Abstract
Preserving mobility, the ability to keep a correct posture and dynamic balance in order to walk properly, is fundamental to maintain autonomy in daily life. Based on the correlation between muscle groups and autonomy, previous research has suggested that maintaining muscular tone in knee extensors is critical. Continuous training of knee extensors during aging is therefore essential to maintain independence. In this work, it is hypothesized that it is possible to estimate knee extensor activity only from IMU data based on a simple lower limbs model. The accuracy of the knee extensor activity estimation algorithm has been tested using sEMG measurements as control data on three different walking patterns: normal walk, fast walk and stair climbing. Estimated knee torque area and measured muscular activity for each step were compared confirming a high estimation accuracy with a correlation efficient R=0.80. Moreover, muscular activity can be divided based on intensity in three groups of statistically significant difference confirmed by the Steel-Dwass method. Future works should test the usability of the algorithm for different walking patterns, and use the collected data and the refined algorithm to implement a smart resistive device to increase knee extensor exertion during each walking pattern to the level necessary for sufficient extensor training.
| Original language | English |
|---|---|
| Title of host publication | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1554-1557 |
| Number of pages | 4 |
| Volume | 2018-July |
| ISBN (Electronic) | 9781538636466 |
| DOIs | |
| Publication status | Published - 2018 Oct 26 |
| Event | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States Duration: 2018 Jul 18 → 2018 Jul 21 |
Other
| Other | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 18/7/18 → 18/7/21 |
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ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
Knee extensor muscular activity estimation during different walking patterns : Flat normal and brisk walking, stair climbing. / Cosentino, Sarah; Kasai, R.; Gu, Z.; Sessa, S.; Kawakami, Yasuo; Takanishi, Atsuo.
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. p. 1554-1557 8512518.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Knee extensor muscular activity estimation during different walking patterns
T2 - Flat normal and brisk walking, stair climbing
AU - Cosentino, Sarah
AU - Kasai, R.
AU - Gu, Z.
AU - Sessa, S.
AU - Kawakami, Yasuo
AU - Takanishi, Atsuo
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Preserving mobility, the ability to keep a correct posture and dynamic balance in order to walk properly, is fundamental to maintain autonomy in daily life. Based on the correlation between muscle groups and autonomy, previous research has suggested that maintaining muscular tone in knee extensors is critical. Continuous training of knee extensors during aging is therefore essential to maintain independence. In this work, it is hypothesized that it is possible to estimate knee extensor activity only from IMU data based on a simple lower limbs model. The accuracy of the knee extensor activity estimation algorithm has been tested using sEMG measurements as control data on three different walking patterns: normal walk, fast walk and stair climbing. Estimated knee torque area and measured muscular activity for each step were compared confirming a high estimation accuracy with a correlation efficient R=0.80. Moreover, muscular activity can be divided based on intensity in three groups of statistically significant difference confirmed by the Steel-Dwass method. Future works should test the usability of the algorithm for different walking patterns, and use the collected data and the refined algorithm to implement a smart resistive device to increase knee extensor exertion during each walking pattern to the level necessary for sufficient extensor training.
AB - Preserving mobility, the ability to keep a correct posture and dynamic balance in order to walk properly, is fundamental to maintain autonomy in daily life. Based on the correlation between muscle groups and autonomy, previous research has suggested that maintaining muscular tone in knee extensors is critical. Continuous training of knee extensors during aging is therefore essential to maintain independence. In this work, it is hypothesized that it is possible to estimate knee extensor activity only from IMU data based on a simple lower limbs model. The accuracy of the knee extensor activity estimation algorithm has been tested using sEMG measurements as control data on three different walking patterns: normal walk, fast walk and stair climbing. Estimated knee torque area and measured muscular activity for each step were compared confirming a high estimation accuracy with a correlation efficient R=0.80. Moreover, muscular activity can be divided based on intensity in three groups of statistically significant difference confirmed by the Steel-Dwass method. Future works should test the usability of the algorithm for different walking patterns, and use the collected data and the refined algorithm to implement a smart resistive device to increase knee extensor exertion during each walking pattern to the level necessary for sufficient extensor training.
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U2 - 10.1109/EMBC.2018.8512518
DO - 10.1109/EMBC.2018.8512518
M3 - Conference contribution
VL - 2018-July
SP - 1554
EP - 1557
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -