Inertial Measurement Unit Sensor Research Articles

Humanoid Robot Based Platform to Evaluate the Efficacy of Using Inertial Sensors for Spasticity Assessment in Cerebral Palsy.

Spasticity is commonly present in individuals with cerebral palsy (CP) and manifests itself as shaky movements, muscle tightness and joint stiffness. Accurate and objective measurement of spasticity is investigated using inertial measurement unit (IMU) sensors. However, use of current IMU-based devices is limited to clinics in urban areas where experienced and trained health professionals are available to collect spasticity data. Designing these devices based on the wearable internet of things based architectures with edge computing will expand their use to home, aged care or remote clinics enabling less-experienced health professionals or care givers to collect spasticity data. However, these new designs require rigorous testing during their prototyping stage and collection of supporting data for regulatory approvals. This work demonstrates that a humanoid robot can act as an accurate model of the movements of CP individuals performing pendulum test during their spasticity assessment. Utilizing this model, we present a robust platform to evaluate new designs of IMU-based spasticity measurement devices.

Human Motion Tracking Using 3D Image Features with a Long Short-Term Memory Mechanism Model-An Example of Forward Reaching.

Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to the body, which can be inconvenient or uncomfortable for users. To alleviate this issue, a visual-based tracking system from two-dimensional (2D) RGB images has been studied extensively in recent years and proven to have a suitable performance for human motion tracking. However, the 2D image system has its limitations. Specifically, human motion consists of spatial changes, and the 3D motion features predicted from the 2D images have limitations. In this study, we propose a deep learning (DL) human motion tracking technology using 3D image features with a deep bidirectional long short-term memory (DBLSTM) mechanism model. The experimental results show that, compared with the traditional 2D image system, the proposed system provides improved human motion tracking ability with RMSE in acceleration less than 0.5 (m/s2) X, Y, and Z directions. These findings suggest that the proposed model is a viable approach for future human motion tracking applications.

The Distinction of Anomalous Kicks in Taekwondo Using IMU Sensors

The present study intends to find a way to distinguish an anomalous kick that appeared as a side effect of the introduction of the electronic Protector and Scoring System (PSS) in taekwondo through Inertial Measurement Unit (IMU) sensors. The 10 normal kicks and 1 anomalous kick were selected for the study. Four IMU sensors were attached to the trunk, pelvis, and both feet of each of 10 taekwondo athletes to obtain the total acceleration and total angular velocity of the right foot based on the trunk and the pelvis. Seven athletes were randomly selected to calculate the reference data of 11 types of kicks and the data were compared with the data of 11 kicks of the remaining three athletes through correlation analysis. The accuracy of distinguishing the anomalous kick through each variable was 83.3%, and the probability of misunderstanding a normal kick as an anomalous kick was 6.7%. When the results of the correlation analysis of the four variables were synthesized and analyzed, the accuracy of distinguishing anomalous kicks improved to 100%, and the ratio of cases of misunderstanding normal kicks as anomalous kicks decreased to 0%. The results of the present study provide a simple but reliable method to distinguish anomalous kicks in taekwondo competitions. If the limitations of the findings of the study are settled and introduced into the taekwondo EPSS, the problem of anomalous kicks in current taekwondo competitions can be solved.

Attitude Determination for GRACE-FO: Reprocessing the Level-1A SC and IMU Data

The satellite gravity mission GRACE(-FO) has not yet reached its designed baseline accuracy. Previous studies demonstrated that the deficiency in the sensor system or the related signal processing might be responsible, which in turn motivates us to keep revising the sensor data processing, typically the spacecraft’s attitude. Many efforts in the past have been made to enhance the attitude modeling for GRACE, for instance, the latest release reprocesses the attitude by fusing the angular acceleration with the star camera/tracker (SC) measurements, which helps to reduce the error in Level-2 temporal gravity fields. Therefore, in addition to GRACE, revising GRACE-FO attitude determination might make sense as well. This study starts with the most original raw GRACE-FO Level-1A data including those from three SCs and one IMU (Inertial Measurement Unit) sensors, and manage to generate a new publicly available Level-1B attitude product called HUGG-01 covering from June 2018 to December 2020, using our independently-developed software. The detailed treatment of individual payload is present in this study, and an indirect Kalman filter method is introduced to fuse the multiple sensors to acquire a relatively stable and precise attitude estimation. Unlike the direct SC combination method with a predefined weight as recommended in previous work, we propose an involvement of each SC measurement in the Kalman filter to enable a dynamic weight adjustment. Intensive experiments are further carried out to assess the HUGG-01, which demonstrate that the error level of HUGG-01 is entirely within the design requirement, i.e., the resulting KBR pointing variations are well controlled within 1 mrad (pitch), 5 mrad (roll) and 1 mrad (yaw). Moreover, comparisons with the official JPL-V04 attitude product demonstrate an equivalent performance in the low-to-middle spectrum, with even a slightly lower noise level (in the high spectrum) than JPL-V04. Further analysis on KBR range-rate residuals and gravity recovery on January 2019 indicates that, i.e., RMS of the difference (HUGG-01 minus JPL-V04) for the range rate is less than 3.234×10−8 m/s, and the amplitude of geoid height difference is approximately 0.5 cm. Both differences are below the sensitivity of the state-of-the-art satellite gravity mission, demonstrating a good agreement between HUGG-01 and JPL-V04.

Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network.

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination () is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and among the hip, knee, and ankle joints.

Measuring Upper Limb Kinematics of Forehand and Backhand Topspin Drives with IMU Sensors in Wheelchair and Able-Bodied Table Tennis Players.

To better understand the biomechanics of para-table tennis players, this study compared the shoulder, elbow, and wrist joint kinematics among able-bodied (AB) and wheelchair players in different classifications. Nineteen participants (AB, n = 9; classification 1 (C1), n = 3; C2, n = 3; C3, n = 4) executed 10 forehand and backhand topspin drives. Shoulder abduction/adduction, elbow flexion/extension, wrist extension/flexion, respective range of motion (ROM), and joint patterns were obtained using inertial measurement unit (IMU) sensors. The results showed clear differences in upper limb kinematics between the able-bodied and wheelchair players, especially in the elbow and wrist. For the para-players, noticeable variations in techniques were also observed among the different disability classes. In conclusion, wheelchair players likely adopted distinct movement strategies compared to AB to compensate for their physical impairments and functional limitations. Hence, traditional table tennis programs targeting skills and techniques for able-bodied players are unsuitable for para-players. Future work can investigate how best to customize training programs and to optimize movement strategies for para-players with varied types and degrees of impairment.

Automatic mapping of traffic signs

Abstract. Nowadays, people easily can get into their cars and drive hundreds of kilometers in a few hours, but for that to work efficiently a system of rules must be applied and those rules have to be communicated transparently. This is why traffic signs are an influential part of our lives and every kind of information about each is helping the government, the community, and the drivers. This paper presents a novel and cost-efficient method for acquiring information on traffic signs, such like the category and the 3D position. The former can be gained using camera images and a Convolutional Neural Network model. The latter can be obtained using positioning devices.With the help of a GNSS device the absolute position of the vehicle can be learned and based on that a local coordinate system can be established. From the vehicle’s point of view the coordinates and the orientation of the traffic sign can be acquired by applying a stereo camera and an IMU (Inertial Measurement Unit) sensor. Then, with the help of these attributes a large database can be built, maintained, and updated. This project displays that adequately precise data can easily be accessible using a few cheap devices and sensors.

Assessment of postural sway with a pendant-mounted wearable sensor

BackgroundBody-worn inertial measurement unit (IMU) sensors have been widely used in postural stability and balance studies because of their low cost and convenience. In most of these studies, a single IMU sensor is attached to a waist belt near the body’s center of mass. Some populations such as pregnant women, however, may find a waist belt challenging in terms of fit and comfort. For this reason it may be useful to identify an alternative location for placement of an IMU and a more comfortable means for attaching the sensor to the body.Research questionDoes placing an IMU sensor in a pendant worn around the neck permit discrimination between conditions with varying postural stability? MethodsTwenty-six healthy participants performed three standing tasks (double-leg, tandem, and single-leg standing) under eyes-open and eyes-closed vision conditions to preliminarily assess the ability of the pendant sensor to discriminate between balance conditions. Discrimination based upon data from a belt-mounted IMU was assessed in the same trials. Differences in standard deviation of acceleration components, sway area, and jerkiness due to trial condition and sensor were evaluated using analysis of variance followed by post hoc comparisons. These data were also incorporated into receiver-operator characteristic (ROC) curve analysis to assess the effectiveness of each sensor at discriminating between conditions. ResultsStability was found to vary across conditions, but there was no interaction between stability and sensor location (all p ≥ 0.323). ROC curve analysis showed that sensors in both locations were good discriminators between conditions.SignificancePlacing an IMU in a pendant may be feasible for studying and monitoring postural instability. This approach may be especially valuable when considering populations for which wearing a belt is uncomfortable.

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation.

Recently, technology utilizing ultra-wideband (UWB) sensors for robot localization in an indoor environment where the global navigation satellite system (GNSS) cannot be used has begun to be actively studied. UWB-based positioning has the advantage of being able to work even in an environment lacking feature points, which is a limitation of positioning using existing vision- or LiDAR-based sensing. However, UWB-based positioning requires the pre-installation of UWB anchors and the precise location of coordinates. In addition, when using a sensor that measures only the one-dimensional distance between the UWB anchor and the tag, there is a limitation whereby the position of the robot is solved but the orientation cannot be acquired. To overcome this, a framework based on an interacting multiple model (IMM) filter that tightly integrates an inertial measurement unit (IMU) sensor and a UWB sensor is proposed in this paper. However, UWB-based distance measurement introduces large errors in multipath environments with obstacles or walls between the anchor and the tag, which degrades positioning performance. Therefore, we propose a non-line-of-sight (NLOS) robust UWB ranging model to improve the pose estimation performance. Finally, the localization performance of the proposed framework is verified through experiments in real indoor environments.

Monitoring toppling failures using wireless inertial measurement unit sensors

A topple is a common rock slope failure – and usually the cause of a rockfall – that depends on the slope and rock geometries. This paper presents a laboratory simulation and field generation of a single-column rock topple failure. Innovative inertial measurement unit (IMU) sensors were used to collect three-dimensional acceleration and angular velocity data during four laboratory experiments to understand and characterise general toppling behaviours. A topple is identified by the gradual change in at least two gravity accelerations, the continuous increase in at least one of the angular velocities and a significant change in the linear acceleration in the direction of toppling. A field experiment showed similar sensor data patterns to those observed from the laboratory experiments. An isolated-block stability analysis of a single-column topple provides the basis for incorporating the IMU sensors into the development of future monitoring and early warning systems.

An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments.

The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.

The Feasibility of Information-Entropy-Based Behavioral Analysis for Detecting Environmental Barriers.

The enhancement of physical activity is highly correlated with the conditions of the built environment. Walking is considered to be a fundamental daily physical activity, which requires an appropriate environment. Therefore, the barriers of the built environment should be identified and addressed. Barriers can act as external stimuli for pedestrians, so pedestrians may diversely respond to them. Based on this consideration, this study examines the feasibility of information-entropy-based behavioral analysis for the detection of environmental barriers. The physical responses of pedestrians were collected using an inertial measurement unit (IMU) sensor in a smartphone. After the acquired data were converted to behavioral probability distributions, the information entropy of each grid cell was calculated. The grid cells whereby the participants indicated that environmental barriers were present yielded relatively high information entropy values. The findings of this study will facilitate the design of more pedestrian-friendly environments and the development of diverse approaches that utilize citizens for monitoring the built environment.

Real-Time Limb Motion Tracking with a Single IMU Sensor for Physical Therapy Exercises.

Limb exercises are common in physical therapy to improve range of motion (RoM), strength, and flexibility of the arm/leg. To improve therapy outcomes and reduce cost, motion tracking systems have been used to monitor the user's movements when performing the exercises and provide guidance. Traditional motion tracking systems are based on either cameras or inertial measurement unit (IMU) sensors. Camera-based systems face problems caused by occlusion and lighting. Traditional IMU-based systems require at least two IMU sensors to track the motion of the entire limb, which is not convenient for use. In this paper, we propose a novel limb motion tracking system that uses a single 9-axis IMU sensor that is worn on the distal end joint of the limb (i.e., wrist for the arm or ankle for the leg). Limb motion tracking using a single IMU sensor is a challenging problem because 1) the noisy IMU data will cause drift problem when estimating position from the acceleration data, 2) the single IMU sensor measures the motion of only one joint but the limb motion consists of motion from multiple joints. To solve these problems, we propose a recurrent neural network (RNN) model to estimate the 3D positions of the distal end joint as well as the other joints of the limb (e.g., elbow or knee) from the noisy IMU data in real time. Our proposed approach achieves high accuracy with a median error of 7.2/7.1 cm for the wrist/elbow joint in leave-one-subject-out cross validation when tracking the arm motion, outperforming the state-of-the-art approach by more than 10%. In addition, the proposed model is lightweight, enabling real-time applications on mobile devices.Clinical relevance- This work has great potential to improve limb exercises monitoring and RoM measurement in home-based physical therapy. It is also cost effective and can be made available widely for immediate application.

A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection.

Cough is a major symptom of respiratory-related diseases. There exists a tremendous amount of work in detecting coughs from audio but there has been no effort to identify coughs from solely inertial measurement unit (IMU). Coughing causes motion across the whole body and especially on the neck and head. Therefore, head motion data during coughing captured by a head-worn IMU sensor could be leveraged to detect coughs using a template matching algorithm. In time series template matching problems, K-Nearest Neighbors (KNN) combined with elastic distance measurement (esp. Dynamic Time Warping (DTW)) achieves outstanding performance. However, it is often regarded as prohibitively time-consuming. Nearest Centroid Classifier is thereafter proposed. But the accuracy is comprised of only one centroid obtained for each class. Centroid-based Classifier performs clustering and averaging for each cluster, but requires manually setting the number of clusters. We propose a novel self-tuning multi-centroid template-matching algorithm, which can automatically adjust the number of clusters to balance accuracy and inference time. Through experiments conducted on synthetic datasets and a real-world earbud-based cough dataset, we demonstrate the superiority of our proposed algorithm and present the result of cough detection with a single accelerometer sensor on the earbuds platform.Clinical relevance- Coughing is a ubiquitous symptom of pulmonary disease, especially for patients with COPD and asthma. This work explores the possibility and and presents the result of cough detection using an IMU sensor embedded in earables.

Electromyography and Inertial Motion Sensors Based Wearable Data Acquisition System for Stroke Patients: A Pilot Study.

Development of wearable data acquisition systems with applications to human-machine interaction (HMI) is of great interest to assist stroke patients or people with motor disabilities. This paper proposes a hybrid wireless data acquisition system, which combines surface electromyography (sEMG) and inertial measurement unit (IMU) sensors. It is designed to interface wrist extension with external devices, which allows the user to operate devices with hand orientations. A pilot study of the system performed on four healthy subjects has successfully produced two different control signals corresponding to wrist extensions. Preliminary results show a high correlation (0.42-0.75) between sEMG and IMU signals, thus proving the feasibility of such a system. Results also show that the developed system is robust as well as less susceptible to external interferences. The generated control signals can be used to perform real-time control of different devices in daily-life activities, such as turning ON/OFF of lights in a smart home, controlling an electric wheelchair, and other assistive devices. Such a system will help decrease the dependency of disabled people on their caretakers and empower them to perform their daily-life activities independently.

Volitional EMG Controlled Wearable FES System for Lower Limb Rehabilitation.

Muscle rehabilitation by functional electrical stimulation (FES) is one of the effective treatments for the patients with neuromuscular diseases. The conventional FES applications, however, have limitations that utilize predetermined or repetitive stimulation patterns with the help of experts such as physical therapists. Therefore, we propose a wearable FES system in which the stimulus intensity is dynamically controlled by the motion intention of user in this paper. The proposed FES system utilizes electromyography (EMG) and inertial measurement unit (IMU) sensors for estimating the motion intention regardless of electrical stimulation, and is designed for the lower limb rehabilitation. The overall system configurations including hardware and software are presented in this paper, and the system performance was tested by lower limb exercises, e.g., squat, heel lift, and gait.

Workload Management System for Cricketers.

Cricketers are dynamic players in the field and hence more vulnerable to injuries. The injury rate of Sri Lankan cricketers is very high, resulting in their careers being shortened. Therefore, we established a workload management system for cricketers to resolve this issue with wearable Inertial Measurement Unit (IMU) sensors mounted on their bodies. In order to mitigate their accidents, we evaluated kinds of the activities performed by an athlete using Convolutional Neural Network (CNN) and computed the workload parameters after the session. The expected results of our project were to develop a system to collect and analyze the critical workload parameters of cricketers and showcase results in a user-friendly manner.

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

AbstractSafe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2$^\circ$and 3$^\circ$, sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1$^\circ$). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

Development of a Lower Limb Finite Element Musculoskeletal Gait Simulation Framework Driven Solely by Inertial Measurement Unit Sensors

Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have a major disadvantage that makes them challenging to apply in clinical environments, i.e., they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb driven solely by inertial measurement unit sensors that include the tissue geometries of the entire knee joint, and that combine modeling of 16 muscles into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input. The target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (11.3 MPa) occurred on the medial side of the cartilage at the maximum loading response. The developed framework combines measurement convenience and accurate modeling, and shows promise for clinical applications aimed at understanding subject-specific biomechanics.

Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults.

The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants’ TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clinicians to assess older adults’ fall risks remotely through the evaluation of the TUG score during their daily walking.