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.     

Measurement of stride parameters using a wearable GPS and inertial measurement unit

Both GPS and inertial measurement units (IMUs) have been extensively used in biomechanical studies. Expensive high accuracy GPS units can provide information about intrastride speed and position, but their application is limited by their size and cost. Single and double integration of acceleration from IMU provides information about short-term fluctuations in speed and position, but suffers from integration error over a longer period of time. The integration of GPS and IMU has been widely used in large and expensive units designed for survey and vehicle navigation. Here we propose a data fusion scheme, which is a Kalman filter based complementary filter and enhances the frequency response of the GPS and IMU used alone. We also report the design of a small (28 g) low cost GPS/IMU unit. Its accuracy after post-processing with the proposed data fusion scheme for determining average speed and intrastride variation was compared to a traditional high cost survey GPS. The low cost unit achieved an accuracy of 0.15 ms⁻¹ (s.d.) for horizontal speed in cycling and human running across a speed range of 3–10 ms⁻¹. The stride frequency and vertical displacement calculated based on measurements from the low cost GPS/IMU units had an s.d. of 0.08 Hz and 0.02 m respectively, compared to measurements from high performance OEM4 GPS units.     

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.     

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.     

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°.     

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.     

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.     

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.     

Validation of magneto-inertial measuring units for measuring hip joint angles

Camera-based motion capture systems are the current gold standard for motion analysis. However, the use of wireless inertial sensor-based systems is increasing in popularity, largely due to convenient portability. The purpose of this study was to validate the use of wireless inertial sensors for measuring hip joint motion with a functional calibration requiring only one motion (walking) and neutral standing. Data were concurrently collected using a 10-camera motion capture system and a wireless inertial sensor-based system. Hip joint angles were measured for 10 participants during walking, jumping jack, and bilateral squat tasks and for a subset (n = 5) a jump turn task. Camera-based system hip joint angles were calculated from retro-reflective marker positions and sensor-based system angles were calculated in MATLAB using the sensor output quaternions. Most hip joint angles measured with the sensor-based system were within 6° of angles measured with the camera motion capture system. Accurate measurement of motion outside of a laboratory setting has broad implications for diagnosing movement abnormalities, monitoring sports performance, and assessing rehabilitation progress.     

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.     

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.     

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.     

Concurrent validation of an inertial measurement system to quantify kicking biomechanics in four football codes

Wearable inertial measurement systems (IMS) allow for three-dimensional analysis of human movements in a sport-specific setting. This study examined the concurrent validity of a IMS (Xsens MVN system) for measuring lower extremity and pelvis kinematics in comparison to a Vicon motion analysis system (MAS) during kicking. Thirty footballers from Australian football (n = 10), soccer (n = 10), rugby league and rugby union (n = 10) clubs completed 20 kicks across four conditions. Concurrent validity was assessed using a linear mixed-modelling approach, which allowed the partition of between and within-subject variance from the device measurement error. Results were expressed in raw and standardised units for assessments of differences in means and measurement error, and interpreted via non-clinical magnitude-based inferences. Trivial to small differences were found in linear velocities (foot and pelvis), angular velocities (knee, shank and thigh), sagittal joint (knee and hip) and segment angle (shank and pelvis) means (mean difference: 0.2–5.8%) between the IMS and MAS in Australian football, soccer and the rugby codes. Trivial to small measurement errors (from 0.1 to 5.8%) were found between the IMS and MAS in all kinematic parameters. The IMS demonstrated acceptable levels of concurrent validity compared to a MAS when measuring kicking biomechanics across the four football codes. Wearable IMS offers various benefits over MAS, such as, out-of-laboratory testing, larger measurement range and quick data output, to help improve the ecological validity of biomechanical testing and the timing of feedback. The results advocate the use of IMS to quantify biomechanics of high-velocity movements in sport-specific settings.     

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.     

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.     

Multi-day Longitudinal Assessment of Physical Activity and Sleep Behavior Among Healthy Young and Older Adults Using Wearable Sensors

ObjectivesThe number of elderly people is growing rapidly and aging is found to affect activities of daily living. Older adults are found to perform less physical activity when compared to younger ones. In the perspective of movement behavior, it is not well understood how are elderly different from younger ones. It is not known whether they produce only low frequency movement accelerations or the overall number of movements produced are reduced in elderly. It is also not known how elderly and younger ones perform movement transitions throughout the duration of a day and during night-time sleep.Material and methodsIn this study, 10 healthy young and 10 healthy old participants wore inertial measurement unit at their lower back for 3-days. The 24 hours of day were divided into four 6 hour time zones and transitions made by young and elderly were investigated. All participants performed their regular daily activities unhindered and longitudinal multi-day signals for acceleration and angular velocity were analyzed. Time-frequency analysis was performed using wavelet transform and frequency content of each movement performed was computed.ResultsWe found that both young and older adults performed significantly more low amplitude movements than medium and high amplitude movements. Healthy young adults produced significantly more movements at 1.1 Hz than older adults. Healthy young adults were also found to have produced significantly smaller number of transitions in the mid-phases of sleep. They were also found to produce significantly larger accelerations during night-time sleep transitions than their older counterparts.ConclusionThe advantages of collecting longitudinal data about human movement and sleep transition data can lead us to important clinical diagnosis. The information from longitudinal assessment can help develop lifestyle interventions for disease prevention, monitoring of chronic diseases to prevent or slow disease progression among elderly people.     

Continuous ambulatory hand force monitoring during manual materials handling using instrumented force shoes and an inertial motion capture suit

Hand forces (HFs) are commonly measured during biomechanical assessment of manual materials handling; however, it is often a challenge to directly measure HFs in field studies. Therefore, in a previous study we proposed a HF estimation method based on ground reaction forces (GRFs) and body segment accelerations and tested it with laboratory equipment: GFRs were measured with force plates (FPs) and segment accelerations were measured using optical motion capture (OMC). In the current study, we evaluated the HF estimation method based on an ambulatory measurement system, consisting of inertial motion capture (IMC) and instrumented force shoes (FSs).Sixteen participants lifted and carried a 10-kg crate from ground level while 3D full-body kinematics were measured using OMC and IMC, and 3D GRFs were measured using FPs and FSs. We estimated 3D hand force vectors based on: (1) FP+OMC, (2) FP+IMC and (3) FS+IMC. We calculated the root-mean-square differences (RMSDs) between the estimated HFs to reference HFs calculated based on crate kinematics and the GRFs of a FP that the crate was lifted from.Averaged over subjects and across 3D force directions, the HF RMSD ranged between 10-15N when using the laboratory equipment (FP + OMC), 11-18N when using the IMC instead of OMC data (FP+IMC), and 17-21N when using the FSs in combination with IMC (FS + IMC). This error is regarded acceptable for the assessment of spinal loading during manual lifting, as it would results in less than 5% error in peak moment estimates.