Human action recognition based on low- and high-level data from wearable inertial sensors
Human action recognition supported by highly accurate specialized systems, ambulatory systems, or wireless sensor networks has a tremendous potential in the areas of healthcare or wellbeing monitoring. Recently, several studies carried out focused on the recognition of actions using wearable inertia...
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| Format: | Article |
| Language: | English |
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Wiley
2019-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719894532 |
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| author | Irvin Hussein Lopez-Nava Angélica Muñoz-Meléndez |
| author_facet | Irvin Hussein Lopez-Nava Angélica Muñoz-Meléndez |
| author_sort | Irvin Hussein Lopez-Nava |
| collection | DOAJ |
| description | Human action recognition supported by highly accurate specialized systems, ambulatory systems, or wireless sensor networks has a tremendous potential in the areas of healthcare or wellbeing monitoring. Recently, several studies carried out focused on the recognition of actions using wearable inertial sensors, in which raw sensor data are used to build classification models, and in a few of them high-level representations are obtained which are directly related to anatomical characteristics of the human body. This research focuses on classifying a set of activities of daily living, such as functional mobility, and instrumental activities of daily living, such as preparing meals, performed by test subjects in their homes in naturalistic conditions. The joint angles of upper and lower limbs are estimated using information from five wearable inertial sensors placed on the body of five test subjects. A set of features related to human limb motions is extracted from the orientation signals (high-level data) and another set from the acceleration raw signals (low-level data) and both are used to build classifiers using four inference algorithms. The proposed features in this work are the number of movements and the average duration of consecutive movements. The classifiers are capable of successfully classifying the set of actions using raw data with up to 77.8% and 93.3% from high-level data. This study allowed comparing the use of two data levels to classify a set of actions performed in daily environments using an inertial sensor network. |
| format | Article |
| id | doaj-art-559fe9c3e5d845d9ad0bb94c052b8bc5 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-559fe9c3e5d845d9ad0bb94c052b8bc52025-08-20T03:18:08ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-12-011510.1177/1550147719894532Human action recognition based on low- and high-level data from wearable inertial sensorsIrvin Hussein Lopez-Nava0Angélica Muñoz-Meléndez1Instituto Nacional de Astrofísica, Óptica y Electrónica, San Andrés Cholula, MexicoInstituto Nacional de Astrofísica, Óptica y Electrónica, San Andrés Cholula, MexicoHuman action recognition supported by highly accurate specialized systems, ambulatory systems, or wireless sensor networks has a tremendous potential in the areas of healthcare or wellbeing monitoring. Recently, several studies carried out focused on the recognition of actions using wearable inertial sensors, in which raw sensor data are used to build classification models, and in a few of them high-level representations are obtained which are directly related to anatomical characteristics of the human body. This research focuses on classifying a set of activities of daily living, such as functional mobility, and instrumental activities of daily living, such as preparing meals, performed by test subjects in their homes in naturalistic conditions. The joint angles of upper and lower limbs are estimated using information from five wearable inertial sensors placed on the body of five test subjects. A set of features related to human limb motions is extracted from the orientation signals (high-level data) and another set from the acceleration raw signals (low-level data) and both are used to build classifiers using four inference algorithms. The proposed features in this work are the number of movements and the average duration of consecutive movements. The classifiers are capable of successfully classifying the set of actions using raw data with up to 77.8% and 93.3% from high-level data. This study allowed comparing the use of two data levels to classify a set of actions performed in daily environments using an inertial sensor network.https://doi.org/10.1177/1550147719894532 |
| spellingShingle | Irvin Hussein Lopez-Nava Angélica Muñoz-Meléndez Human action recognition based on low- and high-level data from wearable inertial sensors International Journal of Distributed Sensor Networks |
| title | Human action recognition based on low- and high-level data from wearable inertial sensors |
| title_full | Human action recognition based on low- and high-level data from wearable inertial sensors |
| title_fullStr | Human action recognition based on low- and high-level data from wearable inertial sensors |
| title_full_unstemmed | Human action recognition based on low- and high-level data from wearable inertial sensors |
| title_short | Human action recognition based on low- and high-level data from wearable inertial sensors |
| title_sort | human action recognition based on low and high level data from wearable inertial sensors |
| url | https://doi.org/10.1177/1550147719894532 |
| work_keys_str_mv | AT irvinhusseinlopeznava humanactionrecognitionbasedonlowandhighleveldatafromwearableinertialsensors AT angelicamunozmelendez humanactionrecognitionbasedonlowandhighleveldatafromwearableinertialsensors |