A Real-Time Kinect Signature-Based Patient Home Monitoring System
Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect (“Kinect”) system has been recently used to estimate...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/11/1965 |
| _version_ | 1798035315007422464 |
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| author | Gaddi Blumrosen Yael Miron Nathan Intrator Meir Plotnik |
| author_facet | Gaddi Blumrosen Yael Miron Nathan Intrator Meir Plotnik |
| author_sort | Gaddi Blumrosen |
| collection | DOAJ |
| description | Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect (“Kinect”) system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints’ estimations, and erroneous multiplexing of different subjects’ estimations to one. This study addresses these limitations by incorporating a set of features that create a unique “Kinect Signature”. The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities. |
| first_indexed | 2024-04-11T20:56:31Z |
| format | Article |
| id | doaj.art-6e553253dc9b486f848072613f7273d5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:56:31Z |
| publishDate | 2016-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6e553253dc9b486f848072613f7273d52022-12-22T04:03:40ZengMDPI AGSensors1424-82202016-11-011611196510.3390/s16111965s16111965A Real-Time Kinect Signature-Based Patient Home Monitoring SystemGaddi Blumrosen0Yael Miron1Nathan Intrator2Meir Plotnik3Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, IsraelCenter of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan 52621, IsraelBlavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, IsraelCenter of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan 52621, IsraelAssessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect (“Kinect”) system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints’ estimations, and erroneous multiplexing of different subjects’ estimations to one. This study addresses these limitations by incorporating a set of features that create a unique “Kinect Signature”. The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities.http://www.mdpi.com/1424-8220/16/11/1965Kinectmotion trackinggait analysisartifact detection |
| spellingShingle | Gaddi Blumrosen Yael Miron Nathan Intrator Meir Plotnik A Real-Time Kinect Signature-Based Patient Home Monitoring System Sensors Kinect motion tracking gait analysis artifact detection |
| title | A Real-Time Kinect Signature-Based Patient Home Monitoring System |
| title_full | A Real-Time Kinect Signature-Based Patient Home Monitoring System |
| title_fullStr | A Real-Time Kinect Signature-Based Patient Home Monitoring System |
| title_full_unstemmed | A Real-Time Kinect Signature-Based Patient Home Monitoring System |
| title_short | A Real-Time Kinect Signature-Based Patient Home Monitoring System |
| title_sort | real time kinect signature based patient home monitoring system |
| topic | Kinect motion tracking gait analysis artifact detection |
| url | http://www.mdpi.com/1424-8220/16/11/1965 |
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