Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke
While accelerometers could be used to monitor important domains of walking in daily living (e.g., walking speed), the interpretation of accelerometer data often relies on validation studies performed with healthy participants. The aim of this study was to develop cut-points for waist- and ankle-worn...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/4080 |
| _version_ | 1797491692609208320 |
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| author | David Moulaee Conradsson Lucian John-Ross Bezuidenhout |
| author_facet | David Moulaee Conradsson Lucian John-Ross Bezuidenhout |
| author_sort | David Moulaee Conradsson |
| collection | DOAJ |
| description | While accelerometers could be used to monitor important domains of walking in daily living (e.g., walking speed), the interpretation of accelerometer data often relies on validation studies performed with healthy participants. The aim of this study was to develop cut-points for waist- and ankle-worn accelerometers to differentiate non-ambulation from walking and different walking speeds in people post stroke. Forty-two post-stroke persons wore waist and ankle accelerometers (ActiGraph GT3x+, AG) while performing three non-ambulation activities (i.e., sitting, setting the table and washing dishes) and while walking in self-selected and brisk speeds. Receiver operating characteristic (ROC) curve analysis was used to define AG cut-points for non-ambulation and different walking speeds (0.41–0.8 m/s, 0.81–1.2 m/s and >1.2 m/s) by considering sensor placement, axis, filter setting and epoch length. Optimal data input and sensor placements for measuring walking were a vector magnitude at 15 s epochs for waist- and ankle-worn AG accelerometers, respectively. Across all speed categories, cut-point classification accuracy was good-to-excellent for the ankle-worn AG accelerometer and fair-to-excellent for the waist-worn AG accelerometer, except for between 0.81 and 1.2 m/s. These cut-points can be used for investigating the link between walking and health outcomes in people post stroke. |
| first_indexed | 2024-03-10T00:52:59Z |
| format | Article |
| id | doaj.art-80f065260d6042e2a6e1965b61f85581 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:52:59Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-80f065260d6042e2a6e1965b61f855812023-11-23T14:48:16ZengMDPI AGSensors1424-82202022-05-012211408010.3390/s22114080Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post StrokeDavid Moulaee Conradsson0Lucian John-Ross Bezuidenhout1Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 141 83 Stockholm, SwedenDivision of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 141 83 Stockholm, SwedenWhile accelerometers could be used to monitor important domains of walking in daily living (e.g., walking speed), the interpretation of accelerometer data often relies on validation studies performed with healthy participants. The aim of this study was to develop cut-points for waist- and ankle-worn accelerometers to differentiate non-ambulation from walking and different walking speeds in people post stroke. Forty-two post-stroke persons wore waist and ankle accelerometers (ActiGraph GT3x+, AG) while performing three non-ambulation activities (i.e., sitting, setting the table and washing dishes) and while walking in self-selected and brisk speeds. Receiver operating characteristic (ROC) curve analysis was used to define AG cut-points for non-ambulation and different walking speeds (0.41–0.8 m/s, 0.81–1.2 m/s and >1.2 m/s) by considering sensor placement, axis, filter setting and epoch length. Optimal data input and sensor placements for measuring walking were a vector magnitude at 15 s epochs for waist- and ankle-worn AG accelerometers, respectively. Across all speed categories, cut-point classification accuracy was good-to-excellent for the ankle-worn AG accelerometer and fair-to-excellent for the waist-worn AG accelerometer, except for between 0.81 and 1.2 m/s. These cut-points can be used for investigating the link between walking and health outcomes in people post stroke.https://www.mdpi.com/1424-8220/22/11/4080accelerometersActiGraphgait speedobjective measurementROC analysisstroke |
| spellingShingle | David Moulaee Conradsson Lucian John-Ross Bezuidenhout Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke Sensors accelerometers ActiGraph gait speed objective measurement ROC analysis stroke |
| title | Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke |
| title_full | Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke |
| title_fullStr | Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke |
| title_full_unstemmed | Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke |
| title_short | Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke |
| title_sort | establishing accelerometer cut points to classify walking speed in people post stroke |
| topic | accelerometers ActiGraph gait speed objective measurement ROC analysis stroke |
| url | https://www.mdpi.com/1424-8220/22/11/4080 |
| work_keys_str_mv | AT davidmoulaeeconradsson establishingaccelerometercutpointstoclassifywalkingspeedinpeoplepoststroke AT lucianjohnrossbezuidenhout establishingaccelerometercutpointstoclassifywalkingspeedinpeoplepoststroke |