A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results
Injuries due to unintentional falls cause high social cost in which several systems have been developed to reduce them. Recently, two trends can be recognized. Firstly, the market is dominated by fall detection systems, which activate an alarm after a fall occurrence, but the focus is moving towards...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Human-Computer Interaction |
| Online Access: | http://dx.doi.org/10.1155/2019/9610567 |
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| _version_ | 1849306347182489600 |
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| author | Masoud Hemmatpour Renato Ferrero Bartolomeo Montrucchio Maurizio Rebaudengo |
| author_facet | Masoud Hemmatpour Renato Ferrero Bartolomeo Montrucchio Maurizio Rebaudengo |
| author_sort | Masoud Hemmatpour |
| collection | DOAJ |
| description | Injuries due to unintentional falls cause high social cost in which several systems have been developed to reduce them. Recently, two trends can be recognized. Firstly, the market is dominated by fall detection systems, which activate an alarm after a fall occurrence, but the focus is moving towards predicting and preventing a fall, as it is the most promising approach to avoid a fall injury. Secondly, personal devices, such as smartphones, are being exploited for implementing fall systems, because they are commonly carried by the user most of the day. This paper reviews various fall prediction and prevention systems, with a particular interest to the ones that can rely on the sensors embedded in a smartphone, i.e., accelerometer and gyroscope. Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. Results show that tilt features in combination with a decision tree algorithm present the best performance. |
| format | Article |
| id | doaj-art-2eb0494edfce4c32bf0103c2c0155c6b |
| institution | Kabale University |
| issn | 1687-5893 1687-5907 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Human-Computer Interaction |
| spelling | doaj-art-2eb0494edfce4c32bf0103c2c0155c6b2025-08-20T03:55:07ZengWileyAdvances in Human-Computer Interaction1687-58931687-59072019-01-01201910.1155/2019/96105679610567A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental ResultsMasoud Hemmatpour0Renato Ferrero1Bartolomeo Montrucchio2Maurizio Rebaudengo3Dipartimento di Automatica e Informatica, Politecnico di Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, ItalyInjuries due to unintentional falls cause high social cost in which several systems have been developed to reduce them. Recently, two trends can be recognized. Firstly, the market is dominated by fall detection systems, which activate an alarm after a fall occurrence, but the focus is moving towards predicting and preventing a fall, as it is the most promising approach to avoid a fall injury. Secondly, personal devices, such as smartphones, are being exploited for implementing fall systems, because they are commonly carried by the user most of the day. This paper reviews various fall prediction and prevention systems, with a particular interest to the ones that can rely on the sensors embedded in a smartphone, i.e., accelerometer and gyroscope. Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. Results show that tilt features in combination with a decision tree algorithm present the best performance.http://dx.doi.org/10.1155/2019/9610567 |
| spellingShingle | Masoud Hemmatpour Renato Ferrero Bartolomeo Montrucchio Maurizio Rebaudengo A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results Advances in Human-Computer Interaction |
| title | A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results |
| title_full | A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results |
| title_fullStr | A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results |
| title_full_unstemmed | A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results |
| title_short | A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results |
| title_sort | review on fall prediction and prevention system for personal devices evaluation and experimental results |
| url | http://dx.doi.org/10.1155/2019/9610567 |
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