Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor

Parameterization of Biomechanical Variables through Inertial Measurement Units (IMUs) in Occasional Healthy Runners

Running is one of the most popular sports practiced today and biomechanical variables are fundamental to understanding it. The main objectives of this study are to describe kinetic, kinematic, and spatiotemporal variables measured using four inertial measurement units (IMUs) in runners during treadmill running, investigate the relationships between these variables, and describe differences associated with different data sampling and averaging strategies. A total of 22 healthy recreational runners (M age = 28 ± 5.57 yrs) participated in treadmill measurements, running at their preferred speed (M = 10.1 ± 1.9 km/h) with a set-up of four IMUs placed on tibias and the lumbar area. Raw data was processed and analysed over selections spanning 30 s, 30 steps and 1 step. Very strong positive associations were obtained between the same family variables in all selections. The temporal variables were inversely associated with the step rate variable in the selection of 30 s and 30 steps of data. There were moderate associations between kinetic (forces) and kinematic (displacement) variables. There were no significant differences between the biomechanics variables in any selection. Our results suggest that a 4-IMU set-up, as presented in this study, is a viable approach for parameterization of the biomechanical variables in running, and also that there are no significant differences in the biomechanical variables studied independently, if we select data from 30 s, 30 steps or 1 step for processing and analysis. These results can assist in the methodological aspects of protocol design in future running research.

Real-world data capture of daily limb loading using force-sensing insoles: Feasibility and lessons learned

Force-sensing insoles are wearable technology that offer an innovative way to measure loading outside of laboratory settings. Few studies, however, have utilized insoles to measure daily loading in real-world settings. This is an ancillary study of a randomized controlled trial examining the effect of weight loss alone, weight loss plus weighted vest, or weight loss plus resistance training on bone health in older adults. The purpose of this ancillary study was to determine the feasibility of using force-sensing insoles to collect daily limb loading metrics, including peak force, impulse, and loading rate. Forty-four participants completed a baseline visit of three, 2-minute walking trials while wearing force-sensing insoles. During month two of the intervention, 37 participants wore insoles for 4 days for 8 waking hours each day. At 6-month follow-up, participants completed three, two-minute walking trials and a satisfaction questionnaire. Criteria for success in feasibility was defined as: a) > 60 % recruitment rate; b) > 80 % adherence rate; c) > 75 % of usable data, and d) > 75 % participant satisfaction. A 77.3 % recruitment rate was achieved, with 44 participants enrolled. Participants wore their insoles an average of 7.4 hours per day, and insoles recorded an average of 5.5 hours per day. Peak force, impulse, and loading rate collected at baseline and follow-up were 100 % usable. During the real-world settings, 87.8 % of data was deemed usable with an average of 1200 min/participant. Lastly, average satisfaction was 80.5 %. These results suggest that force-sensing insoles appears to be feasible to capture real-world limb loading in older adults.

Indoor running temporal variability for different running speeds, treadmill inclinations, and three different estimation strategies

Inertial measurement units (IMU) constitute a light and cost-effective alternative to gold-standard measurement systems in the assessment of running temporal variables. IMU data collected on 20 runners running at different speeds (80, 90, 100, 110 and 120% of preferred running speed) and treadmill inclination (±2, ±5, and ±8%) were used here to predict the following temporal variables: stride frequency, duty factor, and two indices of running variability such as the detrended fluctuation analysis alpha (DFA-α) and the Higuchi's D (HG-D). Three different estimation methodologies were compared: 1) a gold-standard optoelectronic device (which provided the reference values), 2) IMU placed on the runner's feet, 3) a single IMU on the runner's thorax used in conjunction with a machine learning algorithm with a short 2-second or a long 120-second window as input. A two-way ANOVA was used to test the presence of significant (p<0.05) differences due to the running condition or to the estimation methodology. The findings of this study suggest that using both IMU configurations for estimating stride frequency can be effective and comparable to the gold-standard. Additionally, the results indicate that the use of a single IMU on the thorax with a machine learning algorithm can lead to more accurate estimates of duty factor than the strategy of the IMU on the feet. However, caution should be exercised when using these techniques to measure running variability indices. Estimating DFA-α from a short 2-second time window was possible only in level running but not in downhill running and it could not accurately estimate HG-D across all running conditions. By taking a long 120-second window a machine learning algorithm could improve the accuracy in the estimation of DFA-α in all running conditions. By taking these factors into account, researchers and practitioners can make informed decisions about the use of IMU technology in measuring running biomechanics.

Editorial-Special Issue on "Sensor Technology for Enhancing Training and Performance in Sport"

Sensor technology opens up exciting opportunities for sports [...].

Acquisition of Lower-Limb Motion Characteristics with a Single Inertial Measurement Unit—Validation for Use in Physiotherapy

In physiotherapy, there is still a lack of practical measurement options to track the progress of therapy or rehabilitation following injuries to the lower limbs objectively and reproducibly yet simply and with minimal effort and time. We aim at filling this gap with the design of an IMU (inertial measurement unit) system with only one sensor placed on the tibia edge. In our study, the IMU system evaluated a set of 10 motion tests by a score value for each test and stored them in a database for a more reliable longitudinal assessment of the progress. The sensor analyzed the different motion patterns and obtained characteristic physiological parameters, such as angle ranges, and spatial and angular displacements, such as knee valgus under load. The scores represent the patient’s coordination, stability, strength and speed. To validate the IMU system, these scores were compared to corresponding values from a simultaneously recorded marker-based 3D video motion analysis of the measurements from five healthy volunteers. Score differences between the two systems were almost always within 1–3 degrees for angle measurements. Timing-related measurements were nearly completely identical. The tests on the valgus stability of the knee showed equally small deviations but should nevertheless be repeated with patients, because the healthy subjects showed no signs of instability.

Cross-Leg Prediction of Running Kinematics across Various Running Conditions and Drawing from a Minimal Data Set Using a Single Wearable Sensor

The feasibility of prediction of same-limb kinematics using a single inertial measurement unit attached to the same limb has been demonstrated using machine learning. This study was performed to see if a single inertial measurement unit attached to the tibia can predict the opposite leg’s kinematics (cross-leg prediction). It also investigated if there is a minimal or smaller data set in a convolutional neural network model to predict lower extremity running kinematics under other running conditions and with what accuracy for the intra- and inter-participant situations. Ten recreational runners completed running exercises under five conditions, including treadmill running at speeds of 2, 2.5, 3, and 3.5 m/s and level-ground running at their preferred speed. A one-predict-all scheme was adopted to determine which running condition could be used to best predict a participant’s overall running kinematics. Running kinematic predictions were performed for intra- and inter-participant scenarios. Among the tested running conditions, treadmill running at 3 m/s was found to be the optimal condition for accurately predicting running kinematics under other conditions, with R2 values ranging from 0.880 to 0.958 and 0.784 to 0.936 for intra- and inter-participant scenarios, respectively. The feasibility of cross-leg prediction was demonstrated but with significantly lower accuracy than the same leg. The treadmill running condition at 3 m/s showed the highest intra-participant cross-leg prediction accuracy. This study proposes a novel, deep-learning method for predicting running kinematics under different conditions on a small training data set.

Classification of Tennis Shots with a Neural Network Approach

Data analysis plays an increasingly valuable role in sports. The better the data that is analysed, the more concise training methods that can be chosen. Several solutions already exist for this purpose in the tennis industry; however, none of them combine data generation with a wristband and classification with a deep convolutional neural network (CNN). In this article, we demonstrate the development of a reliable shot detection trigger and a deep neural network that classifies tennis shots into three and five shot types. We generate a dataset for the training of neural networks with the help of a sensor wristband, which recorded 11 signals, including an inertial measurement unit (IMU). The final dataset included 5682 labelled shots of 16 players of age 13–70 years, predominantly at an amateur level. Two state-of-the-art architectures for time series classification (TSC) are compared, namely a fully convolutional network (FCN) and a residual network (ResNet). Recent advances in the field of machine learning, like the Mish activation function and the Ranger optimizer, are utilized. Training with the rather inhomogeneous dataset led to an F₁ score of 96% in classification of the main shots and 94% for the expansion. Consequently, the study yielded a solid base for more complex tennis analysis tools, such as the indication of success rates per shot type.