Classification of deadlift biomechanics with wearable inertial measurement units

The deadlift is a compound full-body exercise that is fundamental in resistance training, rehabilitation programs and powerlifting competitions. Accurate quantification of deadlift biomechanics is important to reduce the risk of injury and ensure training and rehabilitation goals are achieved. This study sought to develop and evaluate deadlift exercise technique classification systems utilising Inertial Measurement Units (IMUs), recording at 51.2 Hz, worn on the lumbar spine, both thighs and both shanks. It also sought to compare classification quality when these IMUs are worn in combination and in isolation. Two datasets of IMU deadlift data were collected. Eighty participants first completed deadlifts with acceptable technique and 5 distinct, deliberately induced deviations from acceptable form. Fifty-five members of this group also completed a fatiguing protocol (3-Repition Maximum test) to enable the collection of natural deadlift deviations. For both datasets, universal and personalised random-forests classifiers were developed and evaluated. Personalised classifiers outperformed universal classifiers in accuracy, sensitivity and specificity in the binary classification of acceptable or aberrant technique and in the multi-label classification of specific deadlift deviations. Whilst recent research has favoured universal classifiers due to the reduced overhead in setting them up for new system users, this work demonstrates that such techniques may not be appropriate for classifying deadlift technique due to the poor accuracy achieved. However, personalised classifiers perform very well in assessing deadlift technique, even when using data derived from a single lumbar-worn IMU to detect specific naturally occurring technique mistakes.     

Human crawling performance and technique revealed by inertial measurement units

Human crawling performance and technique are of broad interest to roboticists, biomechanists, and military personnel. This study explores the variables that define crawling performance in the context of an outdoor obstacle course used by military organizations worldwide to evaluate the effects of load and personal equipment on warfighter performance. Crawling kinematics, measured from four body-worn inertial measurement units (IMUs) attached to the upper arms and thighs, are recorded for thirty-three participants. The IMU data is distilled to four metrics of crawling performance; namely, crawl speed, crawl stride time, ipsilateral limb coordination, and contralateral limb coordination. We hypothesize that higher performance (as identified by higher crawl speeds) is associated with more coordinated limbs and lower stride times. A cluster analysis groups participants into high and low performers exhibiting statistically significant differences across the four performance metrics. In particular, high performers exhibit superior limb coordination associated with a “diagonal gait” in which contralateral limbs move largely in-phase to produce faster crawl speeds and shorter crawl stride times. In contrast, low performers crawl at slower speeds with longer crawl stride times and less limb coordination. Beyond these conclusions, a major contribution of this study is a method for deploying wearable IMUs to study crawling in contextually relevant (i.e. non-laboratory) environments.     

Validation of a model-based inverse kinematics approach based on wearable inertial sensors

Abstract

Wearable inertial measurement units (IMUs) are a promising solution to human motion estimation. Using IMUs 3D orientations, a model-driven inverse kinematics methodology to estimate joint angles is presented. Estimated joint angles were validated against encoder-measured kinematics (robot) and against marker-based kinematics (passive mechanism). Results are promising, with RMS angular errors respectively lower than 3 and 6?deg over a minimum range of motion of 50?deg (robot) and 160?deg (passive mechanism). Moreover, a noise robustness analysis revealed that the model-driven approach reduces the effects of experimental noises, making the proposed technique particularly suitable for application in human motion analysis.     

Stride length determination during overground running using a single foot-mounted inertial measurement unit

From a research perspective, detailed knowledge about stride length (SL) is important for coaches, clinicians and researchers because together with stride rate it determines the speed of locomotion. Moreover, individual SL vectors represent the integrated output of different biomechanical determinants and as such provide valuable insight into the control of running gait. In recent years, several studies have tried to estimate SL using body-mounted inertial measurement units (IMUs) and have reported promising results. However, many studies have used systems based on multiple sensors or have only focused on estimating SL for walking. Here we test the concurrent validity of a single foot-mounted, 9-degree of freedom IMU to estimate SL for running. We employed a running-specific, Kalman filter based zero-velocity update (ZUPT) algorithm to calculate individual SL vectors with the IMU and compared the results to SLs that were simultaneously recorded by a 6-camera 3D motion capture system. The results showed that the analytical procedures were able to successfully identify all strides that were recorded by the camera system and that excellent levels of absolute agreement (ICC(3,1) = 0.955) existed between the two methods. The findings demonstrate that individual SL vectors can be accurately estimated with a single foot-mounted IMU when running in a controlled laboratory setting.     

The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units

The use of inertial measurement units (IMUs) for gait analysis has emerged as a tool for clinical applications. Shank gyroscope signals have been utilized to identify heel-strike and toe-off, which serve as the foundation for calculating temporal parameters of gait such as single and double limb support time. Recent publications have shown that toe-off occurs later than predicted by the dual minima method (DMM), which has been adopted as an IMU-based gait event detection algorithm. In this study, a real-time algorithm, Noise-Zero Crossing (NZC), was developed to accurately compute temporal gait parameters. Our objective was to determine the concurrent validity of temporal gait parameters derived from the NZC algorithm against parameters measured by an instrumented walkway. The accuracy and precision of temporal gait parameters derived using NZC were compared to those derived using the DMM. The results from Bland-Altman Analysis showed that the NZC algorithm had excellent agreement with the instrumented walkway for identifying the temporal gait parameters of Gait Cycle Time (GCT), Single Limb Support (SLS) time, and Double Limb Support (DLS) time. By utilizing the moment of zero shank angular velocity to identify toe-off, the NZC algorithm performed better than the DMM algorithm in measuring SLS and DLS times. Utilizing the NZC algorithm’s gait event detection preserves DLS time, which has significant clinical implications for pathologic gait assessment.     

The influence of inertial sensor sampling frequency on the accuracy of measurement parameters in rearfoot running

Increasingly, inertial sensors are being used for running analyses. The aim of this study was to systematically investigate the influence of inertial sensor sampling frequencies (SF) on the accuracy of kinematic, spatio-temporal, and kinetic parameters. We hypothesized that running analyses at lower SF result in less signal information and therefore the inability to sufficiently interpret measurement data. Twenty-one subjects participated in this study. Rearfoot strikers ran on an indoor running track at a velocity of 3.5 ± 0.1 ms^?1. A uniaxial accelerometer was attached at the tibia and an inertial measurement unit was mounted at the heel of the right shoe. All sensors were synchronized at the start and data was measured with 1000 Hz (reference SF). Datasets were reduced to 500, 333, 250, 200, and 100 Hz in post-processing. The results of this study showed that a minimum SF of 500 Hz should be used to accurately measure kinetic parameters (e.g. peak heel acceleration). In contrast, stride length showed accurate results even at 333 Hz. 200 Hz were required to calculate parameters accurately for peak tibial acceleration, stride duration, and all kinematic measurements. The information from this study is necessary to correctly interpret measurement data of existing investigations and to plan future studies.     

Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking

The aim of this study was to investigate if a machine learning algorithm utilizing triaxial accelerometer, gyroscope, and magnetometer data from an inertial motion unit (IMU) could detect surface- and age-related differences in walking. Seventeen older (71.5 ± 4.2 years) and eighteen young (27.0 ± 4.7 years) healthy adults walked over flat and uneven brick surfaces wearing an inertial measurement unit (IMU) over the L5 vertebra. IMU data were binned into smaller data segments using 4-s sliding windows with 1-s step lengths. Ninety percent of the data were used as training inputs and the remaining ten percent were saved for testing. A deep learning network with long short-term memory units was used for training (fully supervised), prediction, and implementation. Four models were trained using the following inputs: all nine channels from every sensor in the IMU (fully trained model), accelerometer signals alone, gyroscope signals alone, and magnetometer signals alone. The fully trained models for surface and age outperformed all other models (area under the receiver operator curve, AUC = 0.97 and 0.96, respectively; p ≤ .045). The fully trained models for surface and age had high accuracy (96.3, 94.7%), precision (96.4, 95.2%), recall (96.3, 94.7%), and f1-score (96.3, 94.6%). These results demonstrate that processing the signals of a single IMU device with machine-learning algorithms enables the detection of surface conditions and age-group status from an individual’s walking behavior which, with further learning, may be utilized to facilitate identifying and intervening on fall risk.     

Assessment of the measurement accuracy of inertial sensors during different tasks of daily living

The low cost and ease of use of inertial measurement units (IMUs) make them an attractive option for motion analysis tasks that cannot be easily measured in a laboratory. To date, only a limited amount of research has been conducted comparing commercial IMU systems to optoelectronic systems, the gold standard, for everyday tasks like stair climbing and inclined walking. In this paper, the 3D joint angles of the lower limbs are determined using both an IMU system and an optoelectronic system for twelve participants during stair ascent and descent, and inclined, declined and level walking. Three different datasets based on different hardware and anatomical models were collected for the same movement in an effort to determine the cause and quantify the errors involved with the analysis. Firstly, to calculate software errors, two different anatomical models were compared for one hardware system. Secondly, to calculate hardware errors, results were compared between two different measurement systems using the same anatomical model. Finally, the overall error between both systems with their native anatomical models was calculated. Statistical analysis was performed using statistical parametric mapping. When both systems were evaluated based on the same anatomical model, the number of trials with significant differences decreased markedly. Thus, the differences in joint angle measurement can mainly be attributed to the variability in the anatomical models used for calculations and not to the IMU hardware.     

Strategy quantification using body worn inertial sensors in a reactive agility task

Agility performance is often evaluated using time-based metrics, which provide little information about which factors aid or limit success. The objective of this study was to better understand agility strategy by identifying biomechanical metrics that were sensitive to performance speed, which were calculated with data from an array of body-worn inertial sensors. Five metrics were defined (normalized number of foot contacts, stride length variance, arm swing variance, mean normalized stride frequency, and number of body rotations) that corresponded to agility terms defined by experts working in athletic, clinical, and military environments. Eighteen participants donned 13 sensors to complete a reactive agility task, which involved navigating a set of cones in response to a vocal cue. Participants were grouped into fast, medium, and slow performance based on their completion time. Participants in the fast group had the smallest number of foot contacts (normalizing by height), highest stride length variance (normalizing by height), highest forearm angular velocity variance, and highest stride frequency (normalizing by height). The number of body rotations was not sensitive to speed and may have been determined by hand and foot dominance while completing the agility task. The results of this study have the potential to inform the development of a composite agility score constructed from the list of significant metrics. By quantifying the agility terms previously defined by expert evaluators through an agility score, this study can assist in strategy development for training and rehabilitation across athletic, clinical, and military domains.     

Dynamic accuracy of inertial measurement units during simple pendulum motion

A motion measurement system based on inertial measurement units (IMUs) has been suggested as an alternative to contemporary video motion capture. This paper reports an investigation into the accuracy of IMUs in estimating 3D orientation during simple pendulum motion. The IMU vendor's (XSens Technologies) accuracy claim of 3° root mean squared (RMS) error is tested. IMUs are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. The motion of a pendulum swing was measured using both IMUs and video motion capture as a reference. The IMU raw data were processed by the Kalman filter algorithm supplied by the vendor and a custom fusion algorithm developed by the authors. The IMU measurement of pendulum motion using the vendor's Kalman filter algorithm did not compare well with the video motion capture with a RMS error of between 8.5° and 11.7° depending on the length and type of pendulum swing. The maximum orientation error was greater than 30°, occurring approximately eight seconds into the motion. The custom fusion algorithm estimation of orientation compared well with the video motion capture with a RMS error of between 0.8° and 1.3°. Future research should concentrate on developing a general purpose fusion algorithm and vendors of IMUs should provide details about the errors to be expected in different measurement situations, not just those in a ‘best case’ scenario.     

Estimation of temporal parameters during sprint running using a trunk-mounted inertial measurement unit

The purpose of this study was to identify consistent features in the signals supplied by a single inertial measurement unit (IMU), or thereof derived, for the identification of foot-strike and foot-off instants of time and for the estimation of stance and stride duration during the maintenance phase of sprint running. Maximal sprint runs were performed on tartan tracks by five amateur and six elite athletes, and durations derived from the IMU data were validated using force platforms and a high-speed video camera, respectively, for the two groups. The IMU was positioned on the lower back trunk (L1 level) of each athlete. The magnitudes of the acceleration and angular velocity vectors measured by the IMU, as well as their wavelet-mediated first and second derivatives were computed, and features related to foot-strike and foot-off events sought. No consistent features were found on the acceleration signal or on its first and second derivatives. Conversely, the foot-strike and foot-off events could be identified from features exhibited by the second derivative of the angular velocity magnitude. An average absolute difference of 0.005 s was found between IMU and reference estimates, for both stance and stride duration and for both amateur and elite athletes. The 95% limits of agreement of this difference were less than 0.025 s. The results proved that a single, trunk-mounted IMU is suitable to estimate stance and stride duration during sprint running, providing the opportunity to collect information in the field, without constraining or limiting athletes' and coaches' activities.     

Stance and swing phase knee flexion recover at different rates following total knee arthroplasty: An inertial measurement unit study

Total knee arthroplasty (TKA) is the most common joint replacement in the United States. Range of motion (ROM) monitoring includes idealized clinic measures (e.g. goniometry during passive ROM) that may not accurately represent knee function. Accordingly, a novel, portable, inertial measurement unit (IMU) based ROM measurement method was developed, validated, and implemented. Knee flexion was computed via relative motion between two IMUs and validated via optical motion capture (p > 0.05). Prospective analyses of 10 healthy individuals (5M, 50 ± 19 years) and 20 patients undergoing TKA (3 lost to follow up, 10M, 65 ± 6 years) were completed. Controls wore IMUs for 1-week. Patients wore IMUs for 1-week pre-TKA, 6-weeks immediately post-TKA, and 1-week at 1-year post-TKA. Flexion was computed continuously each day (8–12 h). Metrics included daily maximum flexion and flexion during stance/swing phases of gait. Maximum flexion was equal between cohorts at all time points. Contrastingly, patient stance and swing flexion were reduced pre-TKA, yet improved post-TKA. Specifically, patient stance and swing flexion were reduced below control/pre-TKA values during post-TKA week 1. Stance flexion exceeded pre-TKA and equaled control levels after week 2. However, swing flexion only exceeded pre-TKA and equaled control levels at 1-year post-TKA. This novel method improves upon the accuracy/portability of current methods (e.g. goniometry). Interestingly, surgery did not impact maximum ROM, yet improved the ability to flex during gait allowing more efficient and safe ambulation. This is the first study continuously monitoring long-term flexion before/after TKA. The results offer richer information than clinical measures about expected TKA rehabilitation.     

Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors

Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU).Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants’ free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time.The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant’s field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants’ estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users’ field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.     

Sensor-to-body calibration procedure for clinical motion analysis of lower limb using magnetic and inertial measurement units

Magnetic and Inertial measurement units (MIMUs) have become exceedingly popular for ambulatory human motion analysis during the past two decades. However, measuring anatomically meaningful segment and joint kinematics requires virtual alignment of the MIMU frame with the anatomical frame of its corresponding segment. Therefore, this paper presents a simple calibration procedure, based on MIMU readouts, to align the inertial frame of the MIMU with the anatomical frames, as recommended by ISB. The proposed calibration includes five seconds of quiet standing in a neutral posture followed by ten consecutive hip flexions/extensions. This procedure will independently calibrate MIMUs attached to the pelvis, thigh, shank, and foot. The accuracy and repeatability of the calibration procedure and the 3D joint angle estimation were validated against the gold standard motion capture system by an experimental study with ten able-bodied participants. The procedure showed high test-retest repeatability in aligning the MIMU frame with its corresponding anatomical frame, i.e., the helical angle between the MIMU and anatomical frames did not significantly differ between the test and retest sessions (except for thigh MIMU). Compared to previously introduced procedures, this procedure attained the highest inter-participant repeatability (inter-participant coefficient of variations of the helical angle: 20.5–42.2%). Further, the proposed calibration would reduce the offset errors of the 3D joint angle estimation (up to 12.8 degrees on average) compared to joint angle estimation without calibration (up to 26.3 degrees on average). The proposed calibration enables MIMU to measure clinically meaningful gait kinematics.     

Monitoring hip posture in total hip arthroplasty using an inertial measurement unit-based hip smart trial system: An in vitro validation experiment using a fixed pelvis model

Intraoperative measurement of hip posture is the basis for assessing hip range of motion (ROM) and predicting postoperative functional limits allowable for activities of daily living. Although computer navigation for total hip arthroplasty (THA) has improved the accuracy of intraoperative ROM evaluation, it has not gained widespread popularity due to its complex and time-consuming protocol. We therefore developed an inertial measurement unit-based hip smart trial system (IMUHST) for intraoperative monitoring of hip posture. An in vitro validation experiment was conducted using bone models with a three-dimensional measurement model as the reference standard. The absolute mean error, Bland – Altman analysis and intra-class correlation coefficient demonstrated that the validity and reliability of this system meets the requirement for clinical application. Given that monitoring posture is the basis for evaluating the direction(s) of potential impingement, subluxation and dislocation, the IMUHST is a promising development direction of computer assisted surgery in THA.     

Driving a musculoskeletal model with inertial and magnetic measurement units

We developed and evaluated a new kinematic driver for musculoskeletal models using ambulatory inertial and magnetic measurement units (IMMUs). The new driver uses the orientation estimates based on sensor fusion of each individual IMMU and benefits from two important properties of musculoskeletal models. First, these models contain more complex, anatomical, kinematic models than those currently used for sensor fusion of multiple IMMUs and are continuously improved. Second, they allow movement between segment and measured sensor. For three different tasks, the new IMMU driver, (optical) marker drivers and a combination of both were used to reconstruct the motion. Maximal root mean square (RMS) joint angle differences with respect to the silver standard (combined IMMU/marker drivers) were found for the hip joint; 4°, 2° and 5° during squat, gait and slideboard tasks for IMMU-driven reconstructions, compared with 6°, 5° and 5° for marker-driven reconstructions, respectively. The measured angular velocities corresponded best to the IMMU-driven reconstructions, with a maximal RMS difference of 66°/s, compared with 108°/s and 91°/s for marker-driven reconstructions and silver standard. However, large oscillations in global accelerations occurred during IMMU-driven reconstructions resulting in a maximal RMS difference with respect to measured acceleration of 23 m/s², compared with 9 m/s² for reconstructions that included marker drivers. The new driver facilitates direct implementation of IMMU-based orientation estimates in currently available biomechanical models. As such, it can help in the rapid expansion of biomechanical analysis based on outdoor measurements.     

The measurement of body segment inertial parameters using dual energy X-ray absorptiometry

Accurate body segment parameter (BSP) information is required for dynamic analyses of motion and the current methods available for obtaining these BSPs have been criticized. The purpose of this study was to determine whether dual energy X-ray absorptiometry (DXA) could accurately measure the BSPs of scanned objects and thus be used as a tool for measuring the BSPs of human subjects. Whole body mass (WBM) of 11 males was measured from a DXA scan and the values were compared to criterion scale-measured values by calculating the mean percent error. Two objects (plastic cylinder, human cadaver leg) were also scanned and DXA measurements of mass, length, centre of mass location (CM) and moment of inertia about the centre of mass (I_CM) were made using custom software. Criterion BSP measurements were then made and compared to DXA BSP values by calculating the percent error. Criterion I_CM measurements of the two objects were made using a pendulum technique and a second criterion I_CM calculation was made for the cylinder using a geometric formula. A mean percent error of −1.05% ±1.32% was found for WBM measurements of the human subjects. Errors for the cylinder and cadaver leg were under 3.2% for all BSPs except for I_CM when DXA was compared to the pendulum method (14.3% and 8.2% for cylinder and leg, respectively). The errors between DXA and the pendulum method were attributed to uncertainty in the pendulum technique (J. Biomech. 2002, in Review). I_CM error of the cylinder when DXA was compared to the geometric calculation was 2.63%. This error, combined with the low errors for all other BSPs, indicated that DXA can be used as a simple and accurate means of obtaining direct BSP information on living humans.     

Optimal inertial sensor location for ambulatory measurement of trunk inclination

Trunk inclination (TI) is used often to quantify back loading in ergonomic workplace evaluation. The aim of the present study was to determine whether TI can be obtained using a single inertial sensor (IS) on the back, and to determine the optimal IS location on the back for the estimation of TI. Gold standard TI, the angle between the vertical and the line connecting the L5/S1 joint and the trunk centre of mass, was measured using an optoelectronic system. Ten subjects performed experimental trials, each consisting of a symmetric and an asymmetric lifting task, and of a left–right lateral flexion movement. Trials were repeated and, in between trials, the IS was shifted in small steps from a location on the thorax towards a location on the sacrum. Optimal IS location was defined as the IS location with minimum root-mean-square (RMS) error between the gold standard TI and the IS TI. Averaged over subjects, the optimal IS location for symmetric and asymmetric lifting was at about 25% of the distance from the midpoint between the posterior superior iliac spines (MPSIS) to the C7 spinous process. The RMS error at this location, averaged over subjects, was 4.6±2.9°. For the left–right lateral flexion task, the optimal IS location was at about 30% of the MPSIS to C7 distance. Because in most activities of daily living, pure lateral flexion does not occur often, it is recommended place the IS at 25% of the distance from the MPSIS to C7.     

The static accuracy and calibration of inertial measurement units for 3D orientation

Inertial measurement units (IMUs) are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. Wearable motion capture systems based on IMUs have been advertised as alternatives to optical motion capture. In this paper, the accuracy of five different IMUs of the same type in measuring 3D orientation in static situations, as well as the calibration of the accelerometers and magnetometers within the IMUs, has been investigated. The maximum absolute static orientation error was 5.2°, higher than the 1° claimed by the vendor. If the IMUs are re-calibrated at the time of measurement with the re-calibration procedure described in this paper, it is possible to obtain an error of less than 1°, in agreement with the vendor's specifications (XSens Technologies B.V. 2005. Motion tracker technical documentation Mtx-B. Version 1.03. Available from: www.xsens.com).

The new calibration appears to be valid for at least 22 days providing the sensor is not exposed to high impacts. However, if several sensors are ‘daisy chained’ together changes to the magnetometer bias can cause heading errors of up to 15°. The results demonstrate the non-linear relationship between the vendor's orthogonality claim of < 0.1° and the accuracy of 3D orientation obtained from factory calibrated IMUs in static situations. The authors hypothesise that the high magnetic dip (64°) in our laboratory may have exacerbated the errors reported. For biomechanical research, small relative movements of a body segment from a calibrated position are likely to be more accurate than large scale global motion that may have an error of up to 9.8°.