Name
Title | ||
|---|---|---|
A Machine Learning Based Fall Detection for Elderly People with Neurodegenerative Disorders. |
Abstract | ||
|---|---|---|
Fall is one of the most serious clinical problems faced by the elderly people. Elder people with neurodegenerative disorders like Parkinson disease often fall. This leads to the damage of physical condition and also mental condition. Therefore, elderly people should be taken care of all the time. However, it is not possible to take care of them every moment. Therefore, an automatic fall detection system is required to track elderly at any time. An automated fall detection system will provide timely assistance and hence, it will reduce medical care costs significantly. The recent developments in motion- sensor technologies have allowed the efficient use of wearable sensors in the overall treatment of the elderly. The paper presents a machine learning framework consisting of data collection, preprocessing of data, feature extraction and machine learning classifiers. They comprise C4.5, Random Forest, RepTree, and LMT (Logistic Model Tree). Dataset used in this research has been collected by using 3-axis accelerometer sensors which are mounted on a person’s waist. Features have been extracted from this dataset which are used by these classifiers. C4.5 gives the highest accuracy which is 97.36% in comparison to other classifiers. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-59277-6_18 | BI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nazmun Nahar | 1 | 0 | 0.34 |
| Mohammad Shahadat Hossain | 2 | 32 | 12.25 |
| Karl Andersson | 3 | 80 | 22.20 |