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Validity of using tri-axial accelerometers to measure human movement – Part II: Step counts at a wide range of gait velocities

A subject-specific step counting method with a high accuracy level at all walking speeds is needed to assess the functional level of impaired patients. The study aim was to validate step counts and cadence calculations from acceleration data by comparison to video data during dynamic activity. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, thigh, and waist of 11 healthy adults. ICC values were greater than 0.98 for video inter-rater reliability of all step counts. The activity monitoring system (AMS) algorithm demonstrated a median (interquartile range; IQR) agreement of 92% (8%) with visual observations during walking/jogging trials at gait velocities ranging from 0.1 to 4.8m/s, while FitBits (ankle and waist), and a Nike Fuelband (wrist) demonstrated agreements of 92% (36%), 93% (22%), and 33% (35%), respectively. The algorithm results demonstrated high median (IQR) step detection sensitivity (95% (2%)), positive predictive value (PPV) (99% (1%)), and agreement (97% (3%)) during a laboratory-based simulated free-living protocol. The algorithm also showed high median (IQR) sensitivity, PPV, and agreement identifying walking steps (91% (5%), 98% (4%), and 96% (5%)), jogging steps (97% (6%), 100% (1%), and 95% (6%)), and less than 3% mean error in cadence calculations.

Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection.

A robust method for identifying movement in the free-living environment is needed to objectively measure physical activity. The purpose of this study was to validate the identification of postural orientation and movement from acceleration data against visual inspection from video recordings. Using tri-axial accelerometers placed on the waist and thigh, static orientations of standing, sitting, and lying down, as well as dynamic movements of walking, jogging and transitions between postures were identified. Additionally, subjects walked and jogged at self-selected slow, comfortable, and fast speeds. Identification of tasks was performed using a combination of the signal magnitude area, continuous wavelet transforms and accelerometer orientations. Twelve healthy adults were studied in the laboratory, with two investigators identifying tasks during each second of video observation. The intraclass correlation coefficients for inter-rater reliability were greater than 0.95 for all activities except for transitions. Results demonstrated high validity, with sensitivity and positive predictive values of greater than 85% for sitting and lying, with walking and jogging identified at greater than 90%. The greatest disagreement in identification accuracy between the algorithm and video occurred when subjects were asked to fidget while standing or sitting. During variable speed tasks, gait was correctly identified for speeds between 0.1m/s and 4.8m/s. This study included a range of walking speeds and natural movements such as fidgeting during static postures, demonstrating that accelerometer data can be used to identify orientation and movement among the general population.

Center of pressure trajectory during gait: A comparison of four foot positions

Knowledge of the center of pressure (COP) trajectory during stance can elucidate possible foot pathology, provide comparative effectiveness of foot orthotics, and allow for appropriate calculation of balance control and joint kinetics during gait. Therefore, the goal of this study was to investigate the COP movement when walking at self-selected speeds with plantigrade, equinus, inverted, and everted foot positions. A total of 13 healthy subjects were asked to walk barefoot across an 8-m walkway with embedded force plates. The COP was computed for each stance limb using the ground reaction forces and moments collected from three force plates. Results demonstrated that the COP excursion was 83% of the foot length and 27% of the foot width in the anterior-posterior and medial lateral directions for plantigrade walking, respectively. Regression equations explained 94% and 44% of the anterior-posterior and medial-lateral COP variability during plantigrade walking, respectively. While the range of motion and COP velocity were similar for inverted and everted walking, the COP remained on the lateral and medial aspects of the foot for these two walking conditions, respectively. A reduced anterior-posterior COP range of motion and velocity were demonstrated during equinus walking. Ankle joint motion in the frontal and sagittal planes supported this COP movement, with increased inversion and plantar flexion demonstrated during inverted and equinus conditions, respectively. Results from this study demonstrated the COP kinematics during simulated pathological gait conditions, with the COP trajectory providing an additional tool for the evaluation of patients with pathology.

Posture and Movement Classification: The Comparison of Tri-Axial Accelerometer Numbers and Anatomical Placement

Patient compliance is important when assessing movement, particularly in a free-living environment when patients are asked to don their own accelerometers. Reducing the number of accelerometers could increase patient compliance. The aims of this study were (1) to determine and compare the validity of different accelerometer combinations and placements for a previously developed posture and dynamic movement identification algorithm. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, right thigh, and waist of 12 healthy adults. Subjects performed a protocol in the laboratory including static orientations of standing, sitting, and lying down, and dynamic movements of walking, jogging, transitions between postures, and fidgeting to simulate free-living activity. When only one accelerometer was used, the thigh was found to be the optimal placement to identify both movement and static postures, with a misclassification error of 10%, and demonstrated the greatest accuracy for walking/fidgeting and jogging classification with sensitivities and positive predictive value (PPVs) greater than 93%. When two accelerometers were used, the waist-thigh accelerometers identified movement and static postures with greater accuracy than the thigh-ankle accelerometers (with a misclassification error of 11% compared to 17%). However, the thigh-ankle accelerometers demonstrated the greatest accuracy for walking/ fidgeting and jogging classification with sensitivities and PPVs greater than 93%. Movement can be accurately classified in healthy adults using tri-axial accelerometers placed on one or two of the following sites: waist, thigh, or ankle. Posture and transitions require an accelerometer placed on the waist and an accelerometer placed on the thigh.

Step detection using multi- versus single tri-axial accelerometer-based systems.

Multiple sensors are often considered necessary for increased step count accuracy. However, subject adherence to device-wear increases using a minimal number of activity monitors (AMs). The study aims were to determine and compare the validity of using multiple AMs versus a single AM to detect steps by comparison to video using a modification of an algorithm previously developed for a four-accelerometer AM system capable, unlike other algorithms, of accurate step detection for gait velocities as low as 0.1 m s(-1). Twelve healthy adults wore ankle, thigh and waist AMs while performing walking/jogging trials at gait velocities from 0.1-4.8 m s(-1) and a simulated free-living dynamic activities protocol. Nineteen older adults wore ankle and waist AMs while walking at velocities from 0.5-2.0 m s(-1). As little as one AM (thigh or waist) accurately detected steps for velocities  >0.5 m s(-1). A single ankle AM accurately detected steps for velocities  ⩾0.1 m s(-1). Only the thigh AM could not accurately detect steps during the dynamic activities. Only the thigh-ankle combination or single waist AM could accurately distinguish between walking and jogging steps. These laboratory-based results suggest that the presented algorithm can accurately detect steps in a free-living environment using only one ankle or waist AM.

Dynamic stability margin using a marker based system and Tekscan: A comparison of four gait conditions

Stability during gait is maintained through control of the center of mass (CoM) position and velocity in relation to the base of support (BoS). The dynamic stability margin, or the interaction of the extrapolated center of mass with the closest boundary of the BoS, can reveal possible control errors during gait. The purpose of this study was to investigate a marker based method for defining the BoS, and compare the dynamic stability margin throughout gait in comparison to a BoS defined from foot pressure sensors. The root mean squared difference between these two methodologies ranged from 0.9 cm to 3.5 cm, when walking under four conditions: plantigrade, equinus, everted, and inverted. As the stability margin approaches -35 cm prior to contralateral heel strike, there was approximately 90% agreement between the two systems at this time point. Underestimation of the marker based dynamic stability margin or overestimation of the pressure based dynamic stability margin was due to inaccuracies in defining the medial boundary of the BoS. Overall, care must be taken to ensure similar definitions of the BoS are utilized when comparing the dynamic stability margin between participants and gait conditions.

Accelerations of the waist and lower extremities over a range of gait velocities to aid in activity monitor selection for field-based studies.

This study aimed to define accelerations measured at the waist and lower extremities over a range of gait velocities to provide reference data for choosing the appropriate accelerometer for field-based human activity monitoring studies. Accelerations were measured with a custom activity monitor (± 16g) at the waist, thighs, and ankles in 11 participants over a range of gait velocities from slow walking to running speeds. The cumulative frequencies and peak accelerations were determined. Cumulative acceleration amplitudes for the waist, thighs, and ankles during gait velocities up to 4.8 m/s were within the standard commercial g-range (± 6g) in 99.8%, 99.0%, and 96.5% of the data, respectively. Conversely, peak acceleration amplitudes exceeding the limits of many commercially available activity monitors were observed at the waist, thighs, and ankles, with the highest peaks at the ankles, as expected. At the thighs, and more so at the ankles, nearly 50% of the peak accelerations would not be detected when the gait velocity exceeds a walking velocity. Activity monitor choice is application specific, and investigators should be aware that when measuring high-intensity gait velocity activities with commercial units that impose a ceiling at ± 6g, peak accelerations may not be measured.

A principal component analysis approach to correcting the knee flexion axis during gait

Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotomies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post-hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms. Gait data were collected on twelve healthy subjects using standard marker placement as well as intentionally misplaced lateral knee markers. The efficacy of the algorithm was assessed by quantifying the reduction in knee angle errors. Crosstalk error was quantified from the coefficient of determination (r(2)) between knee flexion and adduction angles. Mean rotation offset error (αo) was quantified from the knee and hip rotation kinematics across the gait cycle. The principal component analysis (PCA)-based algorithm significantly reduced r(2) (p<0.001) and caused αo,knee to converge toward 11.9±8.0° of external rotation, demonstrating improved certainty of the knee kinematics. The within-subject standard deviation of αo,hip between marker placements was reduced from 13.5±1.5° to 0.7±0.2° (p<0.001), demonstrating improved precision of the knee kinematics. The PCA-based algorithm performed at levels comparable to a knee abduction-adduction minimization algorithm (Baker et al., 1999) and better than a null space algorithm (Schwartz and Rozumalski, 2005) for this healthy subject population.

Dynamic assessment of center of pressure measurements from an instrumented AMTI treadmill with controlled precision

With the increasing use of instrumented force treadmills in biomechanical research, it is imperative that the validity of center of pressure (COP) measurements is established. The study aims were to compare an instrumented treadmills static-belt COP accuracy to that of a floor-embedded platform, develop a novel method to quantify dynamic-belt COP accuracy with controlled precision and perform an initial investigation of how dynamic COP accuracy changes with weight and velocity. Static COP accuracy was assessed by applying a force while moving a rigid rod in a circular clockwise motion at nine positions of interest on the two treadmill and two ground-embedded force plates. Dynamic COP accuracy was assessed for weights (68.0, 102.1, and 136.1kg), applied through a ball bearing of 2.54cm circumference, with peak treadmill belt speeds of 0.5, 0.75, and 1.0m/s. COP accuracy was assessed relative to motion capture marker trajectories. Statically, treadmill COP error was similar to that of the ground-embedded force plates and that reported for other treadmills. Dynamically, COP error appeared to vary systematically with weight and velocity and in the case of anteroposterior COP error, shear force, although testing with a larger number of weights and velocities is needed to fully define the relationship. This novel method can be used to assess any instrumented treadmills dynamic COP accuracy with controlled precision.

Identification of True Knee Flexion Axis Despite Marker Misplacement Using Principal Component Analysis

Gait analysis is useful for revealing pathologic tibial rotation in children with cerebral palsy (CP), thus guiding interventions of de-rotational osteotomy [1]. Surface marker misplacement may cause an inaccurate definition of the knee flexion axis, which generates downstream artifacts such as cross talk between knee rotation angles and internal rotation errors, misleading these patients’ assessments and surgical interventions [2,3]. Therefore, the purpose of this study was to develop and evaluate a novel method for defining the true knee flexion axis from existing marker trajectories during gait. We hypothesized that the devised method would accurately quantify the offset in the flexion axis due to a systematically misplaced marker and correct the downstream crosstalk and rotation errors of the knee joint angles associated with the marker misplacement.Copyright