observation. 12 , 13 Objective measurement of kinematics outside the laboratory requires wearable sensors that are easy to apply, unobtrusive, and reach a level of accuracy sufficient to answer the study question. Inertial measurement units (IMUs) have grown in popularity for the measurement of joint motion
Melissa M.B. Morrow, Bethany Lowndes, Emma Fortune, Kenton R. Kaufman and M. Susan Hallbeck
Live S. Luteberget, Benjamin R. Holme and Matt Spencer
indoors, thus not useable for indoors sports such as team handball. In recent years, an inertial measurement unit (IMU) has been integrated into GPS devices, to provide additional information relating to physical loads during games and training. IMUs consist of the inertial sensors accelerometers and
Andreas Krüger and Jürgen Edelmann-Nusser
This study aims at determining the accuracy of a full body inertial measurement system in a real skiing environment in comparison with an optical video based system. Recent studies have shown the use of inertial measurement systems for the determination of kinematical parameters in alpine skiing. However, a quantitative validation of a full body inertial measurement system for the application in alpine skiing is so far not available. For the purpose of this study, a skier performed a test-run equipped with a full body inertial measurement system in combination with a DGPS. In addition, one turn of the test-run was analyzed by an optical video based system. With respect to the analyzed angles, a maximum mean difference of 4.9° was measured. No differences in the measured angles between the inertial measurement system and the combined usage with a DGPS were found. Concerning the determination of the skier’s trajectory, an additional system (e.g., DGPS) must be used. As opposed to optical methods, the main advantages of the inertial measurement system are the determination of kinematical parameters without the limitation of restricted capture volume, and small time costs for the measurement preparation and data analysis.
Timo Rantalainen, Nicolas H. Hart, Sophia Nimphius and Daniel W. Wundersitz
Inertial measurement units (IMU) provide a convenient tool for gait stability assessment. However, it is unclear how various gait characteristics relate to each other and whether gait characteristics can be obtained from resultant acceleration. Therefore, step duration variability was measured in treadmill walking from 39 young ambulant volunteers (age 24.2 [± 2.5] y; height 1.79 [± 0.09] m; mass 71.6 [± 12.0] kg) using motion capture. Accelerations and gyrations were simultaneously recorded with an IMU. Harmonic ratio, maximum Lyapunov exponents, and multiscale sample entropy (MSE) were calculated. Step duration variability was positively associated with MSE with coarseness levels = 3–6 (r = –.33 to –.42, P ≤ .045). Harmonic ratio and MSE with all coarseness levels were negatively associated (r = –.45 to –.57, P ≤ .004). The MSE with coarseness level = 2 was negatively associated with short-term maximum Lyapunov exponents (r = –.32, P = .047). The agreement between resultant and vertical acceleration derived gait characteristics was excellent (ICC = 0.97–0.99). In conclusion, MSE with varying coarseness levels was associated with the other gait characteristics evaluated in the study. Resultant and vertical acceleration derived results had excellent agreement, which suggests that resultant acceleration is a viable alternative to considering the acceleration dimensions independently.
Håvard Myklebust, Øyvind Gløersen and Jostein Hallén
In regard to simplifying motion analysis and estimating center of mass (COM) in ski skating, this study addressed 3 main questions concerning the use of inertial measurement units (IMU): (1) How accurately can a single IMU estimate displacement of os sacrum (S1) on a person during ski skating? (2) Does incorporating gyroscope and accelerometer data increase accuracy and precision? (3) Moreover, how accurately does S1 determine COM displacement? Six world-class skiers roller-ski skated on a treadmill using 2 different subtechniques. An IMU including accelerometers alone (IMU-A) or in combination with gyroscopes (IMU-G) were mounted on the S1. A reflective marker at S1, and COM calculated from 3D full-body optical analysis, were used to provide reference values. IMU-A provided an accurate and precise estimate of vertical S1 displacement, but IMU-G was required to attain accuracy and precision of < 8 mm (root-mean-squared error and range of displacement deviation) in all directions and with both subtechniques. Further, arm and torso movements affected COM, but not the S1. Hence, S1 displacement was valid for estimating sideways COM displacement, but the systematic amplitude and timing difference between S1 and COM displacement in the anteroposterior and vertical directions inhibits exact calculation of energy fluctuations.
Elena Bergamini, Pélagie Guillon, Valentina Camomilla, Hélène Pillet, Wafa Skalli and Aurelio Cappozzo
The proper execution of the sprint start is crucial in determining the performance during a sprint race. In this respect, when moving from the crouch to the upright position, trunk kinematics is a key element. The purpose of this study was to validate the use of a trunk-mounted inertial measurement unit (IMU) in estimating the trunk inclination and angular velocity in the sagittal plane during the sprint start. In-laboratory sprint starts were performed by five sprinters. The local acceleration and angular velocity components provided by the IMU were processed using an adaptive Kalman filter. The accuracy of the IMU inclination estimate and its consistency with trunk inclination were assessed using reference stereophotogrammetric measurements. A Bland-Altman analysis, carried out using parameters (minimum, maximum, and mean values) extracted from the time histories of the estimated variables, and curve similarity analysis (correlation coefficient > 0.99, root mean square difference < 7 deg) indicated the agreement between reference and IMU estimates, opening a promising scenario for an accurate in-field use of IMUs for sprint start performance assessment.
José Pino-Ortega, Alejandro Hernández-Belmonte, Carlos D. Gómez-Carmona, Alejandro Bastida-Castillo, Javier García-Rubio and Sergio J. Ibáñez
movement alteration or postural adjustments. 14 Advances in technology have facilitated the development of an inertial measurement unit (IMU) that is composed of different sensors (accelerometers, gyroscopes, magnetometers, etc) in the same device. 15 Most sensors that make up these devices are capable of
M. Monda, A. Goldberg, P. Smitham, M. Thornton and I. McCarthy
To study mobility in older populations it can be advantageous to use portable gait analysis systems, such as inertial measurement units (IMUs), which can be used in the community. To define a normal range, 136 active subjects were recruited with an age range of 18 to 97. Four IMUs were attached to the subjects, one on each thigh and shank. Subjects were asked to walk 10 m at their own self-selected speed. The ranges of motion of thigh, shank, and knee in both swing and stance phase were calculated, in addition to stride duration. Thigh, shank, and knee range of movement in swing and stance were significantly different only in the > 80 age group. Regressions of angle against age showed a cubic relationship. Stride duration showed a weak linear relationship with age, increasing by approximately 0.1% per year.
Nick B. Murray, Georgia M. Black, Rod J. Whiteley, Peter Gahan, Michael H. Cole, Andy Utting and Tim J. Gabbett
Throwing loads are known to be closely related to injury risk. However, for logistic reasons, typically only pitchers have their throws counted, and then only during innings. Accordingly, all other throws made are not counted, so estimates of throws made by players may be inaccurately recorded and underreported. A potential solution to this is the use of wearable microtechnology to automatically detect, quantify, and report pitch counts in baseball. This study investigated the accuracy of detection of baseball pitching and throwing in both practice and competition using a commercially available wearable microtechnology unit.
Seventeen elite youth baseball players (mean ± SD age 16.5 ± 0.8 y, height 184.1 ± 5.5 cm, mass 78.3 ± 7.7 kg) participated in this study. Participants performed pitching, fielding, and throwing during practice and competition while wearing a microtechnology unit. Sensitivity and specificity of a pitching and throwing algorithm were determined by comparing automatic measures (ie, microtechnology unit) with direct measures (ie, manually recorded pitching counts).
The pitching and throwing algorithm was sensitive during both practice (100%) and competition (100%). Specificity was poorer during both practice (79.8%) and competition (74.4%).
These findings demonstrate that the microtechnology unit is sensitive to detect pitching and throwing events, but further development of the pitching algorithm is required to accurately and consistently quantify throwing loads using microtechnology.
Blake D. McLean, Cloe Cummins, Greta Conlan, Grant Duthie and Aaron J. Coutts
Purpose: To determine the relationship between drill type and accelerometer-derived loads during various team-sport activities and examine the influence of unit fitting on these loads. Methods: Sixteen rugby league players were fitted with microtechnology devices in either manufacturer vests or playing jerseys before completing standardized running, agility, and tackling drills. Two-dimensional (2D) and 3-dimensional (3D) accelerometer loads (BodyLoad™) per kilometer were compared across drills and fittings (ie, vest and jersey). Results: When fitted in a vest, 2D BodyLoad was higher during tackling (21.5 [14.8] AU/km) than during running (9.5 [2.5] AU/km) and agility (10.3 [2.7] AU/km). Jersey fitting resulted in more than 2-fold higher BodyLoad during running (2D = 9.5 [2.7] vs 29.3 [14.8] AU/km, 3D = 48.5 [14.8] vs 111.5 [45.4] AU/km) and agility (2D = 10.3 [2.7] vs 21.0 [8.1] AU/km, 3D = 40.4 [13.6] vs 77.7 [26.8] AU/km) compared with a vest fitting. Jersey fitting also produced higher BodyLoad during tackling drills (2D = 21.5 [14.8] vs 27.8 [18.6] AU/km, 3D = 42.0 [21.4] vs 63.2 [33.1] AU/km). Conclusions: This study provides evidence supporting the construct validity of 2D BodyLoad for assessing collision/tackling load in rugby league training drills. Conversely, the large values obtained from 3D BodyLoad (which includes the vertical load vector) appear to mask small increases in load during tackling drills, rendering 3D BodyLoad insensitive to changes in contact load. Unit fitting has a large influence on accumulated accelerometer loads during all drills, which is likely related to greater incidental unit movement when units are fitted in jerseys. Therefore, it is recommended that athletes wear microtechnology units in manufacturer-provided vests to provide valid and reliable information.