Stride length determination during overground running using a single foot-mounted inertial measurement unit

2018 ◽  
Vol 71 ◽  
pp. 302-305 ◽  
Author(s):  
C. Markus Brahms ◽  
Yang Zhao ◽  
David Gerhard ◽  
John M. Barden
Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2896
Author(s):  
Pratham Singh ◽  
Michael Esposito ◽  
Zach Barrons ◽  
Christian A. Clermont ◽  
John Wannop ◽  
...  

One possible modality to profile gait speed and stride length includes using wearable technologies. Wearable technology using global positioning system (GPS) receivers may not be a feasible means to measure gait speed. An alternative may include a local positioning system (LPS). Considering that LPS wearables are not good at determining gait events such as heel strikes, applying sensor fusion with an inertial measurement unit (IMU) may be beneficial. Speed and stride length determined from an ultrawide bandwidth LPS equipped with an IMU were compared to video motion capture (i.e., the “gold standard”) as the criterion standard. Ninety participants performed trials at three self-selected walk, run and sprint speeds. After processing location, speed and acceleration data from the measurement systems, speed between the last five meters and stride length in the last stride of the trial were analyzed. Small biases and strong positive intraclass correlations (0.9–1.0) between the LPS and “the gold standard” were found. The significance of the study is that the LPS can be a valid method to determine speed and stride length. Variability of speed and stride length can be reduced when exploring data processing methods that can better extract speed and stride length measurements.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yufeng Mao ◽  
Taiki Ogata ◽  
Hiroki Ora ◽  
Naoto Tanaka ◽  
Yoshihiro Miyake

AbstractInertial measurement unit (IMU)-based gait analysis systems have become popular in clinical environments because of their low cost and quantitative measurement capability. When a shank is selected as the IMU mounting position, an inverted pendulum model (IPM) can accurately estimate its spatial gait parameters. However, the stride-by-stride estimation of gait parameters using one IMU on each shank and the IPMs has not been validated. This study validated a spatial gait parameter estimation method using a shank-based IMU system. Spatial parameters were estimated via the double integration of the linear acceleration transformed by the IMU orientation information. To reduce the integral drift error, an IPM, applied with a linear error model, was introduced at the mid-stance to estimate the update velocity. the gait data of 16 healthy participants that walked normally and slowly were used. The results were validated by comparison with those extracted from an optical motion-capture system; the results showed strong correlation ($$r>0.9$$ r > 0.9 ) and good agreement with the gait metrics (stride length, stride velocity, and shank vertical displacement). In addition, the biases of the stride length and stride velocity extracted using the motion capture system were smaller in the IPM than those in the previous method using the zero-velocity-update. The error variabilities of the gait metrics were smaller in the IPM than those in the previous method. These results indicated that the reconstructed shank trajectory achieved a greater accuracy and precision than that of previous methods. This was attributed to the IPM, which demonstrates that shank-based IMU systems with IPMs can accurately reflect many spatial gait parameters including stride velocity.


Motor Control ◽  
2021 ◽  
Vol 25 (1) ◽  
pp. 89-99
Author(s):  
Luca Correale ◽  
Vittoria Carnevale Pellino ◽  
Luca Marin ◽  
Massimiliano Febbi ◽  
Matteo Vandoni

Spatiotemporal parameters of walking are used to identify gait impairments and provide a tailored therapy program. Baropodometric platforms are not often used for measuring spatiotemporal parameters and walking speed and it is required to determine accuracy. The aim of this study was to compare FreeMed® Platform gait outcomes with a validated inertial measurement unit. There were 40 healthy adults without walking impairments enrolled. Each subject walked along a 15-m walkway at self and slow self-selected speed wearing an inertial measurement unit on the FreeMed® Platform. Stride length and time, right and left stance, swing time, and walking speed were recorded. Walking speed, stride length, and step time showed a very high level of agreement at slow walking speed and a high and moderate level of agreement at normal walking speed. FreeMed® Platform is useful to assess gait outcomes and could improve the exercise prescription.


Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.


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