scholarly journals Effect on kinematic gait variables of two methods of inertial measurement unit sensor attachment to the extremities of horses under controlled conditions of treadmill exercise in sound horses at the walk and trot: A pilot study

2018 ◽  
Vol 34 (4) ◽  
pp. 333-340
Author(s):  
T Seghers ◽  
U E Maninchedda ◽  
B Vidondo ◽  
A Ramseyer ◽  
A M Cruz
Keyword(s):  
Pilot Study ◽  
Inertial Measurement Unit ◽  
Treadmill Exercise ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Controlled Conditions

scholarly journals Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

2021 ◽  
Vol 10 (9) ◽  
pp. 1804
Author(s):  
Jorge Posada-Ordax ◽  
Julia Cosin-Matamoros ◽  
Marta Elena Losa-Iglesias ◽  
Ricardo Becerro-de-Bengoa-Vallejo ◽  
Laura Esteban-Gonzalo ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
High Speeds ◽  
Controlled Conditions ◽  
Gait Parameters ◽  
Measurement Units ◽  
Accuracy And Repeatability

In recent years, interest in finding alternatives for the evaluation of mobility has increased. Inertial measurement units (IMUs) stand out for their portability, size, and low price. The objective of this study was to examine the accuracy and repeatability of a commercially available IMU under controlled conditions in healthy subjects. A total of 36 subjects, including 17 males and 19 females were analyzed with a Wiva Science IMU in a corridor test while walking for 10 m and in a threadmill at 1.6 km/h, 2.4 km/h, 3.2 km/h, 4 km/h, and 4.8 km/h for one minute. We found no difference when we compared the variables at 4 km/h and 4.8 km/h. However, we found greater differences and errors at 1.6 km/h, 2.4 km/h and 3.2 km/h, and the latter one (1.6 km/h) generated more error. The main conclusion is that the Wiva Science IMU is reliable at high speeds but loses reliability at low speeds.

scholarly journals Inter-evaluator and Intra-evaluator Reliability of a Software Program Used to Extract Kinematic Variables Obtained by an Extremity-Mounted Inertial Measurement Unit System in Sound Horses at the Trot Under Soft and Hard Ground Conditions and Treadmill Exercise

2021 ◽  
Vol 8 ◽  
Author(s):  
Julia Schwarz ◽  
Beatriz Vidondo ◽  
Ugo E. Maninchedda ◽  
Miriam Sprick ◽  
Melina C. Schöpfer ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Treadmill Exercise ◽  
Correlation Coefficients ◽  
Variance Partitioning ◽  
Measurement Unit ◽  
Analysis Tool ◽  
Inertial Measurement ◽  
Unit System ◽  
Software Program ◽  
Ground Conditions

Objective: To assess the inter-evaluator and intra-evaluator reliability of a software program used to extract kinematic variables by a commercially available extremity-mounted inertial measurement unit system in sound horses at the trot under soft and hard ground conditions and treadmill exercise.Animals: Thirty adult, sound and healthy French Montagne stallions.Procedures: Data collection was performed with six IMUs strapped to the distal, metacarpal, metatarsal and tibial regions of every horse. Per surface (treadmill, soft and hard ground) 10 stallions were trotted three times. Prior to the analysis done by six evaluators (three experienced, three inexperienced) the data was blinded and copied three times. For every analysis a minimum of five strides had to be selected. To assess the intra- and inter-evaluator reliability a selection of gait variables was used to calculate intra and inter correlation coefficients (ICCs) as well as variance partitioning coefficients (VPCs).Results: All of the tested gait variables showed high levels of reliability. There was no mentionable difference considering the correlation coefficients between the intra and inter reliability as well as between the three different surfaces. VPCs showed that the factor horse is by far the most responsible for any appearing variance. The experience of the evaluator had no influence on the results.Conclusions and Clinical Relevance: The software program tested in this study has a high inter- and intra-evaluator reliability under the chosen conditions for the selected variables and acts independent of the ground situation and the experience of the evaluator. On the condition of a correct application it has the potential to become a clinically relevant and reliable gait analysis tool.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

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.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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