Single Inertial Measurement Unit Research Articles

A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians.

Inertial sensors are widely used in various applications, such as human motion monitoring and pedestrian positioning. However, inertial sensors cannot accurately define the process of human movement, a limitation that causes data drift in the process of human body positioning, thus seriously affecting positioning accuracy and stability. The traditional pedestrian dead-reckoning algorithm, which is based on a single inertial measurement unit, can suppress the data drift, but fails to accurately calculate the number of walking steps and heading value, thus it cannot meet the application requirements. This study proposes an indoor dynamic positioning method with an error self-correcting function based on the symmetrical characteristics of human motion to obtain the definition basis of human motion process quickly and to solve the abovementioned problems. On the basis of this proposed method, an ultra-wide band (UWB) method is introduced. An unscented Kalman filter is applied to fuse inertial sensors and UWB data, inertial positioning is applied to compensation for the defects of susceptibility to UWB signal obstacles, and UWB positioning is used to overcome the error accumulation of inertial positioning. The above method can improve both the positioning accuracy and the response of the positioning results. Finally, this study designs an indoor positioning test system to test the static and dynamic performances of the proposed indoor positioning method. Results show that the positioning system both has high accuracy and good real-time performance.

Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation.

BackgroundBiofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability to objectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts of IMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring technique deviations may not be well detected. One method of combatting these issues is through the development of personalized exercise technique classifiers.ObjectiveWe aimed to create a tablet app for physiotherapists and personal trainers that would automate the development of personalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete a preliminary investigation of the accuracy of such individualized systems in a real-world evaluation.MethodsA tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizing video and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exercise professional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system, 15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD 9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated.ResultsThe tablet app successfully automated the process of creating individualized exercise biofeedback systems. The personalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificity for assessing aberrant and acceptable technique with a single IMU positioned on the left thigh.ConclusionsA tablet app was developed that automates the process required to create a personalized exercise technique classification system. This tool can be applied to any cyclical, repetitive exercise. The personalized classification model displayed excellent system accuracy even when assessing acute deviations in compound exercises with a single IMU.

The use of a single inertial sensor to estimate 3-dimensional ground reaction force during accelerative running tasks

The purpose of this investigation was to determine the feasibility of using a single inertial measurement unit (IMU) placed on the sacrum to estimate 3-dimensional ground reaction force (F) during linear acceleration and change of direction tasks. Force plate measurements of F and estimates from the proposed IMU method were collected while subjects (n=15) performed a standing sprint start (SS) and a 45° change of direction task (COD). Error in the IMU estimate of step-averaged component and resultant F was quantified by comparison to estimates from the force plate using Bland-Altman 95% limits of agreement (LOA), root mean square error (RMSE), Pearson’s product-moment correlation coefficient (r), and the effect size (ES) of the differences between the two systems. RMSE of the IMU estimate of step-average F ranged from 37.70 N to 77.05 N with ES between 0.04 and 0.47 for SS while for COD, RMSE was between 54.19 N to 182.92 N with ES between 0.08 and 1.69. Correlation coefficients between the IMU and force plate measurements were significant (p≤0.05) for all values (r=0.53 to 0.95) except the medio-lateral component of step-average F. The average angular error in the IMU estimate of the orientation of step-average F was ≤10° for all tasks. The results of this study suggest the proposed IMU method may be used to estimate sagittal plane components and magnitude of step-average F during a linear standing sprint start as well as the vertical component and magnitude of step-average F during a 45° change of direction task.

Technology in Strength and Conditioning: Assessing Bodyweight Squat Technique With Wearable Sensors.

O'Reilly, MA, Whelan, DF, Ward, TE, Delahunt, E, and Caulfield, BM. Technology in strength and conditioning: assessing bodyweight squat technique with wearable sensors. J Strength Cond Res 31(8): 2303-2312, 2017-Strength and conditioning (S&C) coaches offer expert guidance to help those they work with achieve their personal fitness goals. However, it is not always practical to operate under the direct supervision of an S&C coach and consequently individuals are often left training without expert oversight. Recent developments in inertial measurement units (IMUs) and mobile computing platforms have allowed for the possibility of unobtrusive motion tracking systems and the provision of real-time individualized feedback regarding exercise performance. These systems could enable S&C coaches to remotely monitor sessions and help individuals record their workout performance. One aspect of such technologies is the ability to assess exercise technique and detect common deviations from acceptable exercise form. In this study, we investigate this ability in the context of a bodyweight (BW) squat exercise. Inertial measurement units were positioned on the lumbar spine, thighs, and shanks of 77 healthy participants. Participants completed repetitions of BW squats with acceptable form and 5 common deviations from acceptable BW squatting technique. Descriptive features were extracted from the IMU signals for each BW squat repetition, and these were used to train a technique classifier. Acceptable or aberrant BW squat technique can be detected with 98% accuracy, 96% sensitivity, and 99% specificity when using features derived from all 5 IMUs. A single IMU system can also distinguish between acceptable and aberrant BW squat biomechanics with excellent accuracy, sensitivity, and specificity. Detecting exact deviations from acceptable BW squatting technique can be achieved with 80% accuracy using a 5 IMU system and 72% accuracy when using a single IMU positioned on the right shank. These results suggest that IMU-based systems can distinguish between acceptable and aberrant BW squat technique with excellent accuracy with a single IMU system. Identification of exact deviations is also possible but multi-IMU systems outperform single IMU systems.

A ROBUST AUTOMATIC GAIT MONITORING APPROACH USING A SINGLE IMU FOR HOME-BASED APPLICATIONS

A new approach of human activity monitoring with a single Inertial Measurement Unit (IMU) capable of gait recognition and assessment is proposed for home-based applications. The method estimates the foot motion using a single IMU, then automatically segments the motion into steps, and extracts multiple kinematics templates. It classifies each segment by extracting Mahalanobis distance-based features from multiple sections of the motion templates and then training a Support Vector Machine. The proposed wearable system can distinguish between nine classes of activities with a classification accuracy of 99.6%. It can also discriminate between normal and abnormal gait patterns with an accuracy of 98.7%. In addition to a high recognition rate, the proposed approach provides a Gait Similarity Score (GSS) of the performed gait to its desired/normal pattern. The experimental results indicate the capability of GSS measure for assessing the quality of motion in “pre-”, “initial”, “mid” and “terminal” stages of swing phase.

Assessment of lower arm movements using one inertial sensor.

Reduction of the number of sensors needed to evaluate arm movements, makes a system for the assessment of human body movements more suitable for clinical practice and daily life assessments. In this study, we propose an algorithm to reconstruct lower arm orientation, velocity and position, based on a sensing system which consists of only one inertial measurement unit (IMU) to the forearm. Lower arm movements were reconstructed using a single IMU and assuming that within a measurement there are moments without arm movements. The proposed algorithm, together with a single IMU attached to the forearm, may be used to evaluate lower arm movements during clinical assessments or functional tasks. In this pilot study, reconstructed quantities were compared with an optical reference system. The limits of agreement in the magnitude of the orientation vector and the norm of the velocity vectors are respectively 4.2 deg (normalized, 5.2 percent) and 7.1 cm/s (normalized, 5.8 percent). The limit of agreement of the difference between the reconstructed positions of both sensing systems were relatively greater 7.7 cm (normalized, 16.8 percent).

Technology in Strength and Conditioning Tracking Lower-Limb Exercises With Wearable Sensors.

Strength and conditioning (S&C) coaches offer expert guidance to help those they work with achieve their personal fitness goals. However, because of cost and availability issues, individuals are often left training without expert supervision. Recent developments in inertial measurement units (IMUs) and mobile computing platforms have allowed for the possibility of unobtrusive motion tracking systems and the provision of real-time individualized feedback regarding exercise performance. These systems could enable S&C coaches to remotely monitor sessions and help gym users record workouts. One component of these IMU systems is the ability to identify the exercises completed. In this study, IMUs were positioned on the lumbar spine, thighs, and shanks on 82 healthy participants. Participants completed 10 repetitions of the squat, lunge, single-leg squat, deadlift, and tuck jump with acceptable form. Descriptive features were extracted from the IMU signals for each repetition of each exercise, and these were used to train an exercise classifier. The exercises were detected with 99% accuracy when using signals from all 5 IMUs, 99% when using signals from the thigh and lumbar IMUs and 98% with just a single IMU on the shank. These results indicate that a single IMU can accurately distinguish between 5 common multijoint exercises.

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 51.2Hz, 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.

Estimation, Control, and Planning for Aggressive Flight With a Small Quadrotor With a Single Camera and IMU

We address the state estimation, control, and planning for aggressive flight with a 150 cm diameter, 250 g quadrotor equipped only with a single camera and an inertial measurement unit (IMU). The use of smartphone grade hardware and the small scale provides an inexpensive and practical solution for autonomous flight in indoor environments. The key contributions of this paper are: 1) robust state estimation and control using only a monocular camera and an IMU at speeds of 4.5 m/s, accelerations of over 1.5 g, roll and pitch angles of up to 90 $^\circ$ , and angular rate of up to 800 $^\circ$ /s without requiring any structure in the environment; 2) planning of dynamically feasible three-dimensional trajectories for slalom paths and flights through narrow windows; and 3) extensive experimental results showing aggressive flights through and around obstacles with large rotation angular excursions and accelerations.

Attitude Estimation Using Redundant Inertial Measurement Units for the Control of a Camera Stabilization Platform

In this brief, a three-axis gimbaled platform for the active stabilization of film and broadcast cameras is considered. To decouple the attitude of the camera from the motion of the operator, accurate estimation of the platform attitude is necessary. Attitude estimation strategies based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) are developed, which fuse redundant measurements of inertial measurement units (IMUs) mounted at different positions of the platform. This brief extends the state of the art, where typically a single IMU or redundant IMUs placed at the same position are used. The performance of the EKF and UKF is analyzed and compared with an EKF using only a single IMU by measurement results of a prototype platform.

How realistic is a racehorse simulator?

Race jockey training is demanding and technical. Increased horse care costs and demands on time have led to greater availability and use of racehorse simulators during training. Little is known about the accuracy of the simulated movement and therefore how effective they are for developing the desired technique. We quantified and compared sacral rotation and displacement vectors for a racehorse simulator and a real galloping horse. A single inertial measurement unit was placed on the sacrum of six horses (horse) during a training gallop along an all-weather seven furlong gallop and on the highest speed setting ‘four’ on the simulator. Displacements were calculated in all three axes before being cut into cycles and analysed along with roll and pitch. Displacement and rotation amplitudes were extracted and compared for the horse and simulator. Horse sacral movement parameters were more varied than those recorded on the simulator. The real horse exhibited greater dorso-ventral, medio-lateral and roll amplitude but smaller cranio-caudal displacement amplitude and no difference in pitch amplitude. Displacement trajectory of the simulator when viewed laterally from the left side, was anticlockwise, the opposite direction to that of the real horse leaving the regular use of a simulator during jockey training under question. Use of the racehorse simulator is beneficial to develop specific fitness and to enable physical manipulation into the optimal position. Care must be taken to avoid any detrimental effects of training with the opposite movement trajectory to that experienced during a race. The programming of the simulators may benefit from adaptations to maximise their benefits.

Movements Indicate Threat Response Phases in Children at Risk for Anxiety.

Temporal phases of threat response, including potential threat (anxiety), acute threat (startle, fear), and post-threat response modulation, have been identified as the underlying markers of anxiety disorders. Objective measures of response during these phases may help identify children at risk for anxiety; however, the complexity of current assessment techniques prevent their adoption in many research and clinical contexts. We propose an alternative technology, an inertial measurement unit (IMU), that enables noninvasive measurement of the movements associated with threat response, and test its ability to detect threat response phases in young children at a heightened risk for developing anxiety. We quantified the motion of 18 children (3-7 years old) during an anxiety-/fear-provoking behavioral task using an IMU. Specifically, measurements from a single IMU secured to the child's waist were used to extract root-mean-square acceleration and angular velocity in the horizontal and vertical directions, and tilt and yaw range of motion during each threat response phase. IMU measurements detected expected differences in child motion by threat phase. Additionally, potential threat motion was positively correlated to familial anxiety risk, startle range of motion was positively correlated with child internalizing symptoms, and response modulation motion was negatively correlated to familial anxiety risk. Results suggest differential theory-driven threat response phases and support previous literature connecting maternal child risk to anxiety with behavioral measures using more feasible objective methods. This is the first study demonstrating the utility of an IMU for characterizing the motion of young children to mark the phases of threat response modulation. The technique provides a novel and objective measure of threat response for mental health researchers.

Model Based Determination of Gait Parameters Using IMU Sensor Data

The paper deals with the determination of gait parameters using inertial measurement units (IMU). An IMU sensor incorporates three microelectromechanical sensors - triple-axis gyroscope, accelerometer and magnetometer. A standard experimental setup for the observation of the locomotion system using seven Xsens MTw sensors was developed. They are applied to the lower limbs and the pelvis of the subject. The synchronization of data from all sensor components (gyroscope, accelerometer and magnetometer) as well as the onboard estimation of the orientation is provided by the Xsens and Adwinda hard-and software. The strapped down data are received with a rate of 60 Hz. The output data of a single IMU sensor allow motion analysis of the sensor unit itself as well as the motion of the limb where the sensor is mounted to. Stable and reliable algorithms processing the gait data and calculating gait features of a single sensor are developed and evaluated. These algorithms are based on precise determination of each gait cycle. In the middle of stance phase the foot is not moving. It stands on the floor and, following, the initial conditions for the calculation of foot velocities and distances by integration are predetermined. Various features of the gait cycle as well as e.g. dependencies in between features or on the gait velocities are investigated. The application of seven sensors to the limbs of the locomotion system provides measurements of their 3D motion observed in an inertial coordinate system. The limbs are parts of skeleton and interconnected by joints. Introducing a skeleton model, the quality of measurements is evaluated and improved. Joint angles, symmetry ratios and other gait parameters are determined. These results can be used for analysis of the gait of any subject as well as of any cohort.

Monitoring of Hip and Knee Joint Angles Using a Single Inertial Measurement Unit During Lower Limb Rehabilitation

This paper investigated the possibility of estimating 3D lower limb joint kinematics during five popular rehabilitation exercises of the hip and knee joints based on the data collected from a single inertial measurement unit located on the shank. The leg was modeled as a four-degree-of-freedom serial chain, and the relevant joint angles were represented by Fourier series. A least square approach based on the minimization of the difference between the measured and estimated 3D linear accelerations and angular velocities was used to solve the related analytical problem. The approach was validated on ten healthy young volunteers (ten trials each), comparing the proposed approach with the measurements collected through a stereophotogrammetric system. The average root mean square differences between the estimated joint angles and those reconstructed with the stereophotogrammetric system were inferior than 3.2° with correlation coefficients higher than 0.85.

Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data.

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject’s foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than under the various walking conditions.

Validity of an inertial measurement unit to assess pelvic orientation angles during gait, sit–stand transfers and step-up transfers: Comparison with an optoelectronic motion capture system*

An inertial measurement unit (IMU) allows kinematic evaluation of human motion with fewer operational constraints than a gold standard optoelectronic motion capture (MOCAP) system. The study's aim was to compare IMU and MOCAP measurements of dynamic pelvic orientation angles during different activities of daily life (ADL): gait, sit-to-stand (STS) transfers and block step-up (BS) transfers. A single IMU was attached onto the lower back in seventeen healthy participants (8F/9 M, age 19–31 years; BMI < 25) and optical skin markers were attached onto anatomical pelvic landmarks for MOCAP measurements. Comparisons between IMU and MOCAP by Bland–Altman plots demonstrated that measurements were between 2SD of the absolute difference and Pearson's correlation coefficients were between 0.85 and 0.94. Frontal plane pelvic angle estimations achieved a RMSE in the range of [2.7°–4.5°] and sagittal plane measurements achieved a RMSE in the range of [2.7°–8.9°] which were both lowest in gait. Waveform peak detection times demonstrated ICCs between 0.96 and 1.00. These results are in accordance to other studies comparing IMU and MOCAP measurements with different applications and suggest that an IMU is a valid tool to measure dynamic pelvic angles during various activities of daily life which could be applied to monitor rehabilitation in a wide variety of musculoskeletal disorders.

Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson's disease and Huntington's disease subjects

The estimation of gait temporal parameters with inertial measurement units (IMU) is a research topic of interest in clinical gait analysis. Several methods, based on the use of a single IMU mounted at waist level, have been proposed for the estimate of these parameters showing satisfactory performance when applied to the gait of healthy subjects. However, the above mentioned methods were developed and validated on healthy subjects and their applicability in pathological gait conditions was not systematically explored. We tested the three best performing methods found in a previous comparative study on data acquired from 10 older adults, 10 hemiparetic, 10 Parkinson's disease and 10 Huntington's disease subjects. An instrumented gait mat was used as gold standard. When pathological populations were analyzed, missed or extra events were found for all methods and a global decrease of their performance was observed to different extents depending on the specific group analyzed. The results revealed that none of the tested methods outperformed the others in terms of accuracy of the gait parameters determination for all the populations except the Parkinson's disease subjects group for which one of the methods performed better than others. The hemiparetic subjects group was the most critical group to analyze (stride duration errors between 4-5 % and step duration errors between 8-13 % of the actual values across methods). Only one method provides estimates of the stance and swing durations which however should be interpreted with caution in pathological populations (stance duration errors between 6-14 %, swing duration errors between 10-32 % of the actual values across populations).

Hybrid Low-Cost Approach for Quadrotor Attitude Estimation

This paper presents a new approach to estimate the orientation of a quadrotor using single low-cost inertial measurement unit (IMU) sensor. The proposed hybrid solution uses two extended Kalman filters (EKF) along with a direction cosine matrix (DCM) algorithm. An EKF utilizes the dynamics of the quadrotor to filter the noise on the body-frame (B-frame) angular rates measured by the three-axis gyroscope sensor. Then, a DCM algorithm uses the filtered gyro signal along with the reading from a three-axis accelerometer and a three-axis magnetometer sensor to estimate the Euler angles. Finally, an additional EKF is presented to enhance the final estimates of the Euler angles. The performance of the proposed hybrid approach is tested and compared with other commonly used methods. Results are presented at the end of the paper to verify the performance of the proposed method. The results show an improvement in the estimated quadrotor's state. Monte Carlo tests are performed to ensure sustained high accuracy estimation.

Ambulatory assessment of shoulder abduction strength curve using a single wearable inertial sensor.

The aim of the present article was to assess the reliability of strength curves as determined from tridimensional linear accelerations and angular velocities measured by a single inertial measurement unit (IMU) fixed on the upper arm during a shoulder abduction movement performed holding a 1 kg dumbbell in the hand. Within-subject repeatability of the task was assessed on 45 subjects performing four trials consisting of one maximal shoulder abduction-adduction movement. Intraclass correlation coefficient (ICC) was computed on the average movement angular velocity (VEL) and range of movement (ROM) across the four trials. Within-subject repeatability of torque curves was assessed in terms of waveform similarities by computing the coefficient of multiple determination (CMD). Accuracy of the estimated ROM was assessed using an isokinetic dynamometer. High ICC values of ROM (0.955) and VEL (0.970) indicated a high within-subject repeatability of the task. A high waveform similarity of torque curves was also found between trials (CMD = 0.867). Accuracy with respect to isokinetic dynamometer in estimating ROM was always <1 degree (p = 0.37). This study showed the effectiveness of using a single wearable IMU for the assessment of strength curve during isoinertial movements in a way that complies with the needs of clinicians in an ambulatory setting.

Foot-mounted inertial measurement unit for activity classification.

This paper proposes a classification technique for daily base activity recognition for human monitoring during physical therapy in home. The proposed method estimates the foot motion using single inertial measurement unit, then segments the motion into steps classify them by template-matching as walking, stairs up or stairs down steps. The results show a high accuracy of activity recognition. Unlike previous works which are limited to activity recognition, the proposed approach is more qualitative by providing similarity index of any activity to its desired template which can be used to assess subjects improvement.