Validation of Novel Relative Orientation and Inertial Sensor-to-Segment Alignment Algorithms for Estimating 3D Hip Joint Angles

Adamowicz, Lukas; Gurchiek, Reed D.; Ferri, Jonathan; Ursiny, Anna T.; Fiorentino, Niccolo M.; McGinnis, Ryan S.

doi:10.3390/s19235143

Cited by 24 publications

(24 citation statements)

References 47 publications

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“…Growing interest in the potential of IMUs has led to a number of validation studies that aim to assess the accuracy of joint kinematics calculated using these wearable sensors. Typically, accuracy has been quantified by comparing joint angles calculated using IMU systems to optical motion capture through root mean squared error (RMSE) [ 10 , 11 ], correlation coefficients [ 12 , 13 ], and/or Bland-Altman limits of agreement [ 14 , 15 ]. Efforts have largely been focused on IMU system validation for lower limb angles during gait [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of an IMU Suit for Military-Based Tasks

Mavor

Ross

Clouthier

et al. 2020

Sensors

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Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements.

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Section: Introductionmentioning

confidence: 99%

Validation of an IMU Suit for Military-Based Tasks

Mavor

Ross

Clouthier

et al. 2020

Sensors

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“…In recent years, there has been increased interest in finding alternatives for the evaluation of mobility, among which inertial measurement units (IMUs) stand out because of their portability, size, and relatively low price [ 1 ]. Most publications that include a validation of an IMU compare its performance with optical motion-capture systems [ 2 , 3 , 4 , 5 , 6 ]. So far, the gold standards for gait analysis are optical motion capture systems, force platforms, and plantar pressure platforms, but these systems are expensive, space limited, and time consuming due to the placement of markers on the test subject.…”

Section: Introductionmentioning

confidence: 99%

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

Posada-Ordax

Cosín-Matamoros

Iglesias

et al. 2021

JCM

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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.

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“…The overview of IMU motion-capture technologies mentioned above is given in Table 2. Several studies comparing IMU-based motion-capture systems with optical systems for different regions of the body have been carried out in the literature [3][4][5][6][7][8][9][10]. The focus of our research is a universal, whole-body motion-capture system using wearable IMU sensors.…”

Section: Introductionmentioning

confidence: 99%

Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

Ancāns

Greitāns

Cacurs

et al. 2021

Sensors

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This paper presents a wearable wireless system for measuring human body activities, consisting of small inertial sensor nodes and the main hub for data transmission via Bluetooth for further analysis. Unlike optical and ultrasonic technologies, the proposed solution has no movement restrictions, such as the requirement to stay in the line of sight, and it provides information on the dynamics of the human body’s poses regardless of its location. The problem of the correct placement of sensors on the body is considered, a simplified architecture of the wearable clothing is described, an experimental set-up is developed and tests are performed. The system has been tested by performing several physical exercises and comparing the performance with the commercially available BTS Bioengineering SMART DX motion capture system. The results show that our solution is more suitable for complex exercises as the system based on digital cameras tends to lose some markers. The proposed wearable sensor clothing can be used as a multi-purpose data acquisition device for application-specific data analysis, thus providing an automated tool for scientists and doctors to measure patient’s body movements.

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Validation of Novel Relative Orientation and Inertial Sensor-to-Segment Alignment Algorithms for Estimating 3D Hip Joint Angles

Cited by 24 publications

References 47 publications

Validation of an IMU Suit for Military-Based Tasks

Validation of an IMU Suit for Military-Based Tasks

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

Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

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