Judgment
Sign in / Sign up

Export Citation Format

Share Document