Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography
Abstract We have investigated the use of an Artificial Neural Network (ANN) for the assessment of fall-risk (FR) in patients with different neural pathologies. The assessment integrates a clinical tool based on a wearable device (WD) with accelerometers (ACCs) and rate gyroscopes (GYROs) properly su...
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| Veröffentlicht in: | Medical engineering & physics 2008-04, Vol.30 (3), p.367-372 |
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| creator | Giansanti, Daniele Maccioni, Giovanni Cesinaro, Stefano Benvenuti, Francesco Macellari, Velio |
| description | Abstract We have investigated the use of an Artificial Neural Network (ANN) for the assessment of fall-risk (FR) in patients with different neural pathologies. The assessment integrates a clinical tool based on a wearable device (WD) with accelerometers (ACCs) and rate gyroscopes (GYROs) properly suited to identify trunk kinematic parameters that can be measured during a posturography test with different constraints. Our ANN – a Multi Layer Perceptron Neural Network with four layers and 272 neurones – shows to be able to classify patients in three well-known fall-risk levels. The training of the neural network was carried on three groups of 30 subjects with different Fall-Risk Tinetti scores. The validation of our neural network was carried out on three groups of 100 subjects with different Fall-Risk Tinetti scores and this validation demonstrated that the neural network had high specificity (≥0.88); sensitivity (≥0.87); area under Receiver-Operator Characteristic Curves (>0.854). |
| doi_str_mv | 10.1016/j.medengphy.2007.04.006 |
| format | Article |
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The assessment integrates a clinical tool based on a wearable device (WD) with accelerometers (ACCs) and rate gyroscopes (GYROs) properly suited to identify trunk kinematic parameters that can be measured during a posturography test with different constraints. Our ANN – a Multi Layer Perceptron Neural Network with four layers and 272 neurones – shows to be able to classify patients in three well-known fall-risk levels. The training of the neural network was carried on three groups of 30 subjects with different Fall-Risk Tinetti scores. The validation of our neural network was carried out on three groups of 100 subjects with different Fall-Risk Tinetti scores and this validation demonstrated that the neural network had high specificity (≥0.88); sensitivity (≥0.87); area under Receiver-Operator Characteristic Curves (>0.854).</description><subject>Acceleration</subject><subject>Accelerometer</subject><subject>Accidental Falls - statistics & numerical data</subject><subject>Accuracy</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Back - physiology</subject><subject>Biomechanical Phenomena - methods</subject><subject>Evaluation Studies as Topic</subject><subject>Fall prevention</subject><subject>Fall-risk</subject><subject>Female</subject><subject>Gyroscopes</subject><subject>Human movement analysis</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Patient-monitoring</subject><subject>Postural Balance</subject><subject>Posture - physiology</subject><subject>Radiology</subject><subject>Risk Assessment - methods</subject><subject>Risk Factors</subject><subject>ROC Curve</subject><subject>Rotation</subject><subject>Transducers</subject><issn>1350-4533</issn><issn>1873-4030</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNUk2P1DAMrRCIXRb-AuTErcVJ2qS9II1Wy4e0EgfgHKWpO2S2TUvS7mrEn8dlRiBxgZMt-_k9y89Z9opDwYGrN4dixA7Dfv52LASALqAsANSj7JLXWuYlSHhMuawgLyspL7JnKR0AoCyVfJpdcF0pqEV1mf3YpYQpjRgWNvWst8OQR5_uWHtkI9qQtqplAddoBwrLwxSpaRN2bApsttGOuGBMzP4iojJNWvaA1GkHZB3ee0dhjT7s2TylZY3TPlra_Hn2hPQSvjjHq-zru5sv1x_y20_vP17vbnNXCb7kjRJVLyquue0Edry2SAkIUfeip1rdVk3FW4lOKqucKoHrnncouVaONyivstcn3jlO31dMixl9cjgMNuC0JqNB1kQn_wnkjdR1oxUB9Qno4pRSxN7M0Y82Hg0HsxlkDua3QWYzyEBpyCCafHmWWFtC_Jk7O0KA3QmAdJF7j9Ek5zE47HxEt5hu8v8h8vYvDjf44J0d7vCI6TCtMdDBDTdJGDCftz_Z3gQ0vYii5Cejjrye</recordid><startdate>20080401</startdate><enddate>20080401</enddate><creator>Giansanti, Daniele</creator><creator>Maccioni, Giovanni</creator><creator>Cesinaro, Stefano</creator><creator>Benvenuti, Francesco</creator><creator>Macellari, Velio</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20080401</creationdate><title>Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography</title><author>Giansanti, Daniele ; Maccioni, Giovanni ; Cesinaro, Stefano ; Benvenuti, Francesco ; Macellari, Velio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c521t-9625f25171ad2ed18aead20228f2f1ad8b5951b3ec36a6c64017f1de3176c19e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Acceleration</topic><topic>Accelerometer</topic><topic>Accidental Falls - statistics & numerical data</topic><topic>Accuracy</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Back - physiology</topic><topic>Biomechanical Phenomena - methods</topic><topic>Evaluation Studies as Topic</topic><topic>Fall prevention</topic><topic>Fall-risk</topic><topic>Female</topic><topic>Gyroscopes</topic><topic>Human movement analysis</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Patient-monitoring</topic><topic>Postural Balance</topic><topic>Posture - physiology</topic><topic>Radiology</topic><topic>Risk Assessment - methods</topic><topic>Risk Factors</topic><topic>ROC Curve</topic><topic>Rotation</topic><topic>Transducers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Giansanti, Daniele</creatorcontrib><creatorcontrib>Maccioni, Giovanni</creatorcontrib><creatorcontrib>Cesinaro, Stefano</creatorcontrib><creatorcontrib>Benvenuti, Francesco</creatorcontrib><creatorcontrib>Macellari, Velio</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Medical engineering & physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Giansanti, Daniele</au><au>Maccioni, Giovanni</au><au>Cesinaro, Stefano</au><au>Benvenuti, Francesco</au><au>Macellari, Velio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography</atitle><jtitle>Medical engineering & physics</jtitle><addtitle>Med Eng Phys</addtitle><date>2008-04-01</date><risdate>2008</risdate><volume>30</volume><issue>3</issue><spage>367</spage><epage>372</epage><pages>367-372</pages><issn>1350-4533</issn><eissn>1873-4030</eissn><abstract>Abstract We have investigated the use of an Artificial Neural Network (ANN) for the assessment of fall-risk (FR) in patients with different neural pathologies. The assessment integrates a clinical tool based on a wearable device (WD) with accelerometers (ACCs) and rate gyroscopes (GYROs) properly suited to identify trunk kinematic parameters that can be measured during a posturography test with different constraints. Our ANN – a Multi Layer Perceptron Neural Network with four layers and 272 neurones – shows to be able to classify patients in three well-known fall-risk levels. The training of the neural network was carried on three groups of 30 subjects with different Fall-Risk Tinetti scores. The validation of our neural network was carried out on three groups of 100 subjects with different Fall-Risk Tinetti scores and this validation demonstrated that the neural network had high specificity (≥0.88); sensitivity (≥0.87); area under Receiver-Operator Characteristic Curves (>0.854).</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>17560825</pmid><doi>10.1016/j.medengphy.2007.04.006</doi><tpages>6</tpages></addata></record> |
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| subjects | Acceleration Accelerometer Accidental Falls - statistics & numerical data Accuracy Adult Aged Aged, 80 and over Algorithms Artificial Intelligence Back - physiology Biomechanical Phenomena - methods Evaluation Studies as Topic Fall prevention Fall-risk Female Gyroscopes Human movement analysis Humans Male Middle Aged Neural networks Neural Networks (Computer) Patient-monitoring Postural Balance Posture - physiology Radiology Risk Assessment - methods Risk Factors ROC Curve Rotation Transducers |
| title | Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography |
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