Comparison of the correlations between impact loading rates and peak accelerations measured at two different body sites: Intra- and inter-subject analysis

Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors

This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors' potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.

A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running

Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU's location has on running data, a reference IMU was 'correctly' placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was 'misplaced' 0.05 m away, simulating a 'worst-case' misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to -10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.

Acceleration-Based Estimation of Vertical Ground Reaction Forces during Running: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns

Twenty-seven methods of estimating vertical ground reaction force first peak, loading rate, second peak, average, and/or time series from a single wearable accelerometer worn on the shank or approximate center of mass during running were compared. Force estimation errors were quantified for 74 participants across different running surfaces, speeds, and foot strike angles and biases, repeatability coefficients, and limits of agreement were modeled with linear mixed effects to quantify the accuracy, reliability, and precision. Several methods accurately and reliably estimated the first peak and loading rate, however, none could do so precisely (the limits of agreement exceeded ±65% of target values). Thus, we do not recommend first peak or loading rate estimation from accelerometers with the methods currently available. In contrast, the second peak, average, and time series could all be estimated accurately, reliably, and precisely with several different methods. Of these, we recommend the 'Pogson' methods due to their accuracy, reliability, and precision as well as their stability across surfaces, speeds, and foot strike angles.

The Effect of Real-Time Tibial Acceleration Feedback on Running Biomechanics During Gait Retraining: A Systematic Review and Meta-Analysis

OBJECTIVES

To explore the immediate and retention effect of real-time tibial acceleration feedback on running biomechanics during gait retraining.

METHODS

Five electronic databases were searched to identify relevant studies published before May 2022. The included studies were evaluated for methodological quality and bias risk, and data were extracted. A meta-analysis was conducted on the primary outcomes, including peak tibial acceleration (PTA) and vertical ground reaction force. Subgroup analysis was performed by gender, feedback criterion, mode, dosage, fading, retention period, and running environment to evaluate the source of heterogeneity. Qualitative analysis was performed to describe other variables.

RESULTS

Fourteen studies (174 participants) were eligible. Meta-analysis showed that real-time tibial acceleration feedback reduced PTA (P < .01, P < .01), vertical impact peak (P = .004, P < .01), vertical average loading rate (P < .01, P < .01), and vertical instantaneous loading rate (P < .01, P < .01) after feedback and during retention period (5 min-12 mo). Subgroup analysis showed that the immediate effect of vertical impact peak was more noticeable with mixed gender (P = .005) and fading feedback (P = .005) conditions, and the retention effect of PTA was more noticeable with high feedback dosage (P < .01) and fading feedback (P < .01) conditions.

CONCLUSIONS

Real-time tibial acceleration feedback can reduce PTA and vertical ground reaction force during gait retraining, and for periods of 5 minutes to 12 months when the feedback is removed.

A machine learning approach to identify risk factors for running-related injuries: study protocol for a prospective longitudinal cohort trial

BACKGROUND

Running is a very popular sport among both recreational and competitive athletes. However, participating in running is associated with a comparably high risk of sustaining an exercise-related injury. Due to the often multifactorial and individual reasons for running injuries, a shift in thinking is required to account for the dynamic process of the various risk factors. Therefore, a machine learning approach will be used to comprehensively analyze biomechanical, biological, and loading parameters in order to identify risk factors and to detect risk patterns in runners.

METHODS

The prospective longitudinal cohort study will include competitive adult athletes, running at least 20 km per week and being free of injuries three months before the start of the study. At baseline and the end of the study period, subjective questionnaires (demographics, injury history, sports participation, menstruation, medication, psychology), biomechanical measures (e.g., stride length, cadence, kinematics, kinetics, tibial shock, and tibial acceleration) and a medical examination (BMI, laboratory: blood count, creatinine, calcium, phosphate, parathyroid hormone, vitamin D, osteocalcin, bone-specific alkaline phosphatase, DPD cross-links) will be performed. During the study period (one season), continuous data collection will be performed for biomechanical parameters, injuries, internal and external load. Statistical analysis of the data is performed using machine learning (ML) methods. For this purpose, the correlation of the collected data to possible injuries is automatically learned by an ML model and from this, a ranking of the risk factors can be determined with the help of sensitivity analysis methods.

DISCUSSION

To achieve a comprehensive risk reduction of injuries in runners, a multifactorial and individual approach and analysis is necessary. Recently, the use of ML processes for the analysis of risk factors in sports was discussed and positive results have been published. This study will be the first prospective longitudinal cohort study in runners to investigate the association of biomechanical, bone health, and loading parameters as well as injuries via ML models. The results may help to predict the risk of sustaining an injury and give way for new analysis methods that may also be transferred to other sports.

TRIAL REGISTRATION

DRKS00026904 (German Clinical Trial Register DKRS), date of registration 18.10.2021.

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.

Accurate Impact Loading Rate Estimation During Running via a Subject-Independent Convolutional Neural Network Model and Optimal IMU Placement

OBJECTIVE

Enable accurate estimation of vertical average loading rate (VALR) in runners with one or more wearable inertial measurement units (IMUs).

METHODS

A subject-independent convolutional neural network (CNN) model was developed to estimate VALR from wearable IMUs. Fifteen runners wore IMUs at the trunk, pelvis, thigh, shank, and foot and ran on an instrumented treadmill for combinations of the following conditions: foot-strike (forefoot, mid-foot, rear-foot), step rate (90% to 110% of baseline), running speed (2.4 m/s and 2.8 m/s) and footwear (standard and minimalist running shoes). Thirty-one IMU placement configurations with combinations of one to five IMUs were evaluated. VALR estimations from the wearable IMUs were compared with force-plate VALR measurements.

RESULTS

VALR estimations via the subject-independent CNN model with a single shank-worn IMU were highly correlated (ρ = 0.94) with force-plate VALR measurements and were substantially higher than previously reported peak tibial acceleration correlations with force-plate VALR measurements from shank-worn accelerometers (ρ = 0.44-0.66). Correlation results from the CNN model for a single IMU placed at the foot, pelvis, trunk, and thigh were ρ = 0.91, 0.76, 0.69, and 0.65, respectively. There was no improvement in accuracy from the shank-worn IMU when adding 1-4 additional IMUs from the trunk, pelvis, thigh, or foot.

CONCLUSION

The proposed subject-independent CNN model with a single shank-worn IMU provides more accurate estimation of VALR than previous wearable sensing approaches.

SIGNIFICANCE

This could enable runners to more accurately assess impact loading rates and potentially provide insights into running-related injury risk and prevention.

Using an Inertial Device (WIMU PRO) to Quantify Neuromuscular Load in Running: Reliability, Convergent Validity, and Influence of Type of Surface and Device Location

Gómez-Carmona, CD, Bastida-Castillo, A, González-Custodio, A, Olcina, G, and Pino-Ortega, J. Using an inertial device (WIMU PRO) to quantify neuromuscular load in running: reliability, convergent validity, and influence of type of surface and device location. J Strength Cond Res 34(2): 365-373, 2020-Currently, the use of accelerometers in sport is increasing, and thus, the devices are required to be valid and reliable. This study tested (a) the reliability and validity of WIMU PRO accelerometers to measure PlayerLoad (PL) and (b) the influence of speed, inertial device location, and type of surface where the incremental test is performed. Twenty resistance-trained men (age: 27.32 ± 6.65 years; height: 1.74 ± 0.03 m; body mass: 68.96 ± 4.37 kg; and body mass index: 22.76 ± 1.11 kg·m) volunteered to participate in the study that lasted 5 weeks. Four progressive incremental tests were performed in treadmill and athletic track conditions. External load variable (PL) and physiological variables (heart rate [HR] and SmO2) were recorded by 4 WIMU PRO inertial devices (scapulae, center of mass, knee, and ankle), a GARMIN HR band, and a MOXY near-infrared spectroscopy device, respectively. High reliability was found on both types of surface, showing the best values at the ankle (treadmill: intraclass correlation coefficient [ICC] = 0.99, coefficient of variation [CV] = 4.65%; track: ICC = 0.96, CV = 6.54%). A nearly perfect convergent validity was shown with HRAVG (r = 0.99) and a moderate one with SmO2 (r = -0.69). Significant differences in the PL variable between surfaces were reported in all locations except the scapulae (p = 0.173), and the higher values were found on the track. In the analysis per location, the ankle location reported the highest values at all speeds and on the 2 surfaces analyzed. Assessment needs to be individualized, due to the great variability of gait biomechanics among subjects. The accelerometer location should be chosen according to the purpose of the measurement, with the ankle location being recommended for neuromuscular load analysis in running.

Effect of minimalist and maximalist shoes on impact loading and footstrike pattern in habitual rearfoot strike trail runners: An in-field study

Running-related injuries among trail runners are very common and footwear selection may modulate the injury risk. However, most previous studies were conducted in a laboratory environment. The objective of this study was to examine the effects of two contrasting footwear designs, minimalist (MIN) and maximalist shoes (MAX), on the running biomechanics of trail runners during running on a natural trail. Eighteen habitual rearfoot strike trail runners completed level, uphill and downhill running at their preferred speeds in both shod conditions. Peak tibial acceleration, strike index and footstrike pattern were compared between the two footwear and slopes. Interactions of footwear and slope were not detected for all the selected variables. There was no significant effect from footwear (F = 1.23, p = 0.27) and slope (F = 2.49, p = 0.09) on peak tibial acceleration and there was no footwear effect on strike index (F = 3.82, p = 0.056). A significant main effect of slope on strike index (F = 13.24, p < 0.001) was found. Strike index during uphill running was significantly greater (i.e. landing with a more anterior foot strike) when compared with level (p < 0.001, Cohen's d = 1.72) or downhill running (p < 0.001, Cohen's d = 1.44) in either MIN or MAX. The majority of habitual rearfoot strike runners switched to midfoot strike during uphill running while maintaining a rearfoot strike pattern during level or downhill running. In summary, wearing either one of the two contrasting footwear (MIN or MAX) demonstrated no effect on impact loading and footstrike pattern in habitual rearfoot strike trail runners running on a natural trail with different slopes.

Tibial Acceleration-Based Prediction of Maximal Vertical Loading Rate During Overground Running: A Machine Learning Approach

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 ± 2.04 BW⋅s^–1, mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 ± 7.90 BW⋅s^–1 (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA.

Transfer Learning Effects of Biofeedback Running Retraining in Untrained Conditions

PURPOSE

Running gait retraining via peak tibial shock biofeedback has been previously shown to reduce impact loading and mitigate running-related symptoms. In previous research, peak tibial shock is typically measured and trained for one limb at a single constant training speed during all training sessions. The goal of this study was to determine how runners transfer learning in the trained limb to the untrained limb at different unconstrained speeds.

METHODS

Thirteen runners (3 females, age = 41.1 ± 6.9 yr, running experience = 6.8 ± 4.4 yr, weekly running distance = 30.7 ± 22.2 km) underwent running gait biofeedback retraining via continuous tibial acceleration measured at the right distal tibia. Before and after the training, participants were asked to run at their self-selected constrained training speeds (2.8 ± 0.2 m·s) and at 110% and 90% of the training speed. Pretraining and posttraining peak tibial shock values for each limb were compared.

RESULTS

Participants reduced peak tibial shock in the trained limb by 35% to 37% (P < 0.05, Cohen's d = 0.78-0.85), and in the untrained limb by 20% to 23% (P < 0.05, Cohen's d = 0.51-0.71) across the three testing speeds. The reduction was not significantly different between the trained and untrained limbs (P = 0.31-0.79, Cohen's d = 0.18-0.45). Similarly, there was no difference in peak tibial shock reduction among the three running speeds (P = 0.48-0.61, Cohen's d = 0.06-0.45).

CONCLUSION

Participants demonstrated transfer learning effects evidenced by concomitant reduced peak tibial shock in the untrained limb, and the learning effects were retrained when running at a 10% variance of the training speed.

Can runners maintain a newly learned gait pattern outside a laboratory environment following gait retraining?

BACKGROUND

Previous peak tibial shock gait retraining programs, which were usually conducted on a treadmill, were reported to be effective on impact loading reduction in runners. However, whether the trained runners can translate the training effect at different running modes (treadmill/overground), or running slopes (uphill/downhill), remains unknown.

RESEARCH QUESTION

Is the training effect from a treadmill-based gait retraining translatable to unconstrained running conditions, including overground and uphill/downhill running?

METHODS

The peak tibial shock was measured during treadmill/overground running, as well as level/uphill/downhill running before and after a course of treadmill-based gait retraining. The 8-session training aimed to soften footfalls using real-time biofeedback of tibial shock data. Repeated measures ANOVA was used to examine the effect of training, running mode, and running slope, on a group level. Reliable change index of each participant was used to assess the individual response to the training protocol used in this study.

RESULTS

Eighty percent of the participants were responsive to the gait retraining and managed to reduce their peak tibial shock following training. They managed to translate the training effect to treadmill slope running (Level: p < 0.05, Cohen's d = 1.65; Uphill: p = 0.001, Cohen's d = 0.91; Downhill: p < 0.05; Cohen's d = 1.29) and overground level running (p = 0.014, Cohen's d = 0.85). However, their peak tibial shock were not reduced during overground slope running (Uphill: p = 0.054; Cohen's d = 0.62; Downhill p = 0.12; Cohen's d = 0.48).

SIGNIFICANCE

Our findings indicated that a newly learned gait pattern may not fully translate to running outside of the laboratory environment.

Shoe-mounted accelerometers should be used with caution in gait retraining

Real-time biofeedback gait retraining has been reported to be an effective intervention to lower the impact loading during gait. While many of the previous gait retraining studies have utilized a laboratory-based setup, some studies used accelerometers affixed at the distal tibia to allow training outside the laboratory environment. However, many commercial sensors for gait modification are shoe-mounted. Hence, this study sought to compare impact loading parameters measured by shoe-mounted and tibia sensors in participants before and after a course of walking or running retraining using signal source from the shoe-mounted sensors. We also compared the correlations between peak positive acceleration measured at shoe (PPA_S ) and tibia (PPA_T ) and vertical loading rates, as these loading rates have been related to injury. Twenty-four and 14 participants underwent a 2-week visual biofeedback walking and running retraining, respectively. Participants in the walking retraining group experienced lower PPA_S following the intervention (P < 0.005). However, they demonstrated no change in PPA_T (P = 0.409) nor vertical loading rates (P > 0.098) following the walking retraining. In contrast, participants in the running retraining group experienced a reduction in the PPA_T (P = 0.001) and vertical loading rates (P < 0.013) after running retraining. PPA_S values were four times that of PPA_T for both walking and running suggesting an uncoupling of the shoe with tibia. As such, PPA_S was not correlated with vertical loading rates for either walking or running, while significant correlations between PPA_T and vertical loading rates were noted. The present study suggests potential limitations of the existing commercial shoe-mounted sensors.

Kinematics and shock attenuation during a prolonged run on the athletic track as measured with inertial magnetic measurement units

BACKGROUND

Tibial stress fractures are common running related injury and their etiology may include biomechanical factors like impact forces, shock attenuation, lower limb kinematics and how these factors are influenced by intense or prolonged running. Inertial-magnetic measurement units (IMUs) have recently emerged as an alternative to motion capture but their use to date was mostly limited to segmental and joint motion.

RESEARCH QUESTION

The present study sought to examine the effects of a prolonged run on shock attenuation, peak tibial and sacral acceleration (PTA, PSA), and lower limb kinematics using IMUs.

METHODS

Ten trained male runners (31 +/- 5 yr, 183 +/- 3 cm, 76 +/- 9 kg) performed a twenty-minute prolonged run on an athletic track at estimated lactate threshold speed. Eight IMUs, positioned over the feet, lower and uppers legs, sacrum and sternum, were used to calculate joint kinematics, impact parameters and shock attenuation in the time domain (1-(PSA/PTA)*100).

RESULTS

PTA increased while PSA and shock attenuation did not change following the prolonged run. Hip and knee flexion at midstance decreased. Vertical lower leg angle at initial contact did not change.

CONCLUSION

By using IMUs, it was shown that a prolonged run at estimated lactate threshold speed had significant effects on kinematics and tibial acceleration parameters. By modifying hip and knee joint kinematics during stance, the body was able to maintain sacral acceleration possibly by shifting from active shock attenuation to more passive mechanisms.

SIGNIFICANCE

The present study shows that inertial sensors can be used in outdoor running to measure joint kinematics and kinetic parameters like PTA, PSA and shock attenuation simultaneously. The results of this study show new insights into how the body copes with impact during prolonged running.

Biomechanical Outcomes Due to Impact Loading in Runners While Looking Sideways

A stable gaze is necessary to optimize visual conditions during running. Head accelerations generally remain stable when looking in front; however, it is unclear if this response is similar when the head is turned sideways, and whether other adaptive strategies are present to maintain this stability. The purpose of this study, therefore, was to examine whether runners maintained stable head accelerations while gazing at fixed targets in front and to their sides. The authors collected biomechanical data from 13 runners as they directed their gaze to visual targets located in front, 45°, and 90° to the sides at a random sequence. Vertical head and tibial accelerations were the primary outcome measures, while vertical loading rate, footstrike angle, contact time, stride length, and stride rate were the secondary measures. A reduction in vertical head accelerations was found in the rightmost direction (P = .04), while an increase in vertical tibial accelerations was found on the same direction (P = .02). No other significant differences were observed for the other variables. The results of this study suggest that the tibia accommodated the increased shock to maintain head stability.

Measurement agreement between a newly developed sensing insole and traditional laboratory-based method for footstrike pattern detection in runners

This study introduced a novel but simple method to continuously measure footstrike patterns in runners using inexpensive force sensors. Two force sensing resistors were firmly affixed at the heel and second toe of both insoles to collect the time signal of foot contact. A total of 109 healthy young adults (42 males and 67 females) were recruited in this study. They ran on an instrumented treadmill at 0°, +10°, and -10° inclinations and attempted rearfoot, midfoot, and forefoot landings using real time visual biofeedback. Intra-step strike index and onset time difference between two force sensors were measured and analyzed with univariate linear regression. We analyzed 25,655 footfalls and found that onset time difference between two sensors explained 80–84% of variation in the prediction model of strike index (R-squared = 0.799–0.836, p<0.001). However, the time windows to detect footstrike patterns on different surface inclinations were not consistent. These findings may allow laboratory-based gait retraining to be implemented in natural running environments to aid in both injury prevention and performance enhancement.