Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data.
10.4258/hir.2017.23.3.147
- Author:
Moiz AHMED
1
;
Nadeem MEHMOOD
;
Adnan NADEEM
;
Amir MEHMOOD
;
Kashif RIZWAN
Author Information
1. Department of Computer Science, University of Karachi, Karachi, Pakistan.
- Publication Type:Original Article
- Keywords:
Aged Humans;
Computer Communication Network;
Accidental Fall Detection;
Information Systems;
Shimmer;
Wireless Technology;
Machine Learning
- MeSH:
Accidental Falls;
Aged*;
Caregivers;
Cause of Death;
Classification*;
Computer Communication Networks;
Dataset;
Developed Countries;
Developing Countries;
Emergencies;
Humans;
Information Systems;
Machine Learning;
Pakistan;
Posture;
Support Vector Machine;
Wireless Technology
- From:Healthcare Informatics Research
2017;23(3):147-158
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. METHODS: We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. RESULTS: To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. CONCLUSIONS: In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.
