Angular Velocity Data Research Articles

Towards a Modular Pathological Tremor Simulation System Based on the Stewart Platform.

Wearable technologies have aided in reducing pathological tremor symptoms through non-intrusive solutions that aim to identify patterns in involuntary movements and suppress them using actuators positioned at specific joints. However, during the development of these devices, tests were primarily conducted on patients due to the difficulty of faithfully simulating tremors using simulation equipment. Based on studies characterizing tremors in Parkinson's disease, the development of a robotic manipulator based on the Stewart platform was initiated, with the goal of satisfactorily simulating resting tremor movements in the hands. In this work, a simulator was implemented in a computational environment using the multibody dynamics method. The platform structure was designed in a virtual environment using SOLIDWORKS® v2017 software and later exported to Matlab® R17a software using the Simulink environment and Simscape multibody library. The workspace was evaluated, and the Kalman filter was used to merge acceleration and angular velocity data and convert them into data related to the inclination and rotation of real patients' wrists, which were subsequently executed in the simulator. The results show a high correlation and low dispersion between real and simulated signals, demonstrating that the simulated mechanism has the capacity to represent Parkinson's disease resting tremors in all wrist movements. The system could contribute to conducting tremor tests in suppression devices without the need for the presence of the patient and aid in comparing suppression techniques, benefiting the development of new wearable devices.

A Wireless Self-Powered Sensor Network Based on Dual-Model Convolutional Neural Network Algorithm for Tennis Sports

More and more scholars turn their attention to the use of digital and systematic management methods to further improve the safety and effectiveness of tennis. At present, most of these systems are based on video monitoring technology, and their actual operation process is limited by the deployment environment, and the cost is very high. In order to obtain the track of tennis players, this paper uses the sensor array in the intelligent insole to collect the original data. A Dual-model Convolutional Neural Network (DMCNN) structure is designed to further improve the computational efficiency and accuracy of tennis players’ gait recognition. Combined with quaternion and complementary filtering algorithm, the rotation matrix is established to convert the data in the sensor coordinate system to the reference coordinate system. Use multiple sensors in the smart insole to collect linear and curve track data from tennis players. After the unit conversion and normalization of the original data, the acceleration and angular velocity data in the sensor coordinate system are converted to the reference coordinate system by using quaternions to represent the rotation matrix and using complementary filtering algorithm to update the quaternions. The experimental results show that the displacement error of the scheme is small and the performance is good when the track tracking of tennis players is realized. The error between the estimated distance and the actual distance is within 6%, which proves the accuracy of the linear trajectory experiment.

Performance Evaluation of a Quadcopter by an Optimized Proportional–Integral–Derivative Controller

This paper presents an analysis of a quadcopter’s stability and its sensors’ data reading using an IMU (inertial measurement unit). Firstly, the angular velocity and acceleration data are read by an Stm® development board embedded with an Stm32f401ccu6 microcontroller. The altitude of the measurement instrument platform was determined using an MS5611 barometric pressure sensor combined with a temperature sensor. The quadcopter’s control was achieved by connecting a brushless DC motor to the Stm® board, which received four PWM (pulse-width modulation) signals via the output port. An electronic governor was utilized to control the brushless DC motor, while a pre-existing remote control was designated as the transmitter. The quadcopter receiver received a 2.4 GHz signal from the transmitter using the BLE (Bluetooth low-energy) protocol, which was used to ensure the simultaneous operation of the four brushless DC motors. Finally, a PID (proportional–integral–derivative) controller was employed for parameter adjustment. The collected PID parameter program was developed in the Simulink software as a simulation platform, allowing for the execution of the Simulink model on the Stm® MCU. The Stm® module facilitates monitoring of the performance of the UAV (unmanned aerial vehicle) and enables immediate parameter adjustments to ensure flight stability. This research aims to reduce calculation errors in sensor and controller usage and improve the efficacy of the remote machine module for future industrial applications.

On-Board Unit (OBU)-Supported Longitudinal Driving Behavior Monitoring Using Machine Learning Approaches.

Driving behavior recognition can provide an important reference for the intelligent vehicle industry and probe vehicle-based traffic estimation. The identification of driving behavior using mobile sensing techniques such as smartphone- and vehicle-mounted terminals has gained significant attention in recent years. The present work proposed the monitoring of longitudinal driving behavior using a machine learning approach with the support of an on-board unit (OBU). Specifically, based on velocity, three-axis acceleration and three-axis angular velocity data were collected by a mobile vehicle terminal OBU; through the process of data preprocessing and feature extraction, seven machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor algorithm (KNN), logistic regression (LR), BP neural network (BPNN), decision tree (DT), and the Naive Bayes (NB), were applied to implement the classification and monitoring of the longitudinal driving behavior of probe vehicles. The results show that the three classifiers SVM, RF and DT achieved good performances in identifying different longitudinal driving behaviors. The outcome of the present work could contribute to the fields of traffic management and traffic safety, providing important support for the realization of intelligent transport systems and the improvement of driving safety.

Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit.

Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson's disease. We previously designed a rehabilitation assist device that can detect and classify a user's gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation -0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications.

A fast and accurate camera-IMU calibration method for localization system

&lt;p&gt;Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance. In particular, a localization system based on sensor fusion, such as Visual Simultaneous Localization and Mapping (VSLAM), plays a crucial role in environment perception and serves as the foundation for decision-making and motion control in intelligent vehicles. The accuracy of extrinsic calibration parameters between the camera and IMU is of utmost importance for precise positioning in VSLAM systems. However, existing calibration methods are often time-consuming, rely on complex optimization techniques, and are sensitive to noise and outliers, leading to potential degradation in system performance. To address these challenges, this paper presents a fast and accurate camera-IMU calibration method based on space coordinate transformation constraints and SVD (Singular Value Decomposition) tricks. The method involves constructing constraint equations by ensuring the equality of rotation and transformation matrices between camera frames and IMU coordinates at different time instances. Subsequently, the external parameters of the camera-IMU system are solved using quaternion transformation and SVD techniques. To validate the proposed method, experiments were conducted using the ROS (Robot Operating System) platform, where camera images and velocity, acceleration, and angular velocity data from the IMU were recorded in a ROS bag file. The results demonstrate that the proposed method achieves reliable camera-IMU calibration parameters, requiring less tuning time and exhibiting reduced uncertainty.&lt;/p&gt;

Predicting ankle and knee sagittal kinematics and kinetics using an ankle-mounted inertial sensor

The purpose of this study was to develop a machine learning model to reconstruct time series kinematic and kinetic profiles of the ankle and knee joint across six different tasks using an ankle-mounted IMU. Four male collegiate basketball players performed repeated tasks, including walking, jogging, running, sidestep cutting, max-height jumping, and stop-jumping, resulting in a total of 102 movements. Ankle and knee flexion-extension angles and moments were estimated using motion capture and inverse dynamics and considered ‘actual data’ for the purpose of model fitting. Synchronous acceleration and angular velocity data were collected from right ankle-mounted IMUs. A time-series feature extraction model was used to determine a set of features used as input to a random forest regression model to predict the ankle and knee kinematics and kinetics. Five-fold cross-validation was performed to verify the model accuracy, and statistical parametric mapping was used to determine the difference between the predicted and experimental time series. The random forest regression model predicted the time-series profiles of the ankle and knee flexion-extension angles and moments with high accuracy (Kinematics: R2 ranged from 0.782 to 0.962, RMSE ranged from 2.19° to 11.58°; Kinetics: R2 ranged from 0.711 to 0.966, RMSE ranged from 0.10 Nm/kg to 0.41 Nm/kg). There were differences between predicted and actual time series for the knee flexion-extension moment during stop-jumping and walking. An appropriately trained feature-based regression model can predict time series knee and ankle joint angles and moments across a wide range of tasks using a single ankle-mounted IMU.

Foot Pronation Prediction with Inertial Sensors during Running: A Preliminary Application of Data-Driven Approaches.

Abnormal foot postures may affect foot movement and joint loading during locomotion. Investigating foot posture alternation during running could contribute to injury prevention and foot mechanism study. This study aimed to develop feature-based and deep learning algorithms to predict foot pronation during prolonged running. Thirty-two recreational runners have been recruited for this study. Nine-axial inertial sensors were attached to the right dorsum of the foot and the vertical axis of the distal anteromedial tibia. This study employed feature-based machine learning algorithms, including support vector machine (SVM), extreme gradient boosting (XGBoost), random forest, and deep learning, i.e., one-dimensional convolutional neural networks (CNN1D), to predict foot pronation. A custom nested k-fold cross-validation was designed for hyper-parameter tuning and validating the model's performance. The XGBoot classifier achieved the best accuracy using acceleration and angular velocity data from the foot dorsum as input. Accuracy and the area under curve (AUC) were 74.7 ± 5.2% and 0.82 ± 0.07 for the subject-independent model and 98 ± 0.4% and 0.99 ± 0 for the record-wise method. The test accuracy of the CNN1D model with sensor data at the foot dorsum was 74 ± 3.8% for the subject-wise approach with an AUC of 0.8 ± 0.05. This study found that these algorithms, specifically for the CNN1D and XGBoost model with inertial sensor data collected from the foot dorsum, could be implemented into wearable devices, such as a smartwatch, for monitoring a runner's foot pronation during long-distance running. It has the potential for running shoe matching and reducing or preventing foot posture-induced injuries.

Estimation for Human Motion Posture and Health Using Improved Deep Learning and Nano Biosensor

To improve the technical level of human motion posture and health estimation, a human motion posture and health estimation algorithm based on Nano biosensor and improved deep learning is proposed. First, we use Nano biological acceleration sensor and Nano biological angular velocity sensor to obtain human motion posture and health data. Second, after the fusion processing of human motion posture and health data, we use the motion posture coordinate system conversion unit and the physiological information recognition unit to convert the coordinate system of human motion angular velocity and acceleration data and recognize the physiological information of blood pressure and heart rhythm. Finally, the convolution neural networks (CNN) in deep learning is improved to obtain the deformable CNN. The transformed angular velocity, physiological information recognition results and other human posture data are input into the deformable CNN, and the human posture estimation results are output. Experiments show that proposed algorithm can accurately obtain human posture data, can quickly and accurately estimate human posture, and has a good application effect. It has important application value in identity recognition and sports performance analysis.

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation.

We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.

Development of a Method for Evaluating Elderly Mobility Functions Using Acceleration Sensors in Smartphones

Locomotive syndrome (LS) is a condition in which a person's mobility is impaired due to musculoskeletal disorders caused by aging or lack of exercise, signaling an imminent need for primary nursing care. This study aims to develop a smartphone application that can distinguish the LS stage in a safe, simple, and quantifiable manner. Using a six‐axes accelerometer built into the smartphone, acceleration and angular velocity data were collected from elderly subjects performing alternating one‐legged stances at intervals of 2.66 s. Frequency analysis was then performed with a fast Fourier transform (FFT) and an autocorrelation coefficient to compare the data characteristics of subjects with and without LS. Due to huge individual variances, significant differences were not observed between LS and non‐LS, and discriminating the LS stage using frequency analysis was considered difficult in the present study. In contrast, the autocorrelation coefficient showed significant differences between LS and non‐LS in acceleration and angular velocity in specific directions. Using the autocorrelation coefficient of acceleration and angular velocity data to detect the presence of LS was concluded to be effective. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Reactive postural responses predict risk for acute musculoskeletal injury in collegiate athletes.

Examine if objective assessments of reactive postural responses, quantified using wearable inertial measurement units, are associated with the risk for acute lower extremity musculoskeletal injuries in collegiate athletes. Prospective survival analysis. 191 Division I National Collegiate Athletic Association athletes completed an instrumented version of a modified Push and Release (I-mP&R) test at the beginning of their competitive season. The I-mP&R was performed with eyes closed under single- and dual-task (concurrent cognitive task) conditions. Inertial measurement units recorded acceleration and angular velocity data that was used to calculate time-to-stability. Acute lower extremity musculoskeletal injuries were tracked from first team activity for six months. Cox proportional hazard models were used to determine if longer times to stability were associated with faster time to injury. Longer time-to-stability was associated with increased risk of injury; every 250 ms increase in dual-task median time-to-stability was associated with a 36% increased risk of acute, lower-extremity musculoskeletal injury. Tests of reactive balance, particularly under dual-task conditions, may be able to identify athletes most at risk of acute lower extremity musculoskeletal injury. Clinically-feasible, instrumented tests of reactive should be considered in assessments for prediction and mitigation of musculoskeletal injury in collegiate athletes.

Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals.

Wearable sensors allow for the objective analysis of gait and motion both in and outside the clinical setting. However, it remains a challenge to apply such systems to highly diverse patient populations, including individuals with lower-limb amputations (LLA) that present with unique gait deviations and rehabilitation goals. This paper presents the development of a novel method using continuous gyroscope data from a single inertial sensor for person-specific classification of gait changes from a physiotherapist-led gait training session. Gyroscope data at the thigh were collected using a wearable gait analysis system for five LLA before, during, and after completing a gait training session. Data from able-bodied participants receiving no intervention were also collected. Models using dynamic time warping (DTW) and Euclidean distance in combination with the nearest neighbor classifier were applied to the gyroscope data to classify the pre- and post-training gait. The model achieved an accuracy of 98.65% ± 0.69 (Euclidean) and 98.98% ± 0.83 (DTW) on pre-training and 95.45% ± 6.20 (Euclidean) and 94.18% ± 5.77 (DTW) on post-training data across the participants whose gait changed significantly during their session. This study provides preliminary evidence that continuous angular velocity data from a single gyroscope could be used to assess changes in amputee gait. This supports future research and the development of wearable gait analysis and feedback systems that are adaptable to a broad range of mobility impairments.

Application of Machine Learning Technology for Automatic Motion Recognition for Japanese Tea-Making Procedure of CHA-NO-YU

CHA-NO-YU, the Japanese traditional tea-making ceremony, has been a research subject in many academic fields including anthropology, psychology, and engineering. To effectively study the CHA-NO-YU, automatic recognition of motions of the tea-making procedure is desirable. Hence, herein, a method is proposed to recognize 12 motion classes of the tea-making procedure with acceleration, angular velocity, and right-hand tilt angle data. In the experiment, one Japanese subject with 18 years of experience in CHA-NO-YU performed the 12 motion classes repeatedly, and a 99-sensor dataset was obtained for every motion class. In the recognition step, random forest classifier was adopted. Finally, the recognition of the 12 motion classes of the tea-making procedure could be realized with greater than 0.98 accuracy, precision, recall, and F1-score

Mixture of Experts for Unmanned Aerial Vehicle Motor Thrust Models

AbstractThis study aims to analyze three unmanned aerial vehicles’ static and dynamic motor thrust models and to compare them with flight test telemetry motor angular velocity data as ground truth. This comparison determines which model is the most accurate based on the root mean square of the difference between the motor models and the telemetry data values. The Burgers and Staples models are dynamic thrust models with nonlinear drag, while the Gibiansky model is a static thrust model with linear drag. The mixture of experts (MoE) architecture assigns weights to each thrust model through a gating network such that higher weights map to the more accurate models. Flight tests include two maneuvers: triangle and shoelace loop. The triangle maneuver uses the cornering option to stop and turn at each waypoint. In contrast, the shoelace loop maneuver uses the curved option to fly smooth curves at waypoints instead of stopping and turning at each waypoint. Simulation results of the motor thrust models use the telemetry data to compute the motor angular velocities with tuned model parameters tailored to the type of maneuver. Regarding motor angular velocity estimation, the Gibiansky and Burgers models tend to overestimate, while the Staples model tends to underestimate. The combination of the simulation results of the models with the mixture of experts allows us to achieve higher accuracy than any of the individual models.

Validity of Spatio-Temporal Gait Parameters in Healthy Young Adults Using a Motion-Sensor-Based Gait Analysis System (ORPHE ANALYTICS) during Walking and Running.

Motion sensors are widely used for gait analysis. The validity of commercial gait analysis systems is of great interest because calculating position/angle-level gait parameters potentially produces an error in the integration process of the motion sensor data; moreover, the validity of ORPHE ANALYTICS, a motion-sensor-based gait analysis system, has not yet been examined. We examined the validity of the gait parameters calculated using ORPHE ANALYTICS relative to those calculated using conventional optical motion capture. Nine young adults performed gait tasks on a treadmill at speeds of 2−12 km/h. The three-dimensional position data and acceleration and angular velocity data of the feet were collected. The gait parameters were calculated from motion sensor data using ORPHE ANALYTICS, and optical motion capture data. Intraclass correlation coefficients [ICC(2,1)] were calculated for relative validities. Eight items, namely, stride duration, stride length, stride frequency, stride speed, vertical height, stance phase duration, swing phase duration, and sagittal angleIC exhibited excellent relative validities [ICC(2,1) > 0.9]. In contrast, sagittal angleTO and frontal angleIC demonstrated good [ICC(2,1) = 0.892−0.833] and moderate relative validity [ICC(2,1) = 0.566−0.627], respectively. ORPHE ANALYTICS was found to exhibit excellent relative validities for most gait parameters. These results suggest its feasibility for gait analysis outside the laboratory setting.

Kinetics of the front-arm technique in cricket fast bowling

The aim of this study was to investigate the kinetics of the front arm in cricket fast bowling. Eighteen male fast bowlers (mean age = 17.2 ± 1.6 years; mean height = 1.89 ± 0.05 m; mean mass = 85.0 ± 10.0 kg) were recruited from NSW grade club level and their bowling actions recorded by a Cortex 2.0 motion analysis system (200 Hz). The interaction between joint power and joint angular velocity data was observed simultaneously for the front shoulder and elbow joints. A joint actuation strategy (active or controlled) was assigned to shoulder flexion–extension, abduction–adduction, and elbow flexion–extension motion to understand whether the joint kinematics followed the time-history of the joint torque. The results only partially agreed with the commonly prescribed coaching maxim of “pulling down” the front arm for faster ball speeds. Active extension and adduction of the front shoulder joint were observed during the delivery phase of the action, whereas the acceleration phase was marked by periods of controlled shoulder extension and active shoulder flexion. At the elbow joint, active elbow flexion was observed at a time when it may reduce the moment of inertia about the thoracic and lumbar spine rotation axes. Overall, combinations of active and controlled joint powers were observed for the front shoulder and elbow joints, implying that this sample of bowlers did not adhere to the traditional notion of “pulling down” the front arm as fast as possible.

Adaptations of Explainable Artificial Intelligence (XAI) to Agricultural Data Models with ELI5, PDPbox, and Skater using Diverse Agricultural Worker Data

We use explainable artificial intelligence (XAI) based on Explain Like I’m 5 (ELI5), Partial Dependency Plot box (PDPbox), and Skater to analyze diverse physical agricultural (agri-) worker datasets. We have developed various promising body-sensing systems to enhance agri-technical advancement, training and worker development, and security. This includes wearable sensing systems (WSSs) that can capture real-time three-axis acceleration and angular velocity data related to agri-worker motion by analyzing human dynamics and statistics in different agri-environments, such as fields, meadows, and gardens. After investigating the obtained time-series data using a novel program written in Python, we discuss our findings and recommendations with real agri-workers and managers. In this study, we use XAI and visualization to analyze diverse data of experienced and inexperienced agri-workers to develop an applied method for agri-directors to train agri-workers.

Smart Shirt for Measuring Trunk Orientation

Improper cycling posture is linked to a variety of spinal musculoskeletal diseases, including structural malformation of the spine and back discomfort. This paper presents a novel smart shirt integrated tri-axial gyroscope and accelerometer that can detect postural variation in terms of spinal curvature changes. To provide accurate feedback to the wearer and improve the wearer's correct movement, the garment is able to recognize trunk body posture. The gyroscope/accelerometer was placed around the upper and mid trunk of the user to record tri-axial angular velocity data. The device can also be used to help determine the trunk bending angle and monitor body postures in order to improve optimal orientation and position. The garment enables continuous measurement in the field at high sample rates (50 Hz), and the sensor has a large measurement range (16 g, 2000°/s). As electronic components are non-washable, instead of encapsulating them, a detachable module was created. In this, magnets are embedded in the jersey, and allow the positioning and removal of the sensor. The test results show that the average trunk-bending angle was 21.5°, and 99 percent of the observed angle fell within the standard (ranging from 8° to 35°). The findings demonstrate the feasibility of employing the smart shirt sensor to estimate trunk motions in the field on a regular basis.

Analyses of Diverse Agricultural Worker Data with Explainable Artificial Intelligence: XAI based on SHAP, LIME, and LightGBM

We use recent explainable artificial intelligence (XAI) based on SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Light Gradient Boosting Machine (LightGBM) to analyze diverse physical agricultural (agri-) worker datasets. We have developed various promising body-sensing systems to enhance agri-technical advancement, training and worker development, and security. However, existing methods and systems are not sufficient for in-depth analysis of human motion. Thus, we have also developed wearable sensing systems (WS) that can capture real-time three-axis acceleration and angular velocity data related to agri-worker motion by analyzing human dynamics and statistics in different agri-fields, meadows, and gardens. After investigating the obtained time-series data using a novel program written in Python, we discuss our findings and recommendations with real agri-workers and managers. In this study, we use XAI and visualization to analyze diverse data of experienced and inexperienced agri-workers to develop an applied method for agri-directors to train agri-workers.