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Yuji Matsuda, Yoshihisa Sakurai, Keita Akashi and Yasuyuki Kubo

In swimming, horizontal velocity fluctuations in the whole-body center of mass (CoM) are related to the energy cost. 1 , 2 Furthermore, variations in the CoM velocity, maximal and minimal CoM velocities in swimming direction during a stroke cycle, are related to the swimming performance. 2 , 3

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Shane R. Wurdeman, Jessie M. Huisinga, Mary Filipi and Nicholas Stergiou

Patients with multiple sclerosis (MS) have less-coordinated movements of the center of mass resulting in greater mechanical work. The purpose of this study was to quantify the work performed on the body’s center of mass by patients with MS. It was hypothesized that patients with MS would perform greater negative work during initial double support and less positive work in terminal double support. Results revealed that patients with MS perform less negative work in single support and early terminal double support and less positive work in the terminal double support period. However, summed over the entire stance phase, patients with MS and healthy controls performed similar amounts of positive and negative work on the body’s center of mass. The altered work throughout different periods in the stance phase may be indicative of a failure to capitalize on passive elastic energy mechanisms and increased reliance upon more active work generation to sustain gait.

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Richard N. Hinrichs, Peter R. Cavanagh and Keith R. Williams

Ten male recreational runners were filmed using three-dimensional cinematography while running on a treadmill at 3.8 m/s, 4.5 m/s, and 5.4 m/s. A 14-segment mathematical model was used to examine the influence of the arm swing on the three-dimensional motion of the body center of mass (CM), and on the vertical and horizontal propulsive impulses (“lift” and “drive”) on the body over the contact phase of the running cycle. The arms were found to reduce the horizontal excursions of the body CM both front to back and side to side, thus tending to make a runner's horizontal velocity more constant. The vertical range of motion of the body CM was increased by the action of the arms. The arms were found to make a small but important contribution to lift, roughly 5–10% of the total. This contribution increased with running speed. The arms were generally not found to contribute to drive, although considerable variation existed between subjects. Consistent with the CM results, the arms were found to reduce the changes in forward velocity of the runner rather than increasing them. It was concluded that there is no apparent advantage of the “classic” style of swinging the arms directly forward and backward over the style that most distance runners adopt of letting the arms cross over slightly in front. The crossover, in fact, helps reduce side-to-side excursions of the body CM mentioned above, hence promoting a more constant horizontal velocity.

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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.

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Kurt L. Mudie, Amitabh Gupta, Simon Green, Hiroaki Hobara and Peter J. Clothier

This study assessed the agreement between Kvert calculated from 4 different methods of estimating vertical displacement of the center of mass (COM) during single-leg hopping. Healthy participants (N = 38) completed a 10-s single-leg hopping effort on a force plate, with 3D motion of the lower limb, pelvis, and trunk captured. Derived variables were calculated for a total of 753 hop cycles using 4 methods, including: double integration of the vertical ground reaction force, law of falling bodies, a marker cluster on the sacrum, and a segmental analysis method. Bland-Altman plots demonstrated that Kvert calculated using segmental analysis and double integration methods have a relatively small bias (0.93 kN⋅m–1) and 95% limits of agreement (–1.89 to 3.75 kN⋅m–1). In contrast, a greater bias was revealed between sacral marker cluster and segmental analysis (–2.32 kN⋅m–1), sacral marker cluster and double integration (–3.25 kN⋅m–1), and the law of falling bodies compared with all methods (17.26–20.52 kN⋅m–1). These findings suggest the segmental analysis and double integration methods can be used interchangeably for the calculation of Kvert during single-leg hopping. The authors propose the segmental analysis method to be considered the gold standard for the calculation of Kvert during single-leg, on-the-spot hopping.

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Alberto Ranavolo, Romildo Don, Angelo Cacchio, Mariano Serrao, Marco Paoloni, Massimiliano Mangone and Valter Santilli

Kinematic and kinetic methods (sacral marker, reconstructed pelvis, segmental analysis, and force platform methods) have been used to calculate the vertical excursion of the center of mass (COM) during movement. In this study we compared the measurement of vertical COM displacement yielded by different methods during able-bodied subjects’ hopping at different frequencies (varying between 1.2 and 3.2 Hz). ANOVA revealed a significant interaction between hopping frequency and method (p < 0.001), showing that increasing hopping frequency reduced the differences between methods. A post hoc analysis revealed a significant difference between all methods at the lowest hopping frequency and between the force platform and both the sacral marker and reconstructed pelvis methods at the intermediate hopping frequencies, with differences ranging from 16 to 67 millimeters (all p < 0.05). Results are discussed in view of each methods’ limits. We conclude that the segmental analysis and force platform methods can be considered to provide the most accurate results for COM vertical excursion during human hopping in a large range of hopping frequency.

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Elena J. Caruthers, Julie A. Thompson, Ajit M.W. Chaudhari, Laura C. Schmitt, Thomas M. Best, Katherine R. Saul and Robert A. Siston

Sit-to-stand transfer is a common task that is challenging for older adults and others with musculoskeletal impairments. Associated joint torques and muscle activations have been analyzed two-dimensionally, neglecting possible three-dimensional (3D) compensatory movements in those who struggle with sit-to-stand transfer. Furthermore, how muscles accelerate an individual up and off the chair remains unclear; such knowledge could inform rehabilitation strategies. We examined muscle forces, muscleinduced accelerations, and interlimb muscle force differences during sit-to-stand transfer in young, healthy adults. Dynamic simulations were created using a custom 3D musculoskeletal model; static optimization and induced acceleration analysis were used to determine muscle forces and their induced accelerations, respectively. The gluteus maximus generated the largest force (2009.07 ± 277.31 N) and was a main contributor to forward acceleration of the center of mass (COM) (0.62 ± 0.18 m/s2), while the quadriceps opposed it. The soleus was a main contributor to upward (2.56 ± 0.74 m/s2) and forward acceleration of the COM (0.62 ± 0.33 m/s2). Interlimb muscle force differences were observed, demonstrating lower limb symmetry cannot be assumed during this task, even in healthy adults. These findings establish a baseline from which deficits and compensatory strategies in relevant populations (eg, elderly, osteoarthritis) can be identified.

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Samantha L. Winter, Sarah M. Forrest, Joanne Wallace and John H. Challis

In order to study, analyze, or optimize human movement, the mass, the center of mass location, and the body segment moments of inertia must be known. These body segment inertial parameters (BSIPs) affect the accuracy when calculating the resultant joint moments during activities which involve high

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Benita Olivier, Samantha-Lynn Quinn, Natalie Benjamin, Andrew Craig Green, Jessica Chiu and Weijie Wang

Khuu et al 13 included kinetics (joint moments) as an assessment method, they did not look at muscle activity or center of mass (CoM) displacement. Furthermore, it is important to establish whether there is in fact a difference between the kinematics of the stance leg and those of the trunk, as a

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Jean Slawinski, Véronique Billat, Jean-Pierre Koralsztein and Michel Tavernier

The purpose of this study was to estimate the difference between potential and kinetic mechanical powers in running (Pke, Ppe) calculated from the center of mass and one anatomic point of the body located on the lower part of the runner's back, the “lumbar point.” Six runners undertook a treadmill run at constant velocity and were filmed individually with a video camera (25 Hz). The 3-D motion analysis system, ANIMAN3D, uses a numerical manikin (MAN3D) which compares a voluminal subject (the athlete) directly to the manikin which possesses the same voluminal properties. This analysis system allows the trajectories of the center of mass and the lumbar point to be calculated. Then, from these trajectories, potential and kinetic mechanical powers in running are calculated. The results show that the utilization of the lumbar point rather than the runner's center of mass leads to a significant overestimation of Pke and a significant underestimation of Ppe (both p < 0.05). In spite of these differences, however, both methods of calculating Pke and Ppe are well correlated: respectively, r = 0.92; p ≤ 0.01, and r = 0.68; p ≤ 0.05. Taking into account that the trajectory of an anatomic point is experimentally easier to access than that of the center of mass, such a point could be used to estimate the evolution of kinetic or potential energy variation in different cases. However, when the lumbar point rather than the center of mass is used to estimate the mechanical energy produced in running, Pke could appear to be a discriminating parameter, which it is not.