Accelerometry-based recognition of the placement sites of a wearable sensor
This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body–ankle, thigh, hip, arm and wrist–from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors...
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| Veröffentlicht in: | Pervasive and mobile computing Jg. 21; S. 62 - 74 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.08.2015
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| ISSN: | 1574-1192, 1873-1589 |
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| Abstract | This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body–ankle, thigh, hip, arm and wrist–from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively. |
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| AbstractList | This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body–ankle, thigh, hip, arm and wrist–from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively. This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and wrist from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively.This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and wrist from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively. |
| Author | Mannini, Andrea Intille, Stephen S. Sabatini, Angelo M. |
| AuthorAffiliation | a The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy b College of Computer and Information Science and Bouvé College of Health Sciences, Northeastern University, Boston, MA |
| AuthorAffiliation_xml | – name: b College of Computer and Information Science and Bouvé College of Health Sciences, Northeastern University, Boston, MA – name: a The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy |
| Author_xml | – sequence: 1 givenname: Andrea surname: Mannini fullname: Mannini, Andrea email: a.mannini@sssup.it organization: The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy – sequence: 2 givenname: Angelo M. surname: Sabatini fullname: Sabatini, Angelo M. organization: The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy – sequence: 3 givenname: Stephen S. surname: Intille fullname: Intille, Stephen S. organization: College of Computer and Information Science and Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26213528$$D View this record in MEDLINE/PubMed |
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| Snippet | This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body–ankle, thigh, hip, arm and... This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and... |
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| SubjectTerms | Accelerometer Activity recognition Body location Walking detection Wearable sensors |
| Title | Accelerometry-based recognition of the placement sites of a wearable sensor |
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