Inertial measurement units (IMUs) are increasingly utilized as motion capture devices in human movement studies. Given their high portability, IMUs can be deployed in any environment, importantly those outside of the laboratory. However, a significant challenge limits the adoption of this technology; namely estimating the orientation of the IMUs to a common world frame, which is essential to estimating the rotations across skeletal joints. Common (probabilistic) methods for estimating IMU orientation rely on the ability to update the current orientation estimate using data provided by the IMU. The objective of this work is to present a novel error-state Kalman filter that yields highly accurate estimates of IMU orientation that are robust to poor measurement updates from fluctuations in the local magnetic field and/or highly dynamic movements. The method is validated with ground truth data collected with highly accurate orientation measurements provided by a coordinate measurement machine. As an example, the method yields IMU-estimated orientations that remain within 3.7 degrees (RMS error) over relatively long (25 cumulative minutes) trials even in the presence of large fluctuations in the local magnetic field. For comparison, ignoring the magnetic interference increases the RMS error to 12.8 degrees, more than a threefold increase.

Researchers employ foot-mounted inertial measurement units (IMUs) to estimate the three-dimensional trajectory of the feet as well as a rich array of gait parameters. However, the accuracy of those estimates depends critically on the limitations of the accelerometers and angular velocity gyros embedded in the IMU design. In this study, we reveal the effects of accelerometer range, gyro range, and sampling frequency on gait parameters (e.g., distance traveled, stride length, and stride angle) estimated using the zero-velocity update (ZUPT) method. The novelty and contribution of this work are that it: (1) quantifies these effects at mean speeds commensurate with competitive distance running (up to 6.4 m/s); (2) identifies the root causes of inaccurate foot trajectory estimates obtained from the ZUPT method; and (3) offers important engineering recommendations for selecting accurate IMUs for studying human running. The results demonstrate that the accuracy of the estimated gait parameters generally degrades with increased mean running speed and with decreased accelerometer range, gyro range, and sampling frequency. In particular, the saturation of the accelerometer and/or gyro induced during running for some IMU designs may render those designs highly inaccurate for estimating gait parameters.

Baseball and softball pitch types are distinguished by the path and speed of the ball which, in turn, are determined by the angular velocity of the ball and the velocity of the ball center at the instant of release from the pitcher’s hand. While radar guns and video-based motion capture (mocap) resolve ball speed, they provide little information about how the angular velocity of the ball and the velocity of the ball center develop and change during the throwing motion. Moreover, mocap requires measurements in a controlled lab environment and by a skilled technician. This study addresses these shortcomings by introducing a highly miniaturized, wireless inertial measurement unit (IMU) that is embedded in both baseballs and softballs. The resulting “ball-embedded” sensor resolves ball dynamics right on the field of play. Experimental results from ten pitches, five thrown by one softball pitcher and five by one baseball pitcher, demonstrate that this sensor technology can deduce the magnitude and direction of the ball’s velocity at release to within 4.6% of measurements made using standard mocap. Moreover, the IMU directly measures the angular velocity of the ball, which further enables the analysis of different pitch types.

Significance: The virus bacteriophage T4 infects the bacterium Escherichia coli using an intriguing nanoscale injection machinery that employs a contractile tail. The injection machinery is responsible for recognizing and puncturing the bacterial host and transferring the viral genome into the host during infection. Fundamental questions remain concerning how this injection process unfolds in real time, a process that presently defies direct experimental observation. Using a combination of atomistic and continuum representations, this study contributes a system-level model of the entire bacteriophage T4 interacting with a host cell, and in doing so, it exposes the energetics, forces, and dynamical pathway associated with the injection process. The results have further implications for future nanotechnology devices for DNA transfection and experimental phage therapies.