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In this research, we present two models (hierarchical model and joint distribution model) and compare between two datasets (MoVi and MotionSense), using only two IMU sensors on right and left hand and motion sense dataset using mobile phone, to predict gender with activity and see how every activity reflect on gender, and explore the efficiency on using autocorrelation function as a feature extractor and compare between three classifiers, Random Forest (RF), Support Vector Machine (SVM) and Convolution Neural Network (CNN).
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In this paper, only two IMU sensors are applied to collect information of the angle and angular velocity of the hip joint in the situation of level-ground walking, ramp ascent, and ramp descent.
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Traditional IMU-based motion tracking systems use 9-axis IMU sensors that include an accelerometer, gyroscope, and magnetometer.
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Accurate and reliable vehicle velocity estimation is greatly motivated by the increasing demands of high-precision motion control for autonomous vehicles and the decreasing cost of the required multi-axis IMU sensors.
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Hoof-mounted IMU sensors with high resolution seem to be a practical and valuable approach to accurately examine BreakD and factors influencing this parameter.
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Detailed examinations of different parts of the stance phase are more accurately performed using hoof-mounted IMU sensors.
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To this end, IMU sensors were attached to the proximal and distal upper and lower extremities of the person being examined and a DTR test was performed without and with a thumb protector worn.
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In the sensor based HAR application, most of the researchers used many IMU sensors to get an accurate HAR classification.
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In this paper, we perform kinematic analysis on time series data from IMU sensors for fatigue detection on runners, using several unsupervised machine learning techniques.
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We implement two intelligent sensing systems that use the proposed DFN: 1) a mobile robot that avoids obstacles detected by a camera and 2) a smartphone-based human activity recognizer using IMU sensors, where different sizes of DFNs are generated to complete the task under various resource budgets, i.
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Hoof-mounted IMU sensors with high resolution seem to be a practical and valuable approach to accurately examine BreakD and factors influencing this parameter.
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In this research, we present two models (hierarchical model and joint distribution model) and compare between two datasets (MoVi and MotionSense), using only two IMU sensors on right and left hand and motion sense dataset using mobile phone, to predict gender with activity and see how every activity reflect on gender, and explore the efficiency on using autocorrelation function as a feature extractor and compare between three classifiers, Random Forest (RF), Support Vector Machine (SVM) and Convolution Neural Network (CNN).
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We further show that skin tone, IMU sensors, and user-level calibration affect measurement error, and must be considered when designing wrist-worn SpO2 sensors and measurement algorithms.
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This fusion problem is slightly different from Simultaneous Localization and Mapping (SLAM) or Visual Inertial Odometry (VIO) in that six linear displacement sensors are tightly coupled with IMU sensors while those sensors in SLAM or VIO problem still provides partial measurement of motion state.
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The IMU sensors are capable of detecting small changes of nutations (i.
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The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly.
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Detailed examinations of different parts of the stance phase are more accurately performed using hoof-mounted IMU sensors.
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This paper describes a small UAV-based SAR using low-cost 5–6 GHz radar, GNSS/RTK and IMU sensors for generation of high-resolution images.
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The purpose of this paper is to find and examine the impact of error of MTw Xsens IMU sensors.
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UAVs equipped with low-cost GNSS/IMU sensors can provide information about position and attitude of the images.
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In this paper, we propose a portable ring-type wireless mouse scheme based on IMU sensors and a multi-level decision algorithm.
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In this paper, only two IMU sensors are applied to collect information of the angle and angular velocity of the hip joint in the situation of level-ground walking, ramp ascent, and ramp descent.
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Based on a monocular visual-inertial navigation system (VINS-mono), a state-of-the-art fusion performance of monocular vision and IMU, this paper designs a new initialization scheme that can calculate the acceleration bias as a variable during the initialization process so that it can be applied to low-cost IMU sensors.
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IMU sensors are used to capture the human motion, while the pressure sensor to measure the fingertip pressure.
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MEMS/IMU sensors lag behind leading technologies in this respect, but the MEMS/IMU performance rapidly changes while is relatively inexpensive.
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The joints angles were calculated using the data from IMU sensors on a 145 m skiing course with 10° slope using the DP technique.
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The papers in this Research Topic adopt different sensing technologies, such as depth sensors, inertial suits, IMU sensors and force-sensing resistors (FSRs) to capture human movement, while they present diverse approaches for modeling the temporal data.
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In this article, methods of reducing measurement data from IMU sensors and encoders connected to servo motors will be presented.
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Considering that the IMU sensors suffer noise in the measurements and the noise progresses over time, this proposal employs a technique that eliminates the filtering step, and the process is done internally by the network.
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These results suggest that the IMU sensors, when calibrated correctly, can be adequate to analyze head posture.
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However, KF is an unbiased estimation method based on the assumption of Gaussian white noise (GWN) while IMU sensors noise is irregular.
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Preliminary data indicate IMU outputs relating to mobility, impact load asymmetry and range of motion can be obtained using commercially available IMU sensors.
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IMU sensors are mainly the accelerometer, gyroscope, and magnetometer.
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In particular, when using LiDAR and IMU sensors as the inputs, most existing methods still use classical filter-based fusion methods to achieve this task.
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Since vision-only methods and IMU sensors are both easy to drift, we optimize the propagation strategies during the IMU preintegration.
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The data collected by the accelerometers of the IMU sensors are filtered using discrete wavelet transform and used to measure the PA in ageing people with an uncertainty-based thresholding method.
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In this paper, a measurement procedure for measuring the precision of IMU sensors is presented.
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Using the external environment information obtained by lidar and IMU sensors, the corresponding SLAM algorithm was developed and designed under the Linux system based on the distributed framework of ROS operating system.
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Using IMU sensors attached to the patients’ leg, knee flexion can be monitored while the patients are recovering in their home environment.
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Due to the need for practical and accurate estimation of joint kinematics, this study implements a reduced number of IMU sensors and employs machine learning algorithm to map sensor data to joint angles.
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Traditional IMU-based motion tracking systems use 9-axis IMU sensors that include an accelerometer, gyroscope, and magnetometer.
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There are several algorithms that use the 3D acceleration and/or rotational velocity vectors from IMU sensors to identify gait events (i.
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EMG and IMU sensors are interfaced with the patient's thigh muscles to classify the patient's intent as three states: forward, backward and rest.
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The simulation model includes the model of IMU sensors, where the location, misalignments, and scaling parameters are also incorporated in the tuning procedure.
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On the other hand, low-cost solutions such as IMU sensors and visual markers exist, but they suffer from large tracking errors.
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In the presented work, a prototype of such a system is developed using IMU sensors - accelerometer and gyroscope.
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‘The IMU sensors measure body-posture angles via the fusion of data from an accelerometer, magnetometer and gyroscope contained within each sensor’ [1].
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IMU sensors monitor the physical orientation of the vehicle.
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This article reviews the key technologies (IMU sensors, communication technology, data fusion and data analysis techniques) that enable the implementation of wearable sensors for performance analysis in sports.
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It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors.
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Gait analysis was performed by letting 17 dairy cows walk in a straight line at their own chosen pace while equipped with IMU sensors on tubera sacrale, left and right tuber coxae (LTC and RTC), back, withers, head, neck and all four lower limbs.
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Kinematics for each of the 3 joints were measured via IMU sensors in addition to a "gold standard" passive marker optical motion capture system.
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To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources.
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Towards addressing this problem, this paper presents ZeroNet, a system that shows the feasibility of developing ML models for IMU sensors with zero training overhead.
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The gesture recognition module consists of a glove built of flex and IMU sensors; the health monitoring devices comprise of ECG, Temperature and PPG; and the UWB based communication is responsible for alert messaging and indoor localization.
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ViMove system was used to obtain ROM by attaching two IMU sensors at the cervical (Occiput-T3) and lumbar spine (L1-Sacrum).
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This paper introduces a Quaternion-based rotational representation of low power, low resource, commercial-of-the-shelf (COTS) IMU sensors, offering high accuracy, reliability and real time performance.
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The portable sensing suit is composed of three IMU sensors (wearable sensors for gait phase detection) and two footswitches (ground truth measurement and not needed for gait detection of the proposed algorithm).
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According to the analysis of state of art researches of overseas UAV visual navigation system, most of which focus on extracting information of aircraft attitude and navigation by utilizing visual information solely or forming a combinational navigation system with united vision, IMU sensors.
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Different from other works, we propose a framework that only use off-the-shelf smartphone’s IMU sensors to detect and classify the vechicle’s steer change during driving.
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The objective of the article is to present posturographic apparatus called POSTURON built on the shape of braces with IMU sensors, which in combination with a microprocessor and a program to process these data, allow assessing the patient's current state of balance and if necessary help him in its correction through vibrational signals.
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Two IMU sensors on hip and knee joint and two FSR sensors in a self-made shoe provide four inputs for the algorithms.
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In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different feature engineering and machine learning algorithms, including both traditional and deep learning methods.
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Our system combines a 3D pose estimation from vision and IMU sensors.
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The bending profiles of the fingers are recorded using ArUco trackers and IMU sensors that are embedded on the adaptive fingers and which are used to train appropriate regression models.
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Our proof-of-concept setup consists of six IMU sensors mounted on the fingers and back of the hand.
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The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking.
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One main issue of these systems is that wearing multiple on-body IMU sensors may bring inconvenience to users' daily life.
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Because the IMU sensors are used in every device that consists of a movement analyzing feature, searching for new applications to use them brings a big gain.
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To accomplish that, an extended Kalman filter (EKF) algorithm is utilized to fuse the data from the wheel encoders, GPS and IMU sensors.
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In addition, our dataset also contains acceleration and gyroscope data from IMU sensors, and 94262 RGB images (47131 images from each web camera).
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First, a custom finger tracking system is developed using IMU sensors, which is lightweight and comfortable enough to allow free movement of the surgeon’s fingers/hands while using instruments.
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