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
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.
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
Luca Correale, Vittoria Carnevale Pellino, Luca Marin, Massimiliano Febbi, and Matteo Vandoni
precision but are expensive, technically difficult to use, and labor-intensive, therefore not easily applicable to clinical settings ( Panero et al., 2018 ). The introduction of inertial measurement unit (IMU) sensors, such as Physilog5 (Gait Up SA, Lausanne, Switzerland) ( Mariani et al., 2010 ), combining
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.
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.
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.
Jonathan S. Dufour, Alexander M. Aurand, Eric B. Weston, Christopher N. Haritos, Reid A. Souchereau, and William S. Marras
– 12 Broadly, technologies that have emerged to capture and assess human motion include (1) markered optical motion capture, (2) markerless optical motion capture, and (3) inertial measurement unit (IMU) sensors. Each technology has significant advantages and disadvantages. Often considered the “gold
Ludovic Seifert, Dominic Orth, Jérémie Boulanger, Vladislavs Dovgalecs, Romain Hérault, and Keith Davids
This study investigated a new performance indicator to assess climbing fluency (smoothness of the hip trajectory and orientation of a climber using normalized jerk coefficients) to explore effects of practice and hold design on performance. Eight experienced climbers completed four repetitions of two, 10-m high routes with similar difficulty levels, but varying in hold graspability (holds with one edge vs holds with two edges). An inertial measurement unit was attached to the hips of each climber to collect 3D acceleration and 3D orientation data to compute jerk coefficients. Results showed high correlations (r = .99, P < .05) between the normalized jerk coefficient of hip trajectory and orientation. Results showed higher normalized jerk coefficients for the route with two graspable edges, perhaps due to more complex route finding and action regulation behaviors. This effect decreased with practice. Jerk coefficient of hip trajectory and orientation could be a useful indicator of climbing fluency for coaches as its computation takes into account both spatial and temporal parameters (ie, changes in both climbing trajectory and time to travel this trajectory).