Gold Standard Motion Capture Research Articles

Assessment of an IMU-Based Experimental Set-Up for Upper Limb Motion in Obese Subjects.

In recent years, wearable systems based on inertial sensors opened new perspectives for functional motor assessment with respect to the gold standard motion capture systems. The aim of this study was to validate an experimental set-up based on 17 body-worn inertial sensors (Awinda, Xsens, The Netherlands), addressing specific body segments with respect to the state-of-the art system (VICON, Oxford Metrics Ltd., Oxford, UK) to assess upper limb kinematics in obese, with respect to healthy subjects. Twenty-three obese and thirty healthy weight individuals were simultaneously acquainted with the two systems across a set of three tasks for upper limbs (i.e., frontal arm rise, lateral arm rise, and reaching). Root Mean Square error (RMSE) was computed to quantify the differences between the measurements provided by the systems in terms of range of motion (ROM), whilst their agreement was assessed via Pearson's correlation coefficient (PCC) and Bland-Altman (BA) plots. In addition, the signal waveforms were compared via one-dimensional statistical parametrical mapping (SPM) based on a paired t-test and a two-way ANOVA was applied on ROMs. The overall results partially confirmed the correlation and the agreement between the two systems, reporting only a moderate correlation for shoulder principal rotation angle in each task (r~0.40) and for elbow/flexion extension in obese subjects (r = 0.66), whilst no correlation was found for most non-principal rotation angles (r < 0.40). Across the performed tasks, an average RMSE of 34° and 26° was reported in obese and healthy controls, respectively. At the current state, the presence of bias limits the applicability of the inertial-based system in clinics; further research is intended in this context.

Validation of a Wearable System for Lower Extremity Assessment.

Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients' functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor-based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold-standard motion capture and gait measurement systems. Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor-based wearable system, a gold-standard motion capture system, and a pressure-based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t-test was conducted for inter-group comparison to analyze the data. The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold-standard pressure-based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold-standard video-based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. This IMU-based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA.

A portable and reliable tool for on-site physical reaction time (RT) measurement

The drop-stick test system (DTS) invention has the capability to measure reaction accurately for sideline mild traumatic brain injury (mTBI) assessment. The reaction time (RT) measurements showed moderate to good inter-instrument reliability with an overall ICC of 0.82 (95 % CI 0.78–0.85). RT is a useful biomarker of mTBI or concussion, but existing technologies in controlled laboratory environments are not feasible for assessments in the field. With wearable technologies and wireless connection with smartphones, it is now easier to conduct RT assessments on the field. The purpose was to develop a portable DTS involving wearable inertial sensors translatable from the laboratory to the sideline assessments on athletic fields. This project investigated if RT measurements using sensors are reliable metrics when compared to the gold-standard motion capture laboratory-test. The intent was to invent and assess the reliability of a clinical DTS using sensors in conjunction with an easy RT measurement protocol. The integrated inertial sensors in the DTS have the potential to reliably capture RT, allowing it to more easily translate into the training room or field environment.

Review on Mandibular Muscle Kinematics.

The complexity of mandibular dynamics encourages constant research as a vehicle to improve oral health. The gold standard motion capture system might help us to understand its functioning and its relation to body position, aiming to perform an exhaustive bibliographic review in the Dentistry field. Six different electronic databases were used (Dentistry & Oral Sciences Source, Scopus, Web of Science, PubMed, CINAHL and SPORTDiscus) in April 2022. The selection criteria includes a biography, critical analysis, and the full text from 1984 to April 2022, based on the odontological gold standard, whether or not in combination with additional devices. Clinical cases, bibliographic reviews or meta-analysis and grey literature were excluded. The checklist of the critical assessment methodology by Joanna Brigs was used (JBI). After choosing scientific articles published in peer-reviewed journals, 23 out of 186 investigations were classified as eligible with a total of 384 participants. The issue being addressed is related to the speech properties, posture and body movement in relation to dento-oro-facial muscle and facial analysis, mandibular kinematics and mandibular dynamics during the mastication process. The markers arrangement depends on the dynamic to be analysed. From a physiologic and pathologic perspective, the applications of the optic system are relevant in Dentistry. The scarcity of literature obtained implies the need for future research.

Validity and Reliability of the Leomo Motion-Tracking Device Based on Inertial Measurement Unit with an Optoelectronic Camera System for Cycling Pedaling Evaluation.

Background: The use of inertial measurement sensors (IMUs), in the search for a more ecological measure, is spreading among sports professionals with the aim of improving the sports performance of cyclists. The kinematic evaluation using the Leomo system (TYPE-R, Leomo, Boulder, CO, USA) has become popular. Purpose: The present study aimed to evaluate the reliability and validity of the Leomo system by measuring the angular kinematics of the lower extremities in the sagittal plane during pedaling at different intensities compared to a gold-standard motion capture camera system (OptiTrack, Natural Point, Inc., Corvallis, OR, USA). Methods: Twenty-four elite cyclists recruited from national and international cycling teams performed two 6-min cycles of cycling on a cycle ergometer at two different intensities (first ventilatory threshold (VT1) and second ventilatory threshold (VT2)) in random order, with a 5 min rest between intensity conditions. The reliability and validity of the Leomo system versus the motion capture system were evaluated. Results: Both systems showed high validity and were consistently excellent in foot angular range Q1 (FAR (Q1)) and foot angular range (FAR) (ICC-VT1 between 0.91 and 0.95 and ICC-VT2 between 0.88 and 0.97), while the variables leg angular range (LAR) and pelvic angle showed a modest validity (ICC-VT1 from 0.52 to 0.71 and ICC-VT2 between 0.61 and 0.67). Compared with Optitrack, Leomo overestimated all the variables, especially the LAR and pelvic angle values, in a range between 12 and 15°. Conclusions: Leomo is a reliable and valid tool for analyzing the ranges of motion of the cyclist’s lower limbs in the sagittal plane, especially for the variables FAR (Q1) and FAR. However, its systematic error for FAR and Pelvic Angle values must be considered in sports performance analysis.

Mobile Robot Assisted Gait Monitoring and Dynamic Margin of Stability Estimation

To assess balance control and fall risk, it is desirable to continuously monitor dynamic stability during walking tasks. Dynamic Margin of Stability (MoS) is widely recognized as a quantitative measure for human walking stability and gait balance strategies. We propose a mobile robot assisted gait monitoring system that precedes human subjects in overground walking. Real-time data from the RGB-D Kinect sensor on the robot are fused with measurement from pressure sensors and inertial measurement units in a pair of instrumented footwear, and Kalman filter based methods are developed to estimate MoS and spatiotemporal gait parameters in real time. Experimental results with 10 subjects are compared with those obtained by a gold-standard motion capture system. Results show that the proposed method achieves acceptable accuracy of MoS estimation and high accuracy for spatio-temporal gait parameters. Whereas existing works on MoS assessment use wearable sensors that can only provide offline analysis, our proposed system provides real time gait monitoring and MoS estimation that could potentially assess fall risk during walking in out-of-lab conditions.

The validation of a low-cost inertial measurement unit system to quantify simple and complex upper-limb joint angles

The purpose of this research was to validate the use of a low-cost IMU system to measure upper-limb joint angles by comparing it to passive optical motion capture measures. Fifteen participants (five females; 25.9 ± 4.7 years) completed one trial of four simple range of motion (ROM) movements (elbow flexion/extension, shoulder abduction/adduction, shoulder flexion/extension, and shoulder internal/external rotation), and three complex functional daily tasks [hand to: back pocket (HBP), contralateral shoulder (HCS), head (HTH)]. Movements were measured, simultaneously, using fourteen OptiTrack cameras and five Notch® IMUs. The mean joint angle difference between devices ranged from 0.10° ± 3.11° for the HBP shoulder internal/external movement to 44.95° ± 3.50° for the simple ROM shoulder internal/external rotation movement. Nine of sixteen movement and plane comparisons showed significant differences between the device–specific movement cycle waveforms. Eleven of the comparisons showed either fixed and/or proportional biases (fixed only: 9; proportional only: 1; both fixed and proportional: 1). Due to multiple movements having large amplitude errors, low waveform similarities, and/or statistically significant mean differences between the Notch® IMUs and the gold-standard motion capture devices, we cannot recommend that Notch® IMUs are valid devices for measuring upper-limb joint angles during simple ROM and complex functional daily tasks.

Validation of a Sensor-Based Gait Analysis System with a Gold-Standard Motion Capture System in Patients with Parkinson's Disease.

Digital technologies provide the opportunity to analyze gait patterns in patients with Parkinson’s Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to compare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland–Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.

Estimating Center of Mass Kinematics During Perturbed Human Standing Using Accelerometers.

Estimating center of mass (COM) through sensor measurements is done to maintain walking and standing stability with exoskeletons. The authors present a method for estimating COM kinematics through an artificial neural network, which was trained by minimizing the mean squared error between COM displacements measured by a gold-standard motion capture system and recorded acceleration signals from body-mounted accelerometers. A total of 5 able-bodied participants were destabilized during standing through: (1)unexpected perturbations caused by 4 linear actuators pulling on the waist and (2)volitionally moving weighted jars on a shelf. Each movement type was averaged across all participants. The algorithm's performance was quantified by the root mean square error and coefficient of determination (R2) calculated from both the entire trial and during each perturbation type. Throughout the trials and movement types, the average coefficient of determination was 0.83, with 89% of the movements with R2 > .70, while the average root mean square error ranged between 7.3% and 22.0%, corresponding to 0.5- and 0.94-cm error in both the coronal and sagittal planes. COM can be estimated in real time for balance control of exoskeletons for individuals with a spinal cord injury, and the procedure can be generalized for other gait studies.

Moving outside the lab: Markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump

Markerless motion capture using deep learning approaches have potential to revolutionize the field of biomechanics by allowing researchers to collect data outside of the laboratory environment, yet there remain questions regarding the accuracy and ease of use of these approaches. The purpose of this study was to apply a markerless motion capture approach to extract lower limb angles in the sagittal plane during the vertical jump and to evaluate agreement between the custom trained model and gold standard motion capture. We performed this study using a large open source data set (N = 84) that included synchronized commercial video and gold standard motion capture. We split these data into a training set for model development (n = 69) and test set to evaluate capture performance relative to gold standard motion capture using coefficient of multiple correlations (CMC) (n = 15). We found very strong agreement between the custom trained markerless approach and marker-based motion capture within the test set across the entire movement (CMC > 0.991, RMSE < 3.22°), with at least strong CMC values across all trials for the hip (0.853 ± 0.23), knee (0.963 ± 0.471), and ankle (0.970 ± 0.055). The strong agreement between markerless and marker-based motion capture provides evidence that markerless motion capture is a viable tool to extend data collection to outside of the laboratory. As biomechanical research struggles with representative sampling practices, markerless motion capture has potential to transform biomechanical research away from traditional laboratory settings into venues convenient to populations that are under sampled without sacrificing measurement fidelity.

Comparison of the Performance of the Leap Motion ControllerTM with a Standard Marker-Based Motion Capture System.

Over the last few years, the Leap Motion Controller™ (LMC) has been increasingly used in clinical environments to track hand, wrist and forearm positions as an alternative to the gold-standard motion capture systems. Since the LMC is marker-less, portable, easy-to-use and low-cost, it is rapidly being adopted in healthcare services. This paper demonstrates the comparison of finger kinematic data between the LMC and a gold-standard marker-based motion capture system, Qualisys Track Manager (QTM). Both systems were time synchronised, and the participants performed abduction/adduction of the thumb and flexion/extension movements of all fingers. The LMC and QTM were compared in both static measuring finger segment lengths and dynamic flexion movements of all fingers. A Bland–Altman plot was used to demonstrate the performance of the LMC versus QTM with Pearson’s correlation (r) to demonstrate trends in the data. Only the proximal interphalangeal joint (PIP) joint of the middle and ring finger during flexion/extension demonstrated acceptable agreement (r = 0.9062; r = 0.8978), but with a high mean bias. In conclusion, the study shows that currently, the LMC is not suitable to replace gold-standard motion capture systems in clinical settings. Further studies should be conducted to validate the performance of the LMC as it is updated and upgraded.

Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle.

Over half of older adult falls are caused by tripping. Many of these trips are likely due to obstacles present on walkways that put older adults or other individuals with low foot clearance at risk. Yet, Minimum Foot Clearance (MFC) values have not been measured in real-world settings and existing methods make it difficult to do so. In this paper, we present the Minimum Foot Clearance Estimation (MFCE) system that includes a device for collecting calibrated video data from pedestrians on outdoor walkways and a computer vision algorithm for estimating MFC values for these individuals. This system is designed to be positioned at ground level next to a walkway to efficiently collect sagittal plane videos of many pedestrians’ feet, which is then processed offline to obtain MFC estimates. Five-hundred frames of video data collected from 50 different pedestrians was used to train (370 frames) and test (130 frames) a convolutional neural network. Finally, data from 10 pedestrians was analyzed manually by three raters and compared to the results of the network. The footwear detection network had an Intersection over Union of 85% and was able to find the bottom of a segmented shoe with a 3-pixel average error. Root Mean Squared (RMS) errors for the manual and automated methods for estimating MFC values were 2.32 mm, and 3.70 mm, respectively. Future work will compare the accuracy of the MFCE system to a gold standard motion capture system and the system will be used to estimate the distribution of MFC values for the population.

Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System.

The knee flexion-extension angle is an important variable to be monitored in various clinical scenarios, for example, during physical rehabilitation assessment. The purpose of this work is to develop and validate a sensor fusion system based on a knee sleeve for monitoring of physical therapy. The system consists of merging data from two inertial measurement units (IMUs) and an intensity-variation based Polymer Optical Fiber (POF) curvature sensor using a quaternion-based Multiplicative Extended Kalman Filter (MEKF). The proposed data fusion method is magnetometer-free and deals with sensors' uncertainties through reliability intervals defined during gait. Walking trials were performed by twelve healthy participants using our knee sleeve system and results were validated against a gold standard motion capture system. Additionally, a comparison with other three knee angle estimation methods, which are exclusively based on IMUs, was carried out. The proposed system presented better performance (mean RMSE 3.3 °, LFM coefficients, a1 = 0.99 ± 0.04, a0 = 0.70 ± 2.29, R2 = 0.98 ± 0.01 and ρC 0.99) when compared to the other evaluated methods. Experimental results demonstrate the usability and feasibility of our system to estimate knee motion with high accuracy, repeatability, and reproducibility. This wearable system may be suitable for motion assessment in rehabilitation labs in future studies.

Smart-Cover: A real time sitting posture monitoring system

Prolonged asymmetrical sitting is common and can exacerbate musculoskeletal back pain and spinal deformities. Monitoring sitting posture can help maintain correct posture and prevent health problems. Currently posture is assessed by expert clinicians using subjective visual observation. More objective methods involve the use of a gold-standard motion capture system in Laboratories, which is expensive and not widely available. We develop a Smart-Cover, an automatic system to provide real time visualization and information about sitting posture. A Sitting Pressure Sensor (SPS) is built using Velostat, conductive fabric and foam to collect pressure distribution information within the seat surface. The data are collected from 10 healthy young subjects where each subject sits for 30 min and are transferred to a cloud server using Internet-of-Things (IoT). A rule-based classifier is used to provide timely notification to users about sitting duration and level of asymmetry. A new end-point device application is developed to show sitting balance. Dial based displays show level of asymmetry, active and static sitting, and daily summary score. Our system is user friendly, inexpensive, automatic and quantitative method for evaluating posture in real time, which has the capability of providing quantitative information about sitting behaviour. The results show that our system can be used for objective monitoring of sitting posture which has medical and social benefits. Smart-Cover can support the management of patients during their rehabilitation by monitoring pressure areas and balance when seated. In addition, it offers users at home, schools and offices the means to improve their sitting posture.

Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement.

Participation in sports has risen in the United States over the last few years, increasing the risk of injuries such as tears to the anterior cruciate ligament (ACL) in the knee. Previous studies have shown a correlation between knee kinematics when landing from a jump and this injury. The purpose of this study was to validate the ability of a commercially available inertial measurement units (IMUs) to accurately measure knee joint angles during a dynamic movement. Eight healthy subjects participated in the study. Validation was performed by comparing the angles measured by the wearable device to those obtained through the gold standard motion capture system when landing from a jump. Root mean square, linear regression analysis, and Bland–Altman plots were performed/constructed. The mean difference between the wearable device and the motion capture data was 8.4° (flexion/extension), 4.9° (ab/adduction), and 3.9° (rotation). In addition, the device was more accurate at smaller knee angles. In our study, a commercially available wearable IMU was able to perform fairly well under certain conditions and was less accurate in other conditions.

Electronically augmented gait abnormality assessment following lower extremity trauma.

Background:Objective evaluation of patient outcomes has become an essential component of patient management. Along with patient-reported outcomes, performance-based measures (PBMs) such as gait analysis are an important part of this evaluation. The purpose of this study was to evaluate the validity of utilizing a wearable inertial measurement unit (IMU) in an outpatient clinic setting to assess its ability to provide clinically relevant data in patients with altered gait resulting from lower extremity trauma.Methods:Five orthopaedic trauma patients with varying degrees of gait pathologies were compared to 5 healthy control subjects. Kinematic data were simultaneously recorded by the IMU and a gold standard Vicon video motion analysis system (Vicon Motion Systems Ltd, Oxford, UK) during a modified 10-m walk test. Raw data captured by the IMU were directly compared to Vicon data. Additionally, 5 objective gait parameters were compared for controls and the 5 trauma patients.Results:The IMU data streams strongly correlated with Vicon data for measured variables used in the subsequent gait analysis: vertical acceleration, vertical displacement, pitch angular velocity, and roll angular velocity (Pearson r-value > 0.9 for all correlations). Quantitative kinematic data in post-trauma patients significantly differed from control data and correlated with observed gait pathology.Conclusions:When compared to the gold standard motion capture reference system (Vicon), an IMU can reliably and accurately measure clinically relevant gait parameters and differentiate between normal and pathologic gait patterns. This technology is easily integrated into clinical settings, requires minimal time, and represents a performance-based method for quantifiably assessing gait outcomes.Level of Evidence:Diagnostic Level 1.

A quantitative method for evaluation of 6 degree of freedom virtual reality systems

Modern virtual reality systems such as the HTC Vive enable users to be immersed in a virtual world. Validation of the HTC Vive and other contemporaneous systems for use in clinic, research, and industry applications will assure users and developers that games and applications made for these systems are accurate representations of the real world. The purpose of this study was to develop a standardized method for testing the translational and rotational capabilities of VR systems such as the HTC Vive. The translational and rotational capabilities of the HTC Vive were investigated using an industry grade robot arm and a gold standard motion capture system. It was found that the average difference between reported translational distances traveled was 0.74 ± 0.42 mm for all room-scale calibration trials and 0.63 ± 0.27 mm for all standing calibration trials. The mean difference in angle rotated was 0.46 ± 0.46° for all room-scale calibration trials and 0.66 ± 0.40° for all standing calibration trials. When tested using human movement, the average difference in distance traveled was 3.97 ± 3.37 mm. Overall, the HTC Vive shows promise as a tool for clinic, research, and industry and its controllers can be accurately tracked in a variety of situations. The methodology used for this study can easily be replicated for other VR systems so that direct comparisons can be made as new systems become available.

Head mounted displays for capturing head kinematics in postural tasks

Tracking head motion in a simple, portable and accurate manner during performance of postural tasks in a virtual reality environment could have important implications for investigating normal and pathological head kinematics. We investigated concurrent validity of head tracking of two Head Mounted Displays (HMDs), Oculus Rift and HTC Vive, vs. a gold-standard motion capture system (Qualisys). Head kinematics of N = 20 healthy young adults was quantified during static and dynamic postural tasks. While wearing the Oculus Rift or HTC Vive, participants observed moving stars (static tasks) or a flying ball (dynamic task). Head kinematics were recorded simultaneously by the Rift or Vive and Qualisys camera system. We calculated head directional path, acceleration in 6 directions and volume of translation movement. Intra-Class Correlations (ICC) and 95% Limits of agreement were calculated. Most ICC values were around 0.9 with several at 0.99 indicating excellent agreement between the HMDs and Qualisys. Weaker agreement was observed for vertical displacement during a static task and moderate agreement was observed pitch and yaw displacement during a dynamic task. A negative bias of a small magnitude (indicating more movement in VR) was observed for most variables in static tasks, while a positive bias was observed for most variables in the dynamic task (indicating less movement in VR). Our results generally support the concurrent validity of Oculus Rift and HTC Vive head tracking during static and dynamic standing tasks in healthy young adults. Specific task- and direction-dependent differences should be considered when planning measurement studies using these novel tools.

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.

Proposal and Validation of a Knee Measurement System for Patients With Osteoarthritis.

Currently most measurements of knee joint function are obtained through observation and patient-reported outcomes. This paper proposes an implementation and validation of a knee monitor to measure quantitative joint data in multiple degrees of freedom. The proposed system is configurable with minimal patient interaction and no frame-alignment calibration procedure is required for measurement after visually placing/replacing sensors on patients. A mobile software system was developed using a method of extracting clinical knee angles based on attitude estimations from independent wearable sensors. Validation was performed using a robot phantom and results were compared with a gold standard motion capture system. Two instrumentation placements (lateral and posterior) were examined. A posterior sensor placement was determined to provide the most repeatable results through multiple degrees of freedom and measurement accuracy approached a gold standard motion capture technology with low root-mean-square error (flexion: 3.34°, internal/external rotation: 2.18°, and varus/valgus: 1.44°). The proposed system is simple to use and convenient for use in ambulatory or unsupervised environments for joint measurement; however, it was shown that accuracy can be sensitive to sensor placement. This system would be beneficial for obtaining quantitative patient data or tracking functional activity in variable environments, providing clinicians with indications of how patients' knees function during activity, potentially permitting more individualized care and recommendations.