Ground reaction force estimates from ActiGraph GT3X+ hip accelerations

Sensing the future: A review on emerging technologies for assessing and monitoring bone health

Bone health is crucial at all stages of life. Several medical conditions and changes in lifestyle affect the growth, structure, and functions of bones. This may lead to the development of bone degenerative disorders, such as osteoporosis, osteoarthritis, rheumatoid arthritis, etc., which are major public health concerns worldwide. Accurate and reliable measurement and monitoring of bone health are important aspects for early diagnosis and interventions to prevent such disorders. Significant progress has recently been made in developing new sensing technologies that offer non-invasive, low-cost, and accurate measurements of bone health. In this review, we have described bone remodeling processes and common bone disorders. We have also compiled information on the bone turnover markers for their use as biomarkers in biosensing devices to monitor bone health. Second, this review details biosensing technology for bone health assessment, including the latest developments in various non-invasive techniques, including dual-energy X-ray absorptiometry, magnetic resonance imaging, computed tomography, and biosensors. Further, we have also discussed the potential of emerging technologies, such as biosensors based on nano- and micro-electromechanical systems and application of artificial intelligence in non-invasive techniques for improving bone health assessment. Finally, we have summarized the advantages and limitations of each technology and described clinical applications for detecting bone disorders and monitoring treatment outcomes. Overall, this review highlights the potential of emerging technologies for improving bone health assessment with the potential to revolutionize clinical practice and improve patient outcomes. The review highlights key challenges and future directions for biosensor research that pave the way for continued innovations to improve diagnosis, monitoring, and treatment of bone-related diseases.

Handrim Reaction Force and Moment Assessment Using a Minimal IMU Configuration and Non-Linear Modeling Approach during Manual Wheelchair Propulsion

Manual wheelchair propulsion represents a repetitive and constraining task, which leads mainly to the development of joint injury in spinal cord-injured people. One of the main reasons is the load sustained by the shoulder joint during the propulsion cycle. Moreover, the load at the shoulder joint is highly correlated with the force and moment acting at the handrim level. The main objective of this study is related to the estimation of handrim reactions forces and moments during wheelchair propulsion using only a single inertial measurement unit per hand. Two approaches are proposed here: Firstly, a method of identification of a non-linear transfer function based on the Hammerstein-Wiener (HW) modeling approach was used. The latter represents a typical multi-input single output in a system engineering modeling approach. Secondly, a specific variant of recurrent neural network called BiLSTM is proposed to predict the time-series data of force and moments at the handrim level. Eleven subjects participated in this study in a linear propulsion protocol, while the forces and moments were measured by a dynamic platform. The two input signals were the linear acceleration as well the angular velocity of the wrist joint. The horizontal, vertical and sagittal moments were estimated by the two approaches. The mean average error (MAE) shows a value of 6.10 N and 4.30 N for the horizontal force for BiLSTM and HW, respectively. The results for the vertical direction show a MAE of 5.91 N and 7.59 N for BiLSTM and HW, respectively. Finally, the MAE for the sagittal moment varies from 0.96 Nm (BiLSTM) to 1.09 Nm for the HW model. The approaches seem similar with respect to the MAE and can be considered accurate knowing that the order of magnitude of the uncertainties of the dynamic platform was reported to be 2.2 N for the horizontal and vertical forces and 2.24 Nm for the sagittal moments. However, it should be noted that HW necessitates the knowledge of the average force and patterns of each subject, whereas the BiLSTM method do not involve the average patterns, which shows its superiority for time-series data prediction. The results provided in this study show the possibility of measuring dynamic forces acting at the handrim level during wheelchair manual propulsion in ecological environments.

Increasing Step Rate Reduces Peak and Cumulative Insole Force in Collegiate Runners

PURPOSE

The primary goal of this study was to examine changes in peak insole force and cumulative weighted peak force (CWPF)/km with increased step rate in collegiate runners. The secondary goal was to determine whether sacral acceleration correlates with insole force when increasing step rate.

METHODS

Twelve collegiate distance runners ran 1000 m outdoors at 3.83 m·s -1 at preferred and 10% increased step rates while insole force and sacral acceleration were recorded. Cumulative weighted peak force/km was calculated from insole force based on cumulative damage models. The effects of step rate on peak insole force and CWPF·km -1 were tested using paired t tests or Wilcoxon tests. Correlation coefficients between peak axial (approximately vertical) sacral acceleration times body mass and peak insole force were calculated on cohort and individual levels.

RESULTS

Peak insole force and CWPF·km -1 decreased ( P < 0.001) with increased step rate. Peak axial sacral acceleration did not correlate with peak insole force on the cohort level ( r = 0.35, P = 0.109) but did within individuals (mean, r = 0.69-0.78; P < 0.05).

CONCLUSIONS

Increasing step rate may reduce peak vGRF and CWPF·km -1 in collegiate runners. Therefore, clinicians should consider step rate interventions to reduce peak and cumulative vGRF in this population. Individual-specific calibrations may be required to assess changes in peak vGRF in response to increasing step rate using wearable accelerometers.

Measurement of Sedentary Behavior-The Outcomes of the Angle for Posture Estimation (APE) Method

Hip-worn accelerometers are commonly used to assess habitual physical activity, but their accuracy in precisely measuring sedentary behavior (SB) is generally considered low. The angle for postural estimation (APE) method has shown promising accuracy in SB measurement. This method relies on the constant nature of Earth's gravity and the assumption that walking posture is typically upright. This study investigated how cardiorespiratory fitness (CRF) and body mass index (BMI) are related to APE output. A total of 3475 participants with adequate accelerometer wear time were categorized into three groups according to CRF or BMI. Participants in low CRF and high BMI groups spent more time in reclining and lying postures (APE ≥ 30°) and less time in sitting and standing postures (APE < 30°) than the other groups. Furthermore, the strongest partial Spearman correlation with CRF (r = 0.284) and BMI (r = -0.320) was observed for APE values typical for standing. The findings underscore the utility of the APE method in studying associations between SB and health outcomes. Importantly, this study emphasizes the necessity of reserving the term "sedentary behavior" for studies wherein the classification of SB is based on both intensity and posture.

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.

Estimation of horizontal running power using foot-worn inertial measurement units

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.

Using Raw Accelerometer Data to Predict High-Impact Mechanical Loading

The purpose of this study was to develop peak ground reaction force (pGRF) and peak loading rate (pLR) prediction equations for high-impact activities in adult subjects with a broad range of body masses, from normal weight to severe obesity. A total of 78 participants (27 males; 82.4 ± 20.6 kg) completed a series of trials involving jumps of different types and heights on force plates while wearing accelerometers at the ankle, lower back, and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Body mass was a predictor in all models, along with peak acceleration in the pGRF models and peak acceleration rate in the pLR models. The equations to predict pGRF had a coefficient of determination (R²) of at least 0.83, and a mean absolute percentage error (MAPE) below 14.5%, while the R² for the pLR prediction equations was at least 0.87 and the highest MAPE was 24.7%. Jumping pGRF can be accurately predicted through accelerometry data, enabling the continuous assessment of mechanical loading in clinical settings. The pLR prediction equations yielded a lower accuracy when compared to the pGRF equations.

Inertial Sensor Location for Ground Reaction Force and Gait Event Detection Using Reservoir Computing in Gait

Inertial measurement units (IMUs) have shown promising outcomes for estimating gait event detection (GED) and ground reaction force (GRF). This study aims to determine the best sensor location for GED and GRF prediction in gait using data from IMUs for healthy and medial knee osteoarthritis (MKOA) individuals. In this study, 27 healthy and 18 MKOA individuals participated. Participants walked at different speeds on an instrumented treadmill. Five synchronized IMUs (Physilog®, 200 Hz) were placed on the lower limb (top of the shoe, heel, above medial malleolus, middle and front of tibia, and on medial of shank close to knee joint). To predict GRF and GED, an artificial neural network known as reservoir computing was trained using combinations of acceleration signals retrieved from each IMU. For GRF prediction, the best sensor location was top of the shoe for 72.2% and 41.7% of individuals in the healthy and MKOA populations, respectively, based on the minimum value of the mean absolute error (MAE). For GED, the minimum MAE value for both groups was for middle and front of tibia, then top of the shoe. This study demonstrates that top of the shoe is the best sensor location for GED and GRF prediction.

Wearables for Running Gait Analysis: A Systematic Review

BACKGROUND

Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance.

OBJECTIVES

We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults.

METHODS

A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science and SPORTDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis and key findings.

RESULTS

A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (n = 62) [i.e. a combination of accelerometers, gyroscopes and magnetometers in a single unit] or solely accelerometers (n = 40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n = 93), using a treadmill (n = 62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g. age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards.

CONCLUSIONS

This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one inertial measurement unit sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and the reliability of devices and suitability of gait outcomes.

CLINICAL TRIAL REGISTRATION

CRD42021235527.

Concurrent validity of the combined HRV/ACC sensor and physical activity diary when monitoring physical activity in university students during free-living days

The purpose of this research was to determine if the scientific research device combined heart rate variability combined with an acceleration sensor (Firstbeat Bodyguard 2, BG2) was valid and reliable for time spent in different intensity zones in free-living. A total of 55 healthy participants performed 48-h physical activity (PA) monitoring with BG2, ActiGraph GT3X+ (GT3X+), and completed Bouchard Physical Activity Diary (Bouchard) every night. In the available studies, GT3X+ is considered the gold standard scientific research device for PA monitor. We compared BG2 and Bouchard with GT3X+ by difference, correlation, and agreement of PA and energy expenditure (EE) in free-living. The results showed that BG2 estimated PA more accurately than Bouchard, with a modest correlation (r > 0.49), strong agreement (τ > 0.29), and they had the lowest limits of agreement when estimating moderate to vigorous physical activity (MVPA). The EE estimated by Bouchard was the highest among the three methods, and the correlation and agreement between the three methods were high. Our findings showed that the BG2 is valid and reliable for estimating time spent in different intensity zones in free-living, especially in MVPA.

Transfer of post-trial feedback on impacts during drop landings in female athletes

Increased vertical ground reaction force (vGRF) and dynamic knee valgus contribute to non-contact anterior cruciate ligament (ACL) injuries. We examined feedback's influence during landing and transfer to a game-specific drill, measured by deceleration. Thirty-one female athletes performed 30 drop landings with augmented feedback and dual-task conditions, with a game-specific drill before and after. Differences were shown across time (baseline, feedback, post-feedback) and between conditions (with or without dual-task) in peak vGRF and knee to ankle ratio (K:A ratio). K:A ratio is the ratio of the frontal plane distance between the knees relative to the frontal plane distance between the ankles. This measure serves as a surrogate for knee valgus where a ratio closer to 1 indicates less knee valgus. There were reductions in peak vGRF (p < 0.05) and improvements in K:A ratio (p < 0.05) across time, improvements in K:A ratio across time and by condition (p < 0.05), and reduction in deceleration during landing in a game-specific drill (p < 0.05). Feedback may improve landing mechanics and transfer to a game-specific drill that can influence ACL injury in sport.

The Impact of Footwear on Occupational Task Performance and Musculoskeletal Injury Risk: A Scoping Review to Inform Tactical Footwear

The aim of this scoping review was to investigate the impact of footwear on worker physical task performance and injury risk. The review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews protocol and registered in the Open Science Framework. Key search terms were entered into five academic databases. Following a dedicated screening process and critical appraisal, data from the final articles informing this review were extracted, tabulated, and synthesised. Of 19,614 identified articles, 50 articles informed this review. Representing 16 countries, the most common populations investigated were military and firefighter populations, but a wide range of general occupations (e.g., shipping, mining, hairdressing, and healthcare workers) were represented. Footwear types included work safety boots/shoes (e.g., industrial, gumboots, steel capped, etc.), military and firefighter boots, sports shoes (trainers, tennis, basketball, etc.) and various other types (e.g., sandals, etc.). Occupational footwear was found to impact gait and angular velocities, joint ranges of motion, posture and balance, physiological measures (like aerobic capacity, heart rates, temperatures, etc.), muscle activity, and selected occupational tasks. Occupational footwear associated with injuries included boots, conventional running shoes, shoes with inserts, harder/stiffer outsoles or thin soles, and shoes with low comfort scores-although the findings were mixed. Occupational footwear was also linked to potentially causing injuries directly (e.g., musculoskeletal injuries) as well as leading to mechanisms associated with causing injuries (like tripping and slipping).

Mechanical loading prediction through accelerometry data during walking and running

Currently, there is no way to assess mechanical loading variables such as peak ground reaction forces (pGRF) and peak loading rate (pLR) in clinical settings. The purpose of this study was to develop accelerometry-based equations to predict both pGRF and pLR during walking and running. One hundred and thirty one subjects (79 females; 76.9 ± 19.6kg) walked and ran at different speeds (2-14km·h^-1) on a force plate-instrumented treadmill while wearing accelerometers at their ankle, lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Our pGRF prediction equation was compared with a reference equation previously published. Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration rate for pLR prediction. All pGRF equation coefficients of determination were above 0.96, and a good agreement between actual and predicted pGRF was observed, with a mean absolute percent error (MAPE) below 7.3%. Accuracy indices from our equations were better than previously developed equations. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF. Walking and running pGRF can be predicted with high accuracy by accelerometry-based equations, representing an easy way to determine mechanical loading in free-living conditions. The pLR prediction equations yielded a somewhat lower prediction accuracy compared with the pGRF equations.

Determining jumping performance from a single body-worn accelerometer using machine learning

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg^-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg^-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

Accelerometer-based prediction of ground reaction force in head-out water exercise with different exercise intensity countermovement jump

Background

Water jumping exercise is an alternative method to achieve maintenance of bone health and reduce exercise injuries. Clarifying the ground reaction force (GRF) of moderate and high cardiopulmonary exercise intensities for jumping movements can help quantify the impact force during different exercise intensities. Accelerometers have been explored for measuring skeletal mechanical loading by estimating the GRFs. Predictive regression equations for GRF using ACC on land have already been developed and performed outside laboratory settings, whereas a predictive regression equation for GRF in water exercises is not yet established. The purpose of this study was to determine the best accelerometer wear-position for three exercise intensities and develop and validate the ground reaction force (GRF) prediction equation.

Methods

Twelve healthy women (23.6 ± 1.83 years, 158.2 ± 5.33 cm, 53.1 ± 7.50 kg) were recruited as participants. Triaxial accelerometers were affixed 3 cm above the medial malleolus of the tibia, fifth lumbar vertebra, and seventh cervical vertebra (C7). The countermovement jump (CMJ) cadence started at 80 beats/min and increased by 5 beats per 20 s to reach 50%, 65%, and 80% heart rate reserves, and then participants jumped five more times. One-way repeated analysis of variance was used to determine acceleration differences among wear-positions and exercise intensities. Pearson’s correlation was used to determine the correlation between the acceleration and GRF per body weight on land (GRF_VL_BW). Backward regression analysis was used to generate GRF_VL_BW prediction equations from full models with C7 acceleration (C7 ACC), age, percentage of water deep divided by body height (PWDH), and bodyweight as predictors. Paired t-test was used to determine GRF_VL_BW differences between values from the prediction equation and force plate measurement during validation. Lin’s CCC and Bland–Altman plots were used to determine the agreement between the predicted and force plate-measured GRF_VL_BW.

Results

The raw full profile data for the resultant acceleration showed that the acceleration curve of C7 was similar to that of GRFv. The predicted formula was − 1.712 + 0.658 * C7ACC + 0.016 * PWDH + 0.008 * age + 0.003*weight. Lin’s CCC score was 0.7453, with bias of 0.369%.

Conclusion

The resultant acceleration measured at C7 was identified as the valid estimated GRF_VL_BW during CMJ in water.

Validity of load rate estimation using accelerometers during physical activity on an anti-gravity treadmill

Introduction

A simple tool to estimate loading on the lower limb joints outside a laboratory may be useful for people who suffer from degenerative joint disease. Here, the accelerometers on board of wearables (smartwatch, smartphone) were used to estimate the load rate on the lower limbs and were compared to data from a treadmill force plate. The aim was to assess the validity of wearables to estimate load rate transmitted through the joints.

Methods

Twelve healthy participants (female n = 4, male n = 8; aged 26 ± 3 years; height: 175 ± 15 cm; body mass: 71 ± 9 kg) carried wearables, while performing locomotive activities on an anti-gravity treadmill with an integrated force plate. Acceleration data from the wearables and force plate data were used to estimate the load rate. The treadmill enabled 7680 data points to be obtained, allowing a good estimate of uncertainty to be examined. A linear regression model and cross-validation with 1000 bootstrap resamples were used to assess the validation.

Results

Significant correlation was found between load rate from the force plate and wearables (smartphone: R 2 = 0.71 ; smartwatch: R 2 = 0.67 ).

Conclusion

Wearables' accelerometers can estimate load rate, and the good correlation with force plate data supports their use as a surrogate when assessing lower limb joint loading in field environments.

Site-specific Concurrent Validity of the ActiGraph GT9X Link in the Estimation of Activity-related Skeletal Loading

PURPOSE

The aims of this project were twofold: 1) to assess the concurrent validity of raw accelerometer outputs with ground reaction forces (GRF) and loading rates (LR) calculated from force plate across a range of simulated habitual PA and 2) to identify the optimal wear site among the ankle, hip, and wrist with the strongest relationships between accelerometer and force plate and/or skeletal outcomes.

METHODS

Thirty healthy young adults (23.0 ± 4.5 yr, 50% female) wore a triaxial accelerometer at the right ankle, hip, and wrist while performing eight trials of walking, jogging, running, low box drops, and high box drops over an in-ground force plate. Repeated-measures correlations and linear mixed models were used to assess concurrent validity of accelerometer and force plate outcomes across wear sites.

RESULTS

Strong repeated-measures associations were observed between peak hip resultant acceleration and resultant LR (rrm 1169 = 0.74, P < 0.001, 95% confidence interval = 0.718, 0.769) and peak hip resultant accelerations and resultant GRF (rrm 1169 = 0.69, P < 0.001, 95% confidence interval = 0.660, 0.720) when data were combined across activities. By contrast, small to moderate associations were seen between ankle-based outcomes and corresponding GRF and LR during walking and jogging (rrm 209 = 0.17-0.34, all P < 0.001). No significant associations were seen with wrist-based outcomes during any activity. In addition, linear mixed models suggested that 24%-50% of the variability in peak GRF and LR could be attributed to measured accelerations at the hip.

CONCLUSION

Peak accelerations measured at the hip were identified as the strongest proxies for skeletal loading assessed via force plate.

Wearable Devices for Physical Activity and Healthcare Monitoring in Elderly People: A Critical Review

The availability of wearable devices (WDs) to collect biometric information and their use during activities of daily living is significantly increasing in the general population. These small electronic devices, which record fitness and health-related outcomes, have been broadly utilized in industries such as medicine, healthcare, and fitness. Since they are simple to use and progressively cheaper, they have also been used for numerous research purposes. However, despite their increasing popularity, most of these WDs do not accurately measure the proclaimed outcomes. In fact, research is equivocal about whether they are valid and reliable methods to specifically evaluate physical activity and health-related outcomes in older adults, since they are mostly designed and produced considering younger subjects' physical and mental characteristics. Additionally, their constant evolution through continuous upgrades and redesigned versions, suggests the need for constant up-to-date reviews and research. Accordingly, this article aims to scrutinize the state-of-the-art scientific evidence about the usefulness of WDs, specifically on older adults, to monitor physical activity and health-related outcomes. This critical review not only aims to inform older consumers but also aid researchers in study design when selecting physical activity and healthcare monitoring devices for elderly people.

Does Site Matter? Impact of Inertial Measurement Unit Placement on the Validity and Reliability of Stride Variables During Running: A Systematic Review and Meta-analysis

BACKGROUND

Inertial measurement units (IMUs) are used for running gait analysis in a variety of sports. These sensors have been attached at various locations to capture stride data. However, it is unclear if different placement sites affect the derived outcome measures.

OBJECTIVE

The aim of this systematic review and meta-analysis was to investigate the impact of placement on the validity and reliability of IMU-derived measures of running gait.

METHODS

Online databases SPORTDiscus with Full Text, CINAHL Complete, MEDLINE (EBSCOhost), EMBASE (Ovid) and Scopus were searched from the earliest record to 6 August 2020. Articles were included if they (1) used an IMU during running (2) reported spatiotemporal variables, peak ground reaction force (GRF) or vertical stiffness and (3) assessed validity or reliability. Meta-analyses were performed for a pooled validity estimate when (1) studies reported means and standard deviation for variables derived from the IMU and criterion (2) used the same IMU placement and (3) determined validity at a comparable running velocity (≤ 1 m·s^-1 difference).

RESULTS

Thirty-nine articles were included, where placement varied between the foot, tibia, hip, sacrum, lumbar spine (LS), torso and thoracic spine (TS). Initial contact, toe-off, contact time (CT), flight time (FT), step time, stride time, swing time, step frequency (SF), step length (SL), stride length, peak vertical and resultant GRF and vertical stiffness were analysed. Four variables (CT, FT, SF and SL) were meta-analysed, where CT was compared between the foot, tibia and LS placements and SF was compared between foot and LS. Foot placement data were meta-analysed for FT and SL. All data are the mean difference (MD [95%CI]). No significant difference was observed for any site compared to the criterion for CT (foot: - 11.47 ms [- 45.68, 22.74], p = 0.43; tibia: 22.34 ms [- 18.59, 63.27], p = 0.18; LS: - 48.74 ms [- 120.33, 22.85], p = 0.12), FT (foot: 11.93 ms [- 8.88, 32.74], p = 0.13), SF (foot: 0.45 step·min^-1 [- 1.75, 2.66], p = 0.47; LS: - 3.45 step·min^-1 [- 16.28, 9.39], p = 0.37) and SL (foot: 0.21 cm [- 1.76, 2.18], p = 0.69). Reliable derivations of CT (coefficient of variation [CV] < 9.9%), FT (CV < 11.6%) and SF (CV < 4.4%) were shown using foot- and LS-worn IMUs, while the CV was < 7.8% for foot-determined stride time, SL and stride length. Vertical GRF was reliable from the LS (CV = 4.2%) and TS (CV = 3.3%) using a spring-mass model, while vertical stiffness was moderately (r = 0.66) and nearly perfectly (r = 0.98) correlated with criterion measures from the TS.

CONCLUSION

Placement of IMUs on the foot, tibia and LS is suitable to derive valid and reliable stride data, suggesting measurement site may not be a critical factor. However, evidence regarding the ability to accurately detect stride events from the TS is unclear and this warrants further investigation.

Low-pass filter cutoff frequency affects sacral-mounted inertial measurement unit estimations of peak vertical ground reaction force and contact time during treadmill running

Inertial measurement units (IMUs) are popular tools for estimating biomechanical variables such as peak vertical ground reaction force (GRF_v) and foot-ground contact time (t_c), often by using multiple sensors or predictive models. Despite their growing use, little is known about the effects of varying low-pass filter cutoff frequency, which can affect the magnitude of force-related dependent variables, the accuracy of IMU-derived metrics, or if simpler methods for such estimations exist. The purpose of this study was to investigate the effects of varying low-pass filter cutoff frequency on the correlation of IMU-derived peak GRF_v and t_c to gold-standard lab-based measurements. Thirty National Collegiate Athletics Association Division 1 cross country runners ran on an instrumented treadmill at a range of speeds while outfitted with a sacral-mounted IMU. A simple method for estimating peak GRF_v from the IMU was implemented by multiplying the IMU's vertical acceleration by the runner's body mass. Data from the IMU were low-pass filtered with 5, 10, and 30 Hz cutoffs. Pearson correlation coefficients were used to determine how well the IMU-derived estimates matched gold-standard biomechanical estimations. Correlations ranged from very weak to moderate for peak GRF_v and t_c. For peak GRF_v, the 10 Hz low-pass filter cutoff performed best (r = 0.638), while for t_c the 5 Hz cut-off performed best (r = 0.656). These results suggest that IMU-derived estimates of force and contact time are influenced by the low-pass filter cutoff frequency. Further investigations are needed to determine the optimal low-pass filter cutoff frequency or a different method to accurately estimate force and contact time is suggested.

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running

There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable sensors enable non-invasive monitoring of human movement in applied situations, current commercial wearables do not estimate tissue-level loading on structures inside the body. Here we explore the feasibility of using wearable sensors to estimate tibial bone force during running. First, we used lab-based data and musculoskeletal modeling to estimate tibial force for ten participants running across a range of speeds and slopes. Next, we converted lab-based data to signals feasibly measured with wearables (inertial measurement units on the foot and shank, and pressure-sensing insoles) and used these data to develop two multi-sensor algorithms for estimating peak tibial force: one physics-based and one machine learning. Additionally, to reflect current running wearables that utilize running impact metrics to infer musculoskeletal loading or injury risk, we estimated tibial force using a commonly measured impact metric, the ground reaction force vertical average loading rate (VALR). Using VALR to estimate peak tibial force resulted in a mean absolute percent error of 9.9%, which was no more accurate than a theoretical step counter that assumed the same peak force for every running stride. Our physics-based algorithm reduced error to 5.2%, and our machine learning algorithm reduced error to 2.6%. Further, to gain insights into how force estimation accuracy relates to overuse injury risk, we computed bone damage expected due to a given loading cycle. We found that modest errors in tibial force translated into large errors in bone damage estimates. For example, a 9.9% error in tibial force using VALR translated into 104% error in estimated bone damage. Encouragingly, the physics-based and machine learning algorithms reduced damage errors to 41% and 18%, respectively. This study highlights the exciting potential to combine wearables, musculoskeletal biomechanics and machine learning to develop more accurate tools for monitoring musculoskeletal loading in applied situations.

Continuous estimation of ground reaction force during long distance running within a fatigue monitoring framework: A Kalman filter-based model-data fusion approach

Estimation of ground reaction forces in runners has been limited to laboratory environments by means of instrumented treadmills, in-ground force plates and optoelectronic systems. Recent advances in estimation techniques using wearable sensors for kinematic analysis and sports performance could enable estimation outside the laboratory. This paper proposes a state-input-parameter estimation framework to continuously estimate the vertical ground reaction force waveform during running. By modeling a runner as a single degree of freedom mass-spring-damper with acceleration measurements at the sacrum a state-space formulation can be applied using Newtonian methods. A dual-Kalman filter is employed to estimate the unmeasured system input which feeds through to an unscented Kalman filter to estimate system dynamics and unknown model parameters (e.g. spring stiffness). For validation, 14 subjects performed three one-minute running trials at three different speeds (self-selected slow, comfortable, and fast) on a pressure-sensor-instrumented treadmill. The estimated vertical ground reaction force waveform parameters; peak vertical ground reaction force (RMSE=6.1-7.2%,ρ=0.95-0.97), vertical impulse (RMSE=8.5-13.0%,ρ=0.50-0.60), loading rate (RMSE=24.6-39.4%,ρ=0.85-0.93), and cadence RMSE<1%,ρ=1.00 were compared against the instrumented treadmill measurements. The proposed state-input-parameter estimation framework could monitor personalized vertical ground reaction force metrics for potential biofeedback applications. The feedback mechanism could provide information about the vertical ground reaction force characteristics to the runner as they are running to provide knowledge of both desirable and undesirable loading characteristics experienced.

Accelerometer Based Data Can Provide a Better Estimate of Cumulative Load During Running Compared to GPS Based Parameters

Running is a popular way to become or stay physically active and to maintain and improve one's musculoskeletal load tolerance. Despite the health benefits, running-related injuries affect millions of people every year and have become a substantial public health issue owing to the popularity of running. Running-related injuries occur when the musculoskeletal load exceeds the load tolerance of the human body. Therefore, it is crucial to provide runners with a good estimate of the cumulative loading during their habitual training sessions. In this study, we validated a wearable system to provide an estimate of the external load on the body during running and investigated how much of the cumulative load during a habitual training session is explained by GPS-based spatiotemporal parameters. Ground reaction forces (GRF) as well as 3D accelerations were registered in nine habitual runners while running on an instrumented treadmill at three different speeds (2.22, 3.33, and 4.44 m/s). Linear regression analysis demonstrated that peak vertical acceleration during running explained 80% of the peak vertical GRF. In addition, accelerometer-based as well as GPS-based parameters were registered during 498 habitual running session of 96 runners. Linear regression analysis showed that only 70% of the cumulative load (sum of peak vertical accelerations) was explained by duration, distance, speed, and the number of steps. Using a wearable device offers the ability to provide better estimates of cumulative load during a running program and could potentially serve as a better guide to progress safely through the program.

Investigation of the Relationship Between Peak Vertical Accelerations and Aerobic Exercise Intensity During Graded Walking and Running in Postmenopausal Women

How exercise intensity targets, calibrated according to oxygen consumption, relate to vertical impacts during weight-bearing exercise is currently unknown. The authors investigated the relationship between vertical peaks (VPs) and metabolic equivalents (METs) of oxygen consumption in 82 women during walking and running. The magnitude of VPs, measured using a hip-worn triaxial accelerometer, was derived from recommended aerobic exercise intensity targets. VPs were 0.63 ± 0.18g at the lower recommended absolute exercise intensity target (3 METs) but >1.5g at the upper end of moderate-intensity activities (1.90 ± 1.13g at 6 METs). Multilevel linear regression analyses identified speed and type of locomotion as the strongest independent predictors of VPs, explaining 54% and 11% of variance, respectively. The authors conclude that, in contrast to lower intensities, exercising close to or above the 6-MET threshold generates VPs of osteogenic potential, suggesting this could provide simultaneous benefits to decrease all-cause mortality and osteoporosis risk.

Accelerometer-based prediction of skeletal mechanical loading during walking in normal weight to severely obese subjects

There is no objective way to monitor mechanical loading characteristics during exercise for bone health improvement. We developed accelerometry-based equations to predict ground reaction force (GRF) and loading rate (LR) in normal weight to severely obese subjects. Equations developed had a high and moderate accuracy for GRF and LR prediction, respectively, thereby representing an accessible way to determine mechanical loading characteristics in clinical settings.

INTRODUCTION

There is no way to objectively prescribe and monitor exercise for bone health improvement in obese patients based on mechanical loading characteristics. We aimed to develop accelerometry-based equations to predict peak ground reaction forces (pGRFs) and peak loading rate (pLR) on normal weight to severely obese subjects.

METHODS

Sixty-four subjects (45 females; 84.6 ± 21.7 kg) walked at different speeds (2-6 km·h^-1) on a force plate-equipped treadmill while wearing accelerometers at lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Actual and predicted values at different speeds were compared by repeated measures ANOVA.

RESULTS

Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration transient rate for pLR prediction. All pGRF equation coefficients of determination were above 0.89, a good agreement between actual and predicted pGRFs, with a mean absolute percent error (MAPE) below 6.7%. No significant differences were observed between actual and predicted pGRFs at each walking speed. Accuracy indices from our equations were better than previously developed equations for normal weight subjects, namely a MAPE approximately 3 times smaller. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF.

CONCLUSION

Walking pGRF and pLR in normal weight to severely obese subjects can be predicted with moderate to high accuracy by accelerometry-based equations, representing an easy and accessible way to determine mechanical loading characteristics in clinical settings.

Effect of a load distribution system on mobility and performance during simulated and field hiking while under load

This study was conducted to test a modular scalable vest-load distribution system (MSV-LDS) against the plate carrier system (PC) currently used by the United States Marine Corps. Ten Marines engaged in 1.6 km load carriage trials in seven experimental conditions in a laboratory study. Kinematic, kinetic, and spatiotemporal gait parameters, muscle activity (electromyography), heart rate, caloric expenditure, shooting reaction times, and subjective responses were recorded. There was lower mean trapezius recruitment for the PC compared with the MSV-LDS for all conditions, and muscle activity was similar to baseline for the MSV-LDS. Twenty-seven Marines carrying the highest load were evaluated in the field, which measured an increase in energy expenditure with MSV-LDS; however, back discomfort was reduced. The field evaluation showed significantly reduced estimated ground reaction force on flat-ground segments with the MSV-LDS, and the data suggest both systems were comparable with respect to mobility and energy cost. Practitioner summary: This study found that a novel load distribution system appears to redistribute load for improved comfort as well as reduce estimated ground reaction force when engaged in hiking activities. Further, hiking with a load distribution system enables more neutral walking posture. Implications of load differences in loads carried are examined. Abbreviations: AGRF: anterior-posterior ground reaction forces; CAREN: Computer Assisted Rehabilitation Environment; GRF: ground reaction forces; HR: heart rate; ML-GRF: mediolateral ground reaction forces; MOLLE: Modular Lightweight Load-carrying Equipment; MSV-LDS: modular scalable vest-load distribution system; NHRC: Naval Health Research Center; PC: plate carrier; PPE: personal protective equipment; RPE: rating of perceived exertion; SAPI: small arms protective insert; sEMG: surface electromyography; USMC: United States Marine Corps; VGRF: Ground reaction forces in the vertical.

Exploring the potential utility of a wearable accelerometer for estimating impact forces in ballet dancers

Excessive forces and/or loading rates during landing may place ballet dancers at risk for overuse injury. The ability to estimate and monitor the landing forces of ballet dancers could help to improve injury prevention and rehabilitation; however, force platforms are not conducive to testing outside of a laboratory. Fortunately, it may be possible to indirectly assess landing forces via a wearable accelerometer. The purposes of this study were to examine the relationship between impact accelerations, recorded via a pelvis-worn accelerometer, and the peak forces and loading rates during performance of a common ballet manoeuvre, and to examine if a wearable accelerometer is sensitive to fatigue-related changes in landing forces. Fifteen ballet dancers continuously performed a ballet manoeuvre until self-determined exhaustion while impact accelerations and landing forces were simultaneously recorded using an accelerometer and force platforms. We observed very strong, positive relationships between the impact accelerations and the peak forces and loading rates during the landings. In addition, the changes in impact accelerations with fatigue paralleled the changes in the peak forces and loading rates. As a result, it appears that a wearable accelerometer could be used to estimate and monitor landing forces in ballet dancers.

Quantifying exposure to running for meaningful insights into running-related injuries

The very term 'running-related overuse injury' implies the importance of 'use', or exposure, to running. Risk factors for running-related injury can be better understood when exposure to running is quantified using either external or internal training loads. The advent of objective methods for quantifying exposure to running, such as global positioning system watches, smartphones, commercial activity monitors and research-grade wearable sensors, make it possible for researchers, coaches and clinicians to track exposure to running with unprecedented detail. This viewpoint discusses practical issues surrounding the use and analysis of data from such devices, including how wearable devices can be used to assess both internal and external training loads. We advocate for an integrative approach where data from multiple sources are used in combination to directly measure exposure to running in diverse settings.

Predicting Ground Reaction Force from a Hip-Borne Accelerometer during Load Carriage

INTRODUCTION

Ground reaction forces (GRF) during load carriage differ from unloaded walking. Methods to quantify peak vertical GRF (pGRFvert) of Soldiers walking with loads outside of a laboratory are needed to study GRF during operationally relevant tasks.

PURPOSE

Develop a statistically based model to predict pGRFvert during loaded walking from ActiGraph GT3X+ activity monitor (AM) vertical acceleration.

METHODS

Fifteen male Soldiers (25.4 ± 5.3 yr, 85.8 ± 9.2 kg, 1.79 ± 9.3 m) wore an ActiGraph GT3X+ AM over their right hip. Six walking trials (0.67-1.58 m·s) with four loads (no load, 15, 27, 46 kg) and two types of footwear (athletic shoes and combat boots) were completed on an instrumented force plate treadmill. Average peak vertical AM acceleration (pACCvert) and pGRFvert were used to develop a regression equation to predict pGRFvert. The model was validated using a leave-one-subject-out approach. Root mean square error (RMSE) and average absolute percent difference (AAPD) between actual and predicted pGRFvert were determined. pGRFvert was also predicted for two novel data sets and AAPD and RMSE calculated.

RESULTS

The final equation to predict pGRFvert included pACCvert, body mass, carried load mass, and pACCvert-carried load mass interaction. Cross-validation resulted in an AAPD of 4.0% ± 2.7% and an RMSE of 69.5 N for leave-one-subject-out and an AAPD of 5.5% ± 3.9% and an RMSE of 78.7 N for the two novel data sets.

CONCLUSION

A statistically based equation developed to predict pGRFvert from ActiGraph GT3X+ AM acceleration proved to be accurate to within 4% for Soldiers carrying loads while walking. This equation provides a means to predict GRF without a force plate.

Validity of block start performance without arm forces or by kinematics-only methods

The purpose of this study was to validate the calculation of reaction time (RT) and normalised power in block starts without considering arm ground reaction forces (GRFs) or using two kinematics-only methods. The RT and normalised power in the action phase were calculated using four different methods: using GRFs of arms and legs by force plates (whole F-based method), which can be regarded as the most valid method, using GRFs of legs captured by force plates (legs F-based method), using position of the centre of mass of the entire body captured by high-speed cameras (whole P-based method), and using only a partial subset of segment position (partial P-based method). Bland-Altman plots demonstrated that the RT of the legs F-based method was not similar to that of the whole F-based method: the mean difference was 7.4 ms and the 95% limits of agreement was-45.1 to 59.8 ms, and it was the least valid method for the calculation among the four methods. In contrast, the normalised power was more valid in the legs F-based method, followed by whole and partial P-based methods. This information will help researchers and practitioners to decide upon their analysis methods when analysing block start performance.

Assessment of gait kinetics in post-menopausal women using tri-axial ankle accelerometers during barefoot walking

BACKGROUND

Physical activity (PA) interventions, designed to increase exposure to ground reaction force (GRF) loading, are a common target for reducing fracture risk in post-menopausal women with low bone mineral density (BMD). Unfortunately, accurate tracking of PA in free-living environments and the ability to translate this activity into evaluations of bone health is currently limited.

RESEARCH QUESTION

This study evaluates the effectiveness of ankle-worn accelerometers to estimate the vertical GRFs responsible for bone and joint loading in post-menopausal women at a range of self-selected walking speeds during barefoot walking.

METHODS

Seventy women, at least one year post-menopause, wore Actigraph GT3X + on both ankles and completed walking trials at self-selected speeds (a minimum of five each at fast, normal and slow walking) along a 30 m instrumented walkway with force plates and photocells to measure loading and estimate gait velocity. Repeated measures correlation analysis and step-wise mixed-effects modelling were performed to evaluate significant predictors of peak vertical GRFs normalized to body weight (pVGRFbw), including peak vertical ankle accelerations (pVacc), walking velocity (Vel_w) and age.

RESULTS

A strong repeated measures correlation of r = 0.75 (95%CI [0.71-0.76] via 1000 bootstrap passes) between pVacc and pVGRFbw was observed. Five-fold cross-validation of mixed-model predictions yielded an average mean-absolute-error (MAE[95%CI]) and root-mean-square-error (RMSE) rate of 5.98%[5.61-6.42] and 0.076 [0.069-0.082] with a more complex model (including Vel_w,) and 6.80%[6.37-7.54] and 0.087BW[0.081-0.095] with a simpler model (including only pVacc), when comparing accelerometer-based estimations of pVGRFbw to force plate measures of pVGRFbw. Age was not found to be significant.

SIGNIFICANCE

This study is the first to show a strong relationship among ankle accelerometry data and high fidelity lower-limb loading approximations in post-menopausal women. The results provide the first steps necessary for estimation of real-world limb and joint loading supporting the goals of accurate PA tracking and improved individualization of clinical interventions.

Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: Implications for science, sport and wearable tech

INTRODUCTION

Tibial stress fractures are a common overuse injury resulting from the accumulation of bone microdamage due to repeated loading. Researchers and wearable device developers have sought to understand or predict stress fracture risks, and other injury risks, by monitoring the ground reaction force (GRF, the force between the foot and ground), or GRF correlates (e.g., tibial shock) captured via wearable sensors. Increases in GRF metrics are typically assumed to reflect increases in loading on internal biological structures (e.g., bones). The purpose of this study was to evaluate this assumption for running by testing if increases in GRF metrics were strongly correlated with increases in tibial compression force over a range of speeds and slopes.

METHODS

Ten healthy individuals performed running trials while we collected GRFs and kinematics. We assessed if commonly-used vertical GRF metrics (impact peak, loading rate, active peak, impulse) were strongly correlated with tibial load metrics (peak force, impulse).

RESULTS

On average, increases in GRF metrics were not strongly correlated with increases in tibial load metrics. For instance, correlating GRF impact peak and loading rate with peak tibial load resulted in r = -0.29±0.37 and r = -0.20±0.35 (inter-subject mean and standard deviation), respectively. We observed high inter-subject variability in correlations, though most coefficients were negligible, weak or moderate. Seventy-six of the 80 subject-specific correlation coefficients computed indicated that higher GRF metrics were not strongly correlated with higher tibial forces.

CONCLUSIONS

These results demonstrate that commonly-used GRF metrics can mislead our understanding of loading on internal structures, such as the tibia. Increases in GRF metrics should not be assumed to be an indicator of increases in tibial bone load or overuse injury risk during running. This has important implications for sports, wearable devices, and research on running-related injuries, affecting >50 scientific publications per year from 2015-2017.

The feasibility of predicting ground reaction forces during running from a trunk accelerometry driven mass-spring-damper model

Background

Monitoring the external ground reaction forces (GRF) acting on the human body during running could help to understand how external loads influence tissue adaptation over time. Although mass-spring-damper (MSD) models have the potential to simulate the complex multi-segmental mechanics of the human body and predict GRF, these models currently require input from measured GRF limiting their application in field settings. Based on the hypothesis that the acceleration of the MSD-model’s upper mass primarily represents the acceleration of the trunk segment, this paper explored the feasibility of using measured trunk accelerometry to estimate the MSD-model parameters required to predict resultant GRF during running.

Methods

Twenty male athletes ran at approach speeds between 2–5 m s⁻¹. Resultant trunk accelerometry was used as a surrogate of the MSD-model upper mass acceleration to estimate the MSD-model parameters (ACC_param) required to predict resultant GRF. A purpose-built gradient descent optimisation routine was used where the MSD-model’s upper mass acceleration was fitted to the measured trunk accelerometer signal. Root mean squared errors (RMSE) were calculated to evaluate the accuracy of the trunk accelerometry fitting and GRF predictions. In addition, MSD-model parameters were estimated from fitting measured resultant GRF (GRF_param), to explore the difference between ACC_param and GRF_param.

Results

Despite a good match between the measured trunk accelerometry and the MSD-model’s upper mass acceleration (median RMSE between 0.16 and 0.22 g), poor GRF predictions (median RMSE between 6.68 and 12.77 N kg⁻¹) were observed. In contrast, the MSD-model was able to replicate the measured GRF with high accuracy (median RMSE between 0.45 and 0.59 N kg⁻¹) across running speeds from GRF_param. The ACC_param from measured trunk accelerometry under- or overestimated the GRF_param obtained from measured GRF, and generally demonstrated larger within parameter variations.

Discussion

Despite the potential of obtaining a close fit between the MSD-model’s upper mass acceleration and the measured trunk accelerometry, the ACC_param estimated from this process were inadequate to predict resultant GRF waveforms during slow to moderate speed running. We therefore conclude that trunk-mounted accelerometry alone is inappropriate as input for the MSD-model to predict meaningful GRF waveforms. Further investigations are needed to continue to explore the feasibility of using body-worn micro sensor technology to drive simple human body models that would allow practitioners and researchers to estimate and monitor GRF waveforms in field settings.

Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.

Accelerometer-based prediction of running injury in National Collegiate Athletic Association track athletes

Running-related injuries (RRI) may result from accumulated microtrauma caused by combinations of high load magnitudes (vertical ground reaction forces; vGRFs) and numbers (strides). Yet relationships between vGRF and RRI remain unclear - potentially because previous research has largely been constrained to collecting vGRFs in laboratory settings and ignoring relationships between RRI and stride number. In this preliminary proof-of-concept study, we addressed these constraints: Over a 60-day period, each time collegiate athletes (n = 9) ran they wore a hip-mounted activity monitor that collected accelerations throughout the entire run. Accelerations were used to estimate peak vGRF, number of strides, and weighted cumulative loading (sum of peak vGRFs weighted to the 9th power) across the entirety of each run. Runners also reported their post-training pain/fatigue and any RRI that prevented training. Across 419 runs and >2.1 million strides, injured (n = 3) and uninjured (n = 6) participants did not report significantly different pain/fatigue (p = 0.56) or mean number of strides per run (p = 0.91). Injured participants did, however, have significantly greater peak vGRFs (p = 0.01) and weighted cumulative loading per run (p < 0.01). Results from this small but extensively studied sample of elite runners demonstrate that loading profiles (load magnitude-number combinations) quantified with activity monitors can provide valuable information that may prove essential for: (1) testing hypotheses regarding overuse injury mechanisms, (2) developing injury-prediction models, and (3) designing and adjusting athlete- and loading-specific training programs and feedback.

A New Proxy Measurement Algorithm with Application to the Estimation of Vertical Ground Reaction Forces Using Wearable Sensors

Measurement of the ground reaction forces (GRF) during walking is typically limited to laboratory settings, and only short observations using wearable pressure insoles have been reported so far. In this study, a new proxy measurement method is proposed to estimate the vertical component of the GRF (vGRF) from wearable accelerometer signals. The accelerations are used as the proxy variable. An orthogonal forward regression algorithm (OFR) is employed to identify the dynamic relationships between the proxy variables and the measured vGRF using pressure-sensing insoles. The obtained model, which represents the connection between the proxy variable and the vGRF, is then used to predict the latter. The results have been validated using pressure insoles data collected from nine healthy individuals under two outdoor walking tasks in non-laboratory settings. The results show that the vGRFs can be reconstructed with high accuracy (with an average prediction error of less than 5.0%) using only one wearable sensor mounted at the waist (L5, fifth lumbar vertebra). Proxy measures with different sensor positions are also discussed. Results show that the waist acceleration-based proxy measurement is more stable with less inter-task and inter-subject variability than the proxy measures based on forehead level accelerations. The proposed proxy measure provides a promising low-cost method for monitoring ground reaction forces in real-life settings and introduces a novel generic approach for replacing the direct determination of difficult to measure variables in many applications.

Development of a Plantar Load Estimation Algorithm for Evaluation of Forefoot Load of Diabetic Patients during Daily Walks Using a Foot Motion Sensor

Forefoot load (FL) contributes to callus formation, which is one of the pathways to diabetic foot ulcers (DFU). In this study, we hypothesized that excessive FL, which cannot be detected by plantar load measurements within laboratory settings, occurs in daily walks. To demonstrate this, we created a FL estimation algorithm using foot motion data. Acceleration and angular velocity data were obtained from a motion sensor attached to each shoe of the subjects. The accuracy of the estimated FL was validated by correlation with the FL measured by force sensors on the metatarsal heads, which was assessed using the Pearson correlation coefficient. The mean of correlation coefficients of all the subjects was 0.63 at a level corridor, while it showed an intersubject difference at a slope and stairs. We conducted daily walk measurements in two diabetic patients, and additionally, we verified the safety of daily walk measurement using a wearable motion sensor attached to each shoe. We found that excessive FL occurred during their daily walks for approximately three hours in total, when any adverse event was not observed. This study indicated that FL evaluation method using wearable motion sensors was one of the promising ways to prevent DFUs.

The effect of three surface conditions, speed and running experience on vertical acceleration of the tibia during running

Research has focused on parameters that are associated with injury risk, e.g. vertical acceleration. These parameters can be influenced by running on different surfaces or at different running speeds, but the relationship between them is not completely clear. Understanding the relationship may result in training guidelines to reduce the injury risk. In this study, thirty-five participants with three different levels of running experience were recruited. Participants ran on three different surfaces (concrete, synthetic running track, and woodchip trail) at two different running speeds: a self-selected comfortable speed and a fixed speed of 3.06 m/s. Vertical acceleration of the lower leg was measured with an accelerometer. The vertical acceleration was significantly lower during running on the woodchip trail in comparison with the synthetic running track and the concrete, and significantly lower during running at lower speed in comparison with during running at higher speed on all surfaces. No significant differences in vertical acceleration were found between the three groups of runners at fixed speed. Higher self-selected speed due to higher performance level also did not result in higher vertical acceleration. These results may show that running on a woodchip trail and slowing down could reduce the injury risk at the tibia.

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

BACKGROUND

High average (VALR) and instantaneous vertical loading rates (VILR) during impact have been associated with many running-related injuries. Peak acceleration (PA), measured with an accelerometer, has provided an alternative method to estimate impact loading during outdoor running. This study sought to compare both intra- and inter-subject correlations between vertical loading rates and PA measured at two body sites during running.

METHODS

Ground reaction force data were collected from 10 healthy adults (age=23.6±3.8 years) during treadmill running at different speeds and inclination surfaces. Concurrently, PAs at the lateral malleoli and the distal tibia were measured using synchronized accelerometers.

RESULTS

We found significant positive intra-subject correlation between loading rates and PA at the lateral malleoli (r=0.561-0.950, p<0.001) and the distal tibia (r=0.486-0.913, p<0.001). PA measured at the lateral malleoli showed stronger correlation with loading rates (p=0.004) than the measurement at the distal tibia. On the other hand, inter-subject variances were observed in the association between PA and vertical loading rates. The inter-subject variances at the distal tibia were 3.88±3.09BW/s and 5.69±3.05BW/s in VALR and VLIR respectively. Similarly, the inter-subject variances in the measurement at lateral malleoli were 5.24±2.85BW/s and 6.67±2.83BW/s in VALR and VLIR respectively.

CONCLUSIONS

PA measured at lateral malleoli has stronger correlation with VALR or VILR than the measurement at distal tibia. Caution is advised when using PA to conduct inter-subject comparisons of vertical loading rates during running.