An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors

Inertial measurement units (IMUs) have been demonstrated to reliably measure human joint angles—an essential quantity in the study of biomechanics. However, most previous literature proposed IMU-based joint angle measurement systems that required manual alignment or prescribed calibration motions. This paper presents a simple, physically-intuitive method for IMU-based measurement of the knee flexion/extension angle in gait without requiring alignment or discrete calibration, based on computationally-efficient and easy-to-implement Principle Component Analysis (PCA). The method is compared against an optical motion capture knee flexion/extension angle modeled through OpenSim. The method is evaluated using both measured and simulated IMU data in an observational study (n = 15) with an absolute root-mean-square-error (RMSE) of 9.24∘ and a zero-mean RMSE of 3.49∘. Variation in error across subjects was found, made emergent by the larger subject population than previous literature considers. Finally, the paper presents an explanatory model of RMSE on IMU mounting location. The observational data suggest that RMSE of the method is a function of thigh IMU perturbation and axis estimation quality. However, the effect size for these parameters is small in comparison to potential gains from improved IMU orientation estimations. Results also highlight the need to set relevant datums from which to interpret joint angles for both truth references and estimated data.National Science Foundation (U.S.) (GRFP)National Science Foundation (U.S.) (IIS-1453141

Development of a mobile technology system to measure shoulder range of motion

In patients with shoulder movement impairment, assessing and monitoring shoulder range of motion is important for determining the severity of impairments due to disease or injury and evaluating the effects of interventions. Current clinical methods of goniometry and visual estimation require an experienced user and suffer from low inter-rater reliability. More sophisticated techniques such as optical or electromagnetic motion capture exist but are expensive and restricted to a specialised laboratory environment.;Inertial measurement units (IMU), such as those within smartphones and smartwatches, show promise as tools bridge the gap between laboratory and clinical techniques and accurately measure shoulder range of motion during both clinic assessments and in daily life.;This study aims to develop an Android mobile application for both a smartphone and a smartwatch to assess shoulder range of motion. Initial performance characterisation of the inertial sensing capabilities of both a smartwatch and smartphone running the application was conducted against an industrial inclinometer, free-swinging pendulum and custom-built servo-powered gimbal.;An initial validation study comparing the smartwatch application with a universal goniometer for shoulder ROM assessment was conducted with twenty healthy participants. An impaired condition was simulated by applying kinesiology tape across the participants shoulder girdle. Agreement, intra and inter-day reliability were assessed in both the healthy and impaired states.;Both the phone and watch performed with acceptable accuracy and repeatability during static (within ±1.1°) and dynamic conditions where it was strongly correlated to the pendulum and gimbal data (ICC > 0.9). Both devices could perform accurately within optimal responsiveness range of angular velocities compliant with humerus movement during activities of daily living (frequency response of 377°/s and 358°/s for the phone and watch respectively).;The concurrent agreement between the watch and the goniometer was high in both healthy and impaired states (ICC > 0.8) and between measurement days (ICC > 0.8). The mean absolute difference between the watch and the goniometer were within the accepted minimal clinically important difference for shoulder movement (5.11° to 10.58°).;The results show promise for the use of the developed Android application to be used as a goniometry tool for assessment of shoulder ROM. However, the limits of agreement across all the tests fell out with the acceptable margin and further investigation is required to determine validity. Evaluation of validity in clinical impairment patients is also required to assess the feasibility of the use of the application in clinical practice.In patients with shoulder movement impairment, assessing and monitoring shoulder range of motion is important for determining the severity of impairments due to disease or injury and evaluating the effects of interventions. Current clinical methods of goniometry and visual estimation require an experienced user and suffer from low inter-rater reliability. More sophisticated techniques such as optical or electromagnetic motion capture exist but are expensive and restricted to a specialised laboratory environment.;Inertial measurement units (IMU), such as those within smartphones and smartwatches, show promise as tools bridge the gap between laboratory and clinical techniques and accurately measure shoulder range of motion during both clinic assessments and in daily life.;This study aims to develop an Android mobile application for both a smartphone and a smartwatch to assess shoulder range of motion. Initial performance characterisation of the inertial sensing capabilities of both a smartwatch and smartphone running the application was conducted against an industrial inclinometer, free-swinging pendulum and custom-built servo-powered gimbal.;An initial validation study comparing the smartwatch application with a universal goniometer for shoulder ROM assessment was conducted with twenty healthy participants. An impaired condition was simulated by applying kinesiology tape across the participants shoulder girdle. Agreement, intra and inter-day reliability were assessed in both the healthy and impaired states.;Both the phone and watch performed with acceptable accuracy and repeatability during static (within ±1.1°) and dynamic conditions where it was strongly correlated to the pendulum and gimbal data (ICC > 0.9). Both devices could perform accurately within optimal responsiveness range of angular velocities compliant with humerus movement during activities of daily living (frequency response of 377°/s and 358°/s for the phone and watch respectively).;The concurrent agreement between the watch and the goniometer was high in both healthy and impaired states (ICC > 0.8) and between measurement days (ICC > 0.8). The mean absolute difference between the watch and the goniometer were within the accepted minimal clinically important difference for shoulder movement (5.11° to 10.58°).;The results show promise for the use of the developed Android application to be used as a goniometry tool for assessment of shoulder ROM. However, the limits of agreement across all the tests fell out with the acceptable margin and further investigation is required to determine validity. Evaluation of validity in clinical impairment patients is also required to assess the feasibility of the use of the application in clinical practice

Inertial sensors signal processing methods for gait analysis of patients with impaired gait patterns

Analiza hoda je postala široko rasprostranjen klinički alat koji se koristi za objektivnu evaluaciju obrasca hoda, efekata hirurških intervencija, oporavka ili efekata terapije. Sve veći broj kliničara bira pogodne tretmane za lečenje pacijenata na osnovu informacija o kinematici i kinetici hoda. Procena i kvantifikacija parametara hoda je važan zahtev u oblasti ortopedije i rehabilitacije, ali takođe i u sportu, rekreaciji i posebno u razvoju tehnologija za ljude u procesu starenja. U cilju objektivne procene obrasca hoda, razvijen je bežični senzorski sistem čije su senzorske jedinice bežične, malih dimenzija i jednostavno se montiraju na segmente nogu subjekta čiji se hoda analizira. Senzorske jedinice podržavaju 3D inercijalne senzore (senzore ubrzanja i ugaonih brzina, tj. akcelerometre i žiroskope), kao i senzore sile. Osnovni cilj istraživanja je doprinos metodologiji za obradu podataka sa inercijalnih senzora i razvoj novih metoda obrade signala sa inercijalnih senzora u procesu određivanja kinematičkih veličina koje su uobičajene u analizi hoda (uglovi u zglobovima, brzina kretanja, dužina koraka). Ova metodologija je od posebne važnosti za objektivnu procenu nivoa motornog deficita, progresa bolesti i efikasnosti terapija, kao i efikasnosti primenjene motorne kontrole (prilikom funkcionalne električne stimulacije). U toku istraživanja razvijeno je nekoliko metoda za računanje uglova segmenata nogu ili zglobova, u zavisnosti od senzorske konfiguracije i složenosti algoritma. U disertaciji su odvojeno prikazani slučajevi u kojima je neophodno posmatrati kretanje u prostoru (3D analiza) i mnogo češći slučaj kad se kinematika može redukovati na sagitalnu ravan (2D analiza). Algoritmi uključuju i kalibraciju senzora, eliminaciju viii drifta, rekonstrukciju trajektorije i izračunavanje niza drugih relevantnih podataka koji karakterišu obrazac hoda. Dobijeni rezultati su poređeni sa postojećim sistemima za analizu hoda koji su validirani za kliničke primene. (sistemi sa kamerama, goniometri, enkoderi)...Gait analysis has become a widely used clinical tool which provides objective evaluation of the gait pattern, the effects of surgical interventions, recovery or therapy progress, and more and more clinicians are choosing therapy treatments based on gait kinematics and kinetics. Measuring gait parameters is an important requirement in the orthopedic and rehabilitation fields, but also in sports and fitness, and development of technologies for elderly population. In order to provide objective evaluation of the gait pattern, we have developed sensor system with light and small wireless sensor units, which can be easily mounted on body. These sensor units comprise 3-D inertial sensors (accelerometers and gyroscopes) and force sensing resistors, and our recommended setup includes one sensor unit per each segment of both legs. The main goal of this research is contribution to the methodology for processing of signals from inertial sensors (accelerometer pairs, or accelerometer and gyroscope sensor units). By using signal processing algorithms developed for this research, inertial sensors allow objective assessment of the quality of the gait pattern. This methodology is especially important for assessment of the motor deficit, progress of the disease and therapy effectiveness, and effectiveness of performed motor control (functional electrical stimulation). We have developed several methods for estimation of leg segment angles and joint angles, which differ in sensor configuration and algorithm complexity. Methods based only on accelerometers offer reliable angle estimations, which are limited to sagittal plane analysis, while the method using accelerometers and gyroscopes allows 3- D analysis. All this algorithms include sensor calibration, drift minimization, trajectory x reconstruction and calculation of numerous other parameters relevant to gait pattern analysis. The obtained results were compared with other commercial systems which are validated for clinical applications (camera systems, goniometers, encoders)..

Multi-Sensor Inertial Measurement System for Analysis of Sports Motion

A portable motion analysis system that can accurately measure body movement kinematics and kinetics has the potential to benefit athletes and coaches in performance improvement and injury prevention. In addition, such a system can allow researchers to collect data without limitations of time and location. In this dissertation, a portable multi-sensor human motion analysis algorithm is been developed based on inertial measurement technology. The algorithm includes a newly designed coordinate flow chart analysis method to systematically construct rotation matrices for multi-Inertial Measurement Unit (IMU) application. Using this system, overhead throwing is investigated to reconstruct arm trajectory, arm rotation velocities, as well as torque and force imposed on the elbow and shoulder. Based on this information, different motion features can be established, such as kinematic chain timing as demonstrated in this work. Human subject experiments are used to validate the functionality of the method and the accuracy of the kinematics reconstruction results. Single axis rotation rig experiments are used to shown that this multi-IMU system and algorithm provides an improved in accuracy on arm rotation calculation over the conventional video camera based motion capture system. Finally, a digital filter with switchable cut-off frequency is developed and demonstrated in its application to the IMU-based sports motion signals. The switchable filter method is not limited only to IMUs, but may be applied to any type of motion sensing technology. With the techniques developed in this work, it will be possible in the near future to use portable and accurate sports motion analysis systems in training, rehabilitation and scientific research on sports biomechanics

Validation of an Accelerometry Based Method of Human Gait Analysis

Gait analysis is the quantification of locomotion. Understanding the science behind the way we move is of interest to a wide variety of fields. Medical professionals might use gait analysis to track the rehabilitation progress of a patient. An engineer may want to design wearable robotics to augment a human operator. Use cases even extend into the sport and entertainment industries. Typically, a gait analysis is performed in a highly specialized laboratory containing cumbersome expensive equipment. The process is tedious and requires specially trained operators. Continued development of small and cheap inertial measurement units (IMUs) over an alternative to current methods of gait analysis. These devices are portable and simple to use allowing gait analysis to be done outside the laboratory in real world environments. Unfortunately, while current IMU based gait analysis systems are able to quantify a subject\u27s joint kinematics they are unable to measure joint kinetics as could be done in a traditional gait laboratory. A novel musculoskeletal model-based movement analysis system using accelerometers has been developed that can calculate both joint kinematics and joint kinetics. The aim of this master\u27s thesis is to validate this accelerometer based gait analysis against the industry standard optical motion capture gait analysi

Evaluation of Angular Velocity Data from Inertial Measurement Units for Use in Clinical Settings

Evaluating the human gait cycle with inertial measurement units (IMU) may prove beneficial for applications such as diagnoses of musculoskeletal diseases and assessment of rehabilitation regimes. An IMU system is potentially applicable for diagnosing and assessing rehabilitation outcomes for a variety of neuromuscular diseases since it is small, portable, and less expensive than a camera system. IMUs directly measure angular velocity, whereas position data from a camera system must be processed twice to obtain this information. The purpose of this research is to determine repeatability of IMU angular velocity data, and agreement between angular velocity data from an IMU system and a camera system during normal gait. From this data, the feasibility of using IMU systems in clinical or rehabilitative settings for obtaining reliable angular velocity data will be determined. Lower limb motion data was collected simultaneously from six XSens MTx IMUs (XSens Technologies, Enschede, The Netherlands) and an 8-camera Qualisys Motion Capture system (Pro-Reflex, 240 Hz system). Each IMU consists of three orthogonal accelerometers, gyroscopes, and magnetometers. Data from 4 subjects (3 males, 2 females) were collected after an initialization technique before each trial to reduce effects of electro-magnetic interference with the IMUs. Knee joint angular velocities (Gx, Gy, Gz) corresponding to appropriate knee joint angles (flexion/extension, adduction/abduction, and internal/external rotations) from both systems were used in this analysis. Coefficients of variation (COV) were calculated for both IMU and camera data to determine variability of data from both systems. Knee joint Average angular velocities from both systems for each subject and limb were plotted together to visually evaluate correlation between data sets. F-test analyses were performed on linear models of the data to determine areas of co-linearity within the gait cycle, and at different intervals of angular velocities. The IMUs had lower COV\u27s than the camera system, likely due to the fact that the IMUs directly measure angular velocity, and camera system derives angular velocity from position data. However, these differences were not statistically different, likely due to variability within trials for individual subjects. Linearity between camera system and IMU angular velocity was visually observed only about the flexion/extension axis during segments of the gait cycle occurring from 0-4% (heel strike) and 65-100% (swing phase) of the gait cycle. Comparisons about the adduction/abduction and internal/external axes showed evidence of linearity for lower angular velocities. Linear regression statistics showed that the only correlational trend between the two systems was around 8-12% of the gait cycle for all three rotational axes. This may be due to drift of the IMU data. Although the camera system is the \u27gold standard\u27 in motion analysis, IMUs may be used for applications in which angular velocity for a flexion-extension movement at low joint angles is being evaluated. Future studies will include a larger sample population, and evaluate specific movements within human gait that affect drift of the IMUs. In addition, other IMU system designs could be evaluated for clinical use, and other algorithms that further reduce the effects of drift should be implemented

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error)