Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features
Faller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/6/1321 |
| _version_ | 1811184867038199808 |
|---|---|
| author | Dylan Drover Jennifer Howcroft Jonathan Kofman Edward D. Lemaire |
| author_facet | Dylan Drover Jennifer Howcroft Jonathan Kofman Edward D. Lemaire |
| author_sort | Dylan Drover |
| collection | DOAJ |
| description | Faller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers and non-fallers, completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. After segmenting straight and turn sections, cross validation tests were conducted on straight and turn walking features to assess classification performance. The best “classifier model—feature selector” combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, and 0.44 Matthew’s Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back) achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score. All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data. Combining turn and straight walking features decreased performance metrics compared to turn features for similar classifier model—feature selector combinations. |
| first_indexed | 2024-04-11T13:19:38Z |
| format | Article |
| id | doaj.art-cf7054a3bf2c41cfab7d0a70bd3d1eb9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:19:38Z |
| publishDate | 2017-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cf7054a3bf2c41cfab7d0a70bd3d1eb92022-12-22T04:22:16ZengMDPI AGSensors1424-82202017-06-01176132110.3390/s17061321s17061321Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based FeaturesDylan Drover0Jennifer Howcroft1Jonathan Kofman2Edward D. Lemaire3Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaDepartment of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaDepartment of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaCentre for Rehabilitation Research and Development, Ottawa Hospital Research Institute, Ottawa, ON K1H 8M2, CanadaFaller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers and non-fallers, completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. After segmenting straight and turn sections, cross validation tests were conducted on straight and turn walking features to assess classification performance. The best “classifier model—feature selector” combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, and 0.44 Matthew’s Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back) achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score. All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data. Combining turn and straight walking features decreased performance metrics compared to turn features for similar classifier model—feature selector combinations.http://www.mdpi.com/1424-8220/17/6/1321wearable sensorsmachine learningaccelerometerfaller classificationfaller predictionfeature selectionelderlyfallsprospective fallers |
| spellingShingle | Dylan Drover Jennifer Howcroft Jonathan Kofman Edward D. Lemaire Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features Sensors wearable sensors machine learning accelerometer faller classification faller prediction feature selection elderly falls prospective fallers |
| title | Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features |
| title_full | Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features |
| title_fullStr | Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features |
| title_full_unstemmed | Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features |
| title_short | Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features |
| title_sort | faller classification in older adults using wearable sensors based on turn and straight walking accelerometer based features |
| topic | wearable sensors machine learning accelerometer faller classification faller prediction feature selection elderly falls prospective fallers |
| url | http://www.mdpi.com/1424-8220/17/6/1321 |
| work_keys_str_mv | AT dylandrover fallerclassificationinolderadultsusingwearablesensorsbasedonturnandstraightwalkingaccelerometerbasedfeatures AT jenniferhowcroft fallerclassificationinolderadultsusingwearablesensorsbasedonturnandstraightwalkingaccelerometerbasedfeatures AT jonathankofman fallerclassificationinolderadultsusingwearablesensorsbasedonturnandstraightwalkingaccelerometerbasedfeatures AT edwarddlemaire fallerclassificationinolderadultsusingwearablesensorsbasedonturnandstraightwalkingaccelerometerbasedfeatures |