Development of LSTM Model for Fall Prediction Using IMU

V. Chandramouleesvar; M.E. Swetha; P. Visalakshi

doi:10.4028/p-stigt6

Paper Titles

Crime Prediction Using Machine Learning Algorithms
p.457

Prediction of Eligibility for Covid-19 Vaccine Using SMLT Technique
p.462

A Novel Analysis of Employee Attrition Rate by Maneuvering Machine Learning
p.469

Building a Recommender System Using Collaborative Filtering Algorithms and Analyzing its Performance
p.478

Development of LSTM Model for Fall Prediction Using IMU
p.486

CNN-Based Covid-19 Severity Detection and it’s Diagnosis
p.496

Classification of Imbalanced Datasets Using Various Techniques along with Variants of SMOTE Oversampling and ANN
p.504

Used Car Price Prediction Using Machine Learning
p.512

Pest Classification with Deep Learning and ReactJS
p.518

HomeAdvances in Science and TechnologyAdvances in Science and Technology Vol. 124Development of LSTM Model for Fall Prediction...

Development of LSTM Model for Fall Prediction Using IMU

Abstract:

One of the major contributor leading to cause of unintentional injuries after motor vehicle crashes and poisoning, is Falls. The existing Fall Prediction Algorithms are used to predict falls in older or disabled people by analyzing their fall history, capturing their movements through visual sensors (cameras, thermal imaging etc.) in a fixed environment, using inertial sensors to identify the patterns of movements. These algorithms are monologues for each person as they learn from their history and predict falls specific only to that person. The algorithm proposed in this paper aims to predict falls using kinematic data such as accelerometer, magnetometer, and gyroscopic values, for any user. This work involves developing an algorithm capable of predicting falls and to achieve this, we use Long Short-Term Memory (LSTM). The benefit of this algorithm is to prevent trauma to the body or at least reduce the impact of fall and the fatality caused by it. In the future, this algorithm can be used to design a device to predict falls in real-time to scenario be used by everyone irrespective of gender, age, and health.