A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices

Allseits, Eric; Kim, Kyoung Jae; Bennett, Christopher L.; Gailey, Robert; Gaunaurd, Ignacio; Agrawal, Vibhor

doi:10.3390/s18092759

Cited by 33 publications

(21 citation statements)

References 19 publications

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“…However, we attempted to limit this using Co-Flex bands. A study conducted by Allseits et al, using IMUs in gait analysis, also noted the unwarranted affect that noise due to sensor motion may play in proper data analysis [38]. Further, while the effect should not drastically alter any potential findings within our study, further research into the potential effect of sensor motion noise should be done.…”

Section: Limitationsmentioning

confidence: 63%

Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement

Ajdaroski

Tadakala

Nichols

et al. 2020

Sensors

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Participation in sports has risen in the United States over the last few years, increasing the risk of injuries such as tears to the anterior cruciate ligament (ACL) in the knee. Previous studies have shown a correlation between knee kinematics when landing from a jump and this injury. The purpose of this study was to validate the ability of a commercially available inertial measurement units (IMUs) to accurately measure knee joint angles during a dynamic movement. Eight healthy subjects participated in the study. Validation was performed by comparing the angles measured by the wearable device to those obtained through the gold standard motion capture system when landing from a jump. Root mean square, linear regression analysis, and Bland–Altman plots were performed/constructed. The mean difference between the wearable device and the motion capture data was 8.4° (flexion/extension), 4.9° (ab/adduction), and 3.9° (rotation). In addition, the device was more accurate at smaller knee angles. In our study, a commercially available wearable IMU was able to perform fairly well under certain conditions and was less accurate in other conditions.

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Section: Limitationsmentioning

confidence: 63%

Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement

Ajdaroski

Tadakala

Nichols

et al. 2020

Sensors

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“…The present paper is devoted to the analysis of motion features [17] based upon the analysis of the signals recorded by accelerometers and gyrometers [18] located inside wearable devices, such as mobile phones [19,20] and tablets. These sensors are often used for gait analysis [21][22][23] or for monitoring physical activities [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

et al. 2019

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Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use of microelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities.

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“…The main finding of this study was that there was a strong relationship between the capacitance and knee angles for seven out of the nine participants with high R 2 and a RMSE below 5°. This is clinically important as 5° has been considered as a clinically acceptable level of error in knee angle measurement [1,25,26]. However, participant 1 and participant 8 (Figure 3) presented with high RMSE greater than 5°.…”

Section: Discussionmentioning

confidence: 93%

Can stretch sensors measure knee range of motion in healthy adults?

Poomsalood

Muthumayandi

Hambly

2019

Biomedical Human Kinetics

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Study aim: There are currently limited methods available to access dynamic knee range of motion (ROM) during free-living activities. This type of method would be valuable for monitoring and progressing knee rehabilitation. Therefore, the aim of this study was to evaluate the functioning of stretch sensors for the measurement of knee ROM and to assess the level of the measurement error. Material and methods: Nine healthy participants were included in the study. Three stretch sensors (StretchSense™, Auckland, NZ) were attached on the participants’ right knees by Kinesiotape®. A Cybex dynamometer was used to standardise movement speed of the knee joint. Data was recorded through the StretchSense™ BLE application. Knee angles were obtained from the video clips recorded during the testing and were analysed by MaxTraq® 2D motion analysis software. The knee angles were then synchronised with the sensor capacitance through R programme. Results: Seven out of the nine participants presented with high coefficient of determination (R2) (>0.98) and low root mean square error (RMSE) (<5°) between the sensor capacitance and knee angle. Two participants did not confirm good relationship between capacitance and knee angle as they presented high RMSE (>5°). The equations generated from these 7 participants’ data were used individually to predict knee angles. Conclusions: The stretch sensors can be used to measure knee ROM in healthy adults during a passive, non-weight-bearing movement with a clinically acceptable level of error. Further research is needed to establish the validity and reliability of the methodology under different conditions before considered within a clinical setting.

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A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices

Cited by 33 publications

References 19 publications

Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement

Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Can stretch sensors measure knee range of motion in healthy adults?

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