The real-time elderly fall posture identifying scheme with wearable sensors
The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2019-11-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719885616 |
| _version_ | 1797712246432858112 |
|---|---|
| author | Tao Xu Wei Sun Shaowei Lu Ke-ming Ma Xiaoqiang Wang |
| author_facet | Tao Xu Wei Sun Shaowei Lu Ke-ming Ma Xiaoqiang Wang |
| author_sort | Tao Xu |
| collection | DOAJ |
| description | The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message. |
| first_indexed | 2024-03-12T07:18:55Z |
| format | Article |
| id | doaj.art-1f554888468c4c4f953e4cdb4c7ed424 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T07:18:55Z |
| publishDate | 2019-11-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-1f554888468c4c4f953e4cdb4c7ed4242023-09-02T22:37:41ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772019-11-011510.1177/1550147719885616The real-time elderly fall posture identifying scheme with wearable sensorsTao Xu0Wei Sun1Shaowei Lu2Ke-ming Ma3Xiaoqiang Wang4School of Automation, Shenyang Aerospace University (SAU), Shenyang, ChinaSchool of Automation, Shenyang Aerospace University (SAU), Shenyang, ChinaCollege of Material Science and Engineering, Shenyang Aerospace University (SAU), Shenyang, ChinaCollege of Material Science and Engineering, Shenyang Aerospace University (SAU), Shenyang, ChinaDepartment of Aeronautics, College of Aerospace Engineering, Shenyang Aerospace University (SAU), Shenyang, ChinaThe accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.https://doi.org/10.1177/1550147719885616 |
| spellingShingle | Tao Xu Wei Sun Shaowei Lu Ke-ming Ma Xiaoqiang Wang The real-time elderly fall posture identifying scheme with wearable sensors International Journal of Distributed Sensor Networks |
| title | The real-time elderly fall posture identifying scheme with wearable sensors |
| title_full | The real-time elderly fall posture identifying scheme with wearable sensors |
| title_fullStr | The real-time elderly fall posture identifying scheme with wearable sensors |
| title_full_unstemmed | The real-time elderly fall posture identifying scheme with wearable sensors |
| title_short | The real-time elderly fall posture identifying scheme with wearable sensors |
| title_sort | real time elderly fall posture identifying scheme with wearable sensors |
| url | https://doi.org/10.1177/1550147719885616 |
| work_keys_str_mv | AT taoxu therealtimeelderlyfallpostureidentifyingschemewithwearablesensors AT weisun therealtimeelderlyfallpostureidentifyingschemewithwearablesensors AT shaoweilu therealtimeelderlyfallpostureidentifyingschemewithwearablesensors AT kemingma therealtimeelderlyfallpostureidentifyingschemewithwearablesensors AT xiaoqiangwang therealtimeelderlyfallpostureidentifyingschemewithwearablesensors AT taoxu realtimeelderlyfallpostureidentifyingschemewithwearablesensors AT weisun realtimeelderlyfallpostureidentifyingschemewithwearablesensors AT shaoweilu realtimeelderlyfallpostureidentifyingschemewithwearablesensors AT kemingma realtimeelderlyfallpostureidentifyingschemewithwearablesensors AT xiaoqiangwang realtimeelderlyfallpostureidentifyingschemewithwearablesensors |