Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters
Step length is a critical hallmark of health status. However, few studies have investigated the modifiable factors that may affect step length. An exploratory, cross-sectional study was performed to evaluate the surface electromyography (sEMG) and body impedance analysis (BIA) parameters, combined w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5686 |
| _version_ | 1797432615170473984 |
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| author | Jin-Woo Park Seol-Hee Baek Joo Hye Sung Byung-Jo Kim |
| author_facet | Jin-Woo Park Seol-Hee Baek Joo Hye Sung Byung-Jo Kim |
| author_sort | Jin-Woo Park |
| collection | DOAJ |
| description | Step length is a critical hallmark of health status. However, few studies have investigated the modifiable factors that may affect step length. An exploratory, cross-sectional study was performed to evaluate the surface electromyography (sEMG) and body impedance analysis (BIA) parameters, combined with individual demographic data, to predict the individual step length using the GAITRite<sup>®</sup> system. Healthy participants aged 40–80 years were prospectively recruited, and three models were built to predict individual step length. The first model was the best-fit model (R<sup>2</sup> = 0.244, <i>p</i> < 0.001); the root mean square (RMS) values at maximal knee flexion and height were included as significant variables. The second model used all candidate variables, except sEMG variables, and revealed that age, height, and body fat mass (BFM) were significant variables for predicting the average step length (R<sup>2</sup> = 0.198, <i>p</i> < 0.001). The third model, which was used to predict step length without sEMG and BIA, showed that only age and height remained significant (R<sup>2</sup> = 0.158, <i>p</i> < 0.001). This study revealed that the RMS value at maximal strength knee flexion, height, age, and BFM are important predictors for individual step length, and possibly suggesting that strengthening knee flexor function and reducing BFM may help improve step length. |
| first_indexed | 2024-03-09T10:05:11Z |
| format | Article |
| id | doaj.art-1ce51b3230e04661aee3dfd24e0b8c17 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:05:11Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1ce51b3230e04661aee3dfd24e0b8c172023-12-01T23:09:58ZengMDPI AGSensors1424-82202022-07-012215568610.3390/s22155686Predictors of Step Length from Surface Electromyography and Body Impedance Analysis ParametersJin-Woo Park0Seol-Hee Baek1Joo Hye Sung2Byung-Jo Kim3Department of Neurology, Korea University Anam Hospital, Korea University Medicine, Seoul 02841, KoreaDepartment of Neurology, Korea University Anam Hospital, Korea University Medicine, Seoul 02841, KoreaDepartment of Neurology, Korea University Anam Hospital, Korea University Medicine, Seoul 02841, KoreaDepartment of Neurology, Korea University Anam Hospital, Korea University Medicine, Seoul 02841, KoreaStep length is a critical hallmark of health status. However, few studies have investigated the modifiable factors that may affect step length. An exploratory, cross-sectional study was performed to evaluate the surface electromyography (sEMG) and body impedance analysis (BIA) parameters, combined with individual demographic data, to predict the individual step length using the GAITRite<sup>®</sup> system. Healthy participants aged 40–80 years were prospectively recruited, and three models were built to predict individual step length. The first model was the best-fit model (R<sup>2</sup> = 0.244, <i>p</i> < 0.001); the root mean square (RMS) values at maximal knee flexion and height were included as significant variables. The second model used all candidate variables, except sEMG variables, and revealed that age, height, and body fat mass (BFM) were significant variables for predicting the average step length (R<sup>2</sup> = 0.198, <i>p</i> < 0.001). The third model, which was used to predict step length without sEMG and BIA, showed that only age and height remained significant (R<sup>2</sup> = 0.158, <i>p</i> < 0.001). This study revealed that the RMS value at maximal strength knee flexion, height, age, and BFM are important predictors for individual step length, and possibly suggesting that strengthening knee flexor function and reducing BFM may help improve step length.https://www.mdpi.com/1424-8220/22/15/5686step lengthsurface electromyographybody impedance analysis |
| spellingShingle | Jin-Woo Park Seol-Hee Baek Joo Hye Sung Byung-Jo Kim Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters Sensors step length surface electromyography body impedance analysis |
| title | Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters |
| title_full | Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters |
| title_fullStr | Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters |
| title_full_unstemmed | Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters |
| title_short | Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters |
| title_sort | predictors of step length from surface electromyography and body impedance analysis parameters |
| topic | step length surface electromyography body impedance analysis |
| url | https://www.mdpi.com/1424-8220/22/15/5686 |
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