In-field assessment of change-of-direction ability with a single wearable sensor.

The Agility T-test is a standardized method to measure the change-of-direction (COD) ability of athletes in the field. It is traditionally scored based on the total completion time, which does not provide information on the different CODs. Augmenting the T-test with wearable sensors provides the opportunity to explore new metrics. Towards this, data of 23 professional soccer players were recorded with a trunk-worn GNSS-IMU (Global Navigation Satellite System-Inertial Measurement Unit) device. A method for detecting the four CODs based on the wavelet-denoised antero-posterior acceleration signal was developed and validated using video data (60 Hz). Following this, completion time was estimated using GNSS ground speed and validated with the photocell data. The proposed method yields an error (mean ± standard deviation) of 0 ± 66 ms for the COD detection, - 0.16 ± 0.22 s for completion time, and a relative error for each COD duration and each sequential movement durations of less than 3.5 ± 16% and 7 ± 7%, respectively. The presented algorithm can highlight the asymmetric performance between the phases and CODs in the right and left direction. By providing a more comprehensive analysis in the field, this work can enable coaches to develop more personalized training and rehabilitation programs.

Estimation of Temporal Parameters During Running with a Wrist-worn Inertial Sensor: an In-field Validation.

The aim of this study was to estimate the temporal gait parameters using a wrist-worn Inertial Measurement Unit (IMU) during an outdoor run. While it is easier to compute running gait parameters using foot IMUs, a wrist IMU is more convenient and less obtrusive when it comes to data acquisition. During a track run of 12 minutes, we equipped 14 highly-trained male runners with one IMU on the wrist and one on each foot. We trained machine learning models based on CNN, GPR, and Lasso regression using wrist IMU signals and validated them with a foot-worn IMU reference system. Lasso model performed the best, with the accuracy for cycle time, swing time, flight time, and contact time being 0.27 % ±0.1 %, 2.6 %±1.7 %, 7.3 % ±4.9 %, and 10.6 % ±5.5 %, respectively.

The association between motor modules and movement primitives of gait: A muscle and kinematic synergy study.

In spite of the extensive literature on the analysis of the muscle synergies during gait, the functionality of these synergies has not been studied in detail. This study explored the relationship between the motor modules and the kinematic maneuvers involved in human walking. Motion and surface electromyography data (of 28 trunk and lower extremity muscles) were acquired from ten healthy subjects during ten trials of self-selected speed gait each. The joint angle trajectories were half-wave rectified and divided into two independent positive directional degrees-of-freedom. The muscle and kinematic synergies were both extracted using the non-negative matrix factorization (NNMF) technique and clustered via k-means method. Results indicated that for both the muscle and kinematic synergies, a same number of modules (five) could reconstruct the 200 limb-trial data reasonably well. Moreover, each individual muscle synergy was found to be associated with a single kinematic synergy, based on the similar activation periods of the paired muscle and kinematic modules and the high correlation of their activation patterns (r = 0.88 ± 0.05), with a consistent phase advance for muscle synergies (mean = 7.59 ± 2.34 %). It was concluded that there is a one-to-one association between the motor modules and kinematic synergies, suggesting that each individual kinematic synergy, representing a movement primitive of human walking, might be implemented by recruitment of a paired motor module.