IMU Sensor Research Articles

Fractional-Order Identification of Gyroscope MEMS Noise Under Various Temperature Conditions

This paper deals with identifying the fractional-order noise parameters for MEMS gyroscopes under various temperature conditions. The significant contribution of the paper is to investigate the relation between the fractional noise model of MEMS devices and different ambient temperatures. In our paper, variance, correlation, and introduced estimation analysis methods have been meticulously applied to determine noise parameters with fractional-order dynamics. Experimental data were collected precisely under various ambient temperatures, while the MEMS device was located in a climate chamber. The origin of the paper is motivated by a project entitled “Family of optoelectronic heads for guided missiles—SEEKER”, where the IMU sensor is a crucial electronic device used to measure the angular velocity of the optoelectronic head. It is widely known that the IMU measurements built-in MEMS technology often come with a random walk, as well as biases and noises affecting the final results.

Is it safe to use sand surface during rehab when training basic skills in early stages? How sand surface and inertial sensor position affect Drop Jump landing's G-forces and it's application in injury recovery

In sports, the use of sand surface as a tool in the injury recovery process has received very good acceptance and an increased attention in recent years. Unfortunately, the number of studies in this area continues being scarce. Therefore, this study analyzes the existence of possible differences in the magnitude of the impact generated during a 45 cm Drop Jump (DJ), depending on the body area analyzed, the contact surface (sand or grass) and the height (thoracic spine and ankle) at which a jump is placed. To that purpose, 6 participants were analyzed by wearing 3 IMU sensors (WIMU PRO™) to measure G foreces in the landing phase of the DJ. The results suggest the existence of statistically significant differences when comparing surfaces (sand vs. grass), body area (ankle and lumbar) and location of device placement (thoracic area vs. ankle).

Pre-Impact Fall Detection for E-Scooter Riding Using an IMU: Threshold-Based, Supervised, and Unsupervised Approaches

Pre-impact fall detection during e-scooter riding is essential for rider safety. Both threshold-based and deep learning algorithms (supervised and unsupervised models) were developed in this study. Twenty participants performed normal driving maneuvers such as straight driving, speed bumps, clockwise roundabouts, and counterclockwise roundabouts, along with falls (abnormal driving maneuvers). A 6-axis IMU sensor (Xsens DOT, The Netherlands) was positioned at the T7 location to record data at 60 Hz. The approaches included threshold-based, supervised learning, and unsupervised learning models The threshold-based approach yielded an accuracy of 98.86% with an F1 score of 0.99, while the supervised model had a slightly lower performance, reaching 86.29% accuracy and an F1 score of 0.56. The unsupervised knowledge distillation model achieved 98.86% accuracy, an F1 score of 0.99, and a memory size of only 46 kB. All models demonstrated lead times of more than 250 ms, sufficient for airbag deployment.

Detection of Eating Gestures in Older Persons Using IMU Sensors With Multistage Temporal Convolutional Network

Detection of Eating Gestures in Older Persons Using IMU Sensors With Multistage Temporal Convolutional Network

The Problems and Design of a Neck Dummy.

This paper addresses the biomechanical requirements and design of a neck dummy for assessing neck injury risks. The need for an accurate biomechanical representation of the human neck in crash tests is highlighted, emphasizing the importance of replicating the neck's response to impacts. Existing neck dummies are reviewed to assess their similarity to human neck biomechanics, revealing several limitations. To address these gaps, a novel prototype is proposed to mimic the joint between two vertebrae using elastic elements to replicate the behavior of the intervertebral disc. The performance of the neck dummy is evaluated through experimental testing, using IMU and force sensors to monitor its response to perturbations from impacts. The reported results demonstrate that the prototype effectively simulates the intervertebral movement, offering an approach for more accurate injury assessments in crash testing. Concluding remarks suggest the potential of this design to improve the reliability of neck injury assessments in automotive safety research.

Total Least Squares In-Field Identification for MEMS-Based Inertial Measurement Units

Inertial Measurement Units are widely used in various applications and, hardware-wise, they primarily consist of a tri-axial accelerometer and a tri-axial gyroscope. For low-end commercial employments, the low cost of the device is crucial: this makes MEMS-based sensors a popular choice in this context. However, MEMS-based transducers are prone to significant, non-uniform and environmental-condition-dependent systematic errors, that require frequent re-calibration to be eliminated. To this end, identification methods that can be performed in-field by non-expert users, without the need for high-precision or costly equipment, are of particular interest. In this paper, we propose an in-field identification procedure based on the Total Least Squares method for both tri-axial accelerometers and gyroscopes. The proposed identification model is linear and requires no prior knowledge of the parameters to be identified. It enables accelerometer calibration without the need for specific reference surface orientation relative to Earth’s gravity and allows gyroscope calibration to be performed independently of accelerometer data, without requiring the sensor’s sensitive axes to be aligned with the rotation axes during calibration. Experiments conducted on NXP sensors FXOS8700CQ and FXAS21002 demonstrated that using parameters identified by our method reduced cross-validation standard deviations by about two orders of magnitude compared to those obtained using manufacturer-provided parameters. This result indicates that our method enables the effective calibration of IMU sensor parameters, relying only on simple 3D-printed equipment and significantly improving IMU performance at minimal cost.

Wireless PID-Based Control for a Single-Legged Rehabilitation Exoskeleton

The demand for remote rehabilitation is increasing, opening up convenient and effective home-based therapy for the sick and elderly. In this study, we use AnyBody simulations to analyze muscle activity and determine key parameters for designing a rehabilitation exoskeleton, as well as selecting the appropriate motor torque to assist patients during rehabilitation sessions. The exoskeleton was designed with a PID control mechanism for the precise management of motor positions and joint torques, and it operates in both automated and teleoperation modes. Hip and knee movements are monitored via smartphone-based IMU sensors, enabling real-time feedback. Bluetooth communication ensures seamless control during various training scenarios. Our study demonstrates that remotely controlled rehabilitation systems can be implemented effectively, offering vital support not only during global health crises such as pandemics but also in improving the accessibility of rehabilitation services in remote or underserved areas. This approach has the potential to transform the way physical therapy can be delivered, making it more accessible and adaptable to the needs of a larger patient population.

Validation of sound source localization quality in binaural audio synthesis with head-tracking

Binaural synthesis with head tracking has become a highly important tool in creating an auditory experience for users that is as similar as possible to their perception of sound in the real world. This is crucial in many different applications that rely on accurate representation of real or virtual sound environments, ranging from scientific research to gaming applications. The quality of the binaural synthesis process and the accuracy and naturalness of the produced result depend on numerous factors, ranging from the selection of suitable IMU sensor technology used for head tracking to the choice of methods for implementing binaural synthesis using generic or individual sets of Head-Related Transfer Functions (HRTF). This is especially significant in Virtual Reality (VR) systems with three or six degrees of freedom that fully immerse the users in the virtual world through visual and auditory stimuli. This paper presents the authors' experiences in addressing a variety of challenges that arose during the design of such spatial audio systems and the testing of sound source localization quality. Strong emphasis is put on the testing of sound source localization quality, where the position of the source dynamically changes in relation to the listener.

Comparison of Gait in Women with Degenerative Changes of the Hip Joint and Healthy Women Using the MoKA System-A Pilot Study.

Osteoarthritis (OA) is a global problem. There are few reports in the literature regarding the temporal and spatial parameters of gait in people with OA. The aim of this study was to determine spatiotemporal parameters for the pelvis and lower limbs during walking in women with OA and to compare these parameters with healthy people. For this purpose, a 6 min walking test (6MWT) was carried out. OA subjects had worse outcomes compared to the control group (p < 0.05). Data were collected using IMU sensors integrated into the MoKA system and mounted on indicator points on the body. Limited mobility of the pelvis in the frontal plane was observed in the study group, which influenced walking strategy. For the comparison with the control group at each minute, p < 0.05. IMU sensors attached to the body and integrated in one application provide extensive research and diagnostic capabilities.

456P Gait analysis by IMU sensor in myotonic dystrophy type 1

456P Gait analysis by IMU sensor in myotonic dystrophy type 1

Validity of IMU sensors for assessing features of walking in laboratory and outdoor environments among older adults

IntroductionIMU sensors (three-dimensional accelerometer, gyroscope and magnetometer) enable assessment of walking in older adults outside the laboratory. We studied whether IMUs are valid for detecting walking parameters (step events, time, length, and cadence) in a laboratory and outdoors on a level surface in older adults. MethodsThis validation study is part of a larger cross-sectional study. Twenty-six participants (mean age 76 years, 65 % female) walked on a treadmill indoors and on a sport track outdoors at self-selected speed. IMUs were attached laterally on the shanks and on the lower back at the level of L3-L4. Initial contact (IC) and step lengths were also estimated using acceleration signals (vertical, antero-posterior) from the pelvic IMU. Terminal contact (TC) was determined from the shank IMU sagittal angular velocity. For step length, inverted pendulum model and participant’s leg length (0.53 x height) was used. Step duration was calculated from IC to the opposite leg IC and stride duration from IC to next ipsilateral IC. Cadence was calculated as steps/min. As reference data, 3D motion capture was used in the laboratory and a high-speed video camera outdoors. Intraclass correlation coefficients (ICC), root mean squared errors (RMSE), typical errors and Bland-Altman plots were calculated and drawn. ResultsWhen comparing IC timing between IMU and reference data, mean bias was 0.031 s in the laboratory and −0.004 s outdoors, and for TC −0.057 s and −0.070 s respectively. Step and stride duration and cadence showed ICC values >0.80 and mean bias was <0.005 s for step and stride durations and <0.05 steps/min for cadence in both environments. Step length ICC values were <0.40 in the laboratory and outdoors. SignificanceIMUs can be used to monitor temporal walking variables in older adults and may be useful for rehabilitation interventions and functional capacity assessment.

Exploring User-Defined Gestures as Input for Hearables and Recognizing Ear-Level Gestures with IMUs

Hearables are highly functional earphone-type wearables; however, existing input methods using stand-alone hearables are limited in the number of commands, and there is a need to extend device operation through hand gestures. In previous research on hearables for hand input, user understanding and gesture recognition systems have been developed. However, in the realm of user understanding, investigation concerning hand input with hearables remains incomplete, and existing recognition systems have not demonstrated proficiency in discerning user-defined gestures. In this study, we conducted a gesture elicitation study (GES) assuming hand input using hearables under six conditions (three interaction areas x two device shapes). Then, we extracted ear-level gestures that the device's built-in IMU sensor could recognize from the user-defined gestures and investigated the recognition performance. The results of sitting experiments showed that the gesture recognition rate for in-ear devices was 91.0% and that for ear-hook devices was 74.7%.

USV-Tracker: A novel USV tracking system for surface investigation with limited resources

This paper introduces USV-Tracker, a novel tracking system for Unmanned Surface Vehicles (USVs) tailored for practical applications such as surface investigation and target tracking. The system tackles three pivotal challenges: perception robustness, tracking concealment, and planning efficiency. The contributions of this work are manifold: (1) A multi-sensor fusion framework utilizing an Extended Kalman Filter (EKF) to enhance target detection and positioning accuracy, integrating data from cameras, LiDAR, GPS, and IMU sensors. (2) A two-stage path planning algorithm that generates occlusion avoidance trajectories and employs a virtual elastic force constraint to maintain appropriate relative positioning. In dense obstacle environments, the algorithm tends to get closer to the target and incorporates FOV orientation constraints to ensure stable perception. (3) A visibility-aware control strategy that ensures continuous target observability through EKF-based trajectory prediction. Simulations in Gazebo and corresponding physical experiments validate the system’s effectiveness and robustness, demonstrating its applicability in real-world scenarios. The computational workload is managed on a constrained on-board computer, underscoring the system’s practicality.

Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data

This study aims to enhance the navigation capabilities of autonomous tractors by predicting the surface type they are traversing using data collected from BNO055 Inertial Measurement Units (IMU sensors). IMU sensor data were collected from a small mobile robot driven over seven different floor surfaces within a university environment, including tile, carpet, grass, gravel, asphalt, concrete, and sand. Several machine learning models, including Logistic Regression, K-Neighbors, SVC, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and XGBoost, were trained and evaluated to predict the surface type based on the sensor data. The results indicate that Random Forest and XGBoost achieved the highest accuracy, with scores of 98.5% and 98.7% in K-Fold Cross-Validation, respectively, and 98.8% and 98.6% in an 80/20 Random State split. These findings demonstrate that ensemble methods are highly effective for this classification task. Accurately identifying surface types can prevent operational errors and improve the overall efficiency of autonomous systems. Integrating these models into autonomous tractor systems can significantly enhance adaptability and reliability across various terrains, ensuring safer and more efficient operations.

TinyBioGait—Embedded intelligence and homologous time approximation warping for gait biometric authentication from IMU signals

The gait of a subject follows a specific pattern, but variations exist that are unique to a subject but contrasting to other subjects. This can be utilized for biometric authentication to prevent impersonation during gait studies. However, due to the dynamic nature of gait, like changes in gait speed while walking, gait biometric authentications are challenging. In the state-of-the-art, although attempts have been made to use deep learning and other signal processing methods for biometric authentication, which obtained reliable results, these are either highly resource-consuming, require several sensors or need an expensive framework, making it challenging to implement this in many scenarios. Therefore, a knowledge gap exists to build a reliable, inexpensive and resource-efficient gait biometric authentication system. The paper proposes a method for using only one embedded IMU sensor with a microcontroller for tracking the motion of a subject, resource-efficient on-device elimination of the gait speed differences by proposing a homologous time approximation warping algorithm and building a resource-efficient TinyML model for reliable biometric authentication. Based on an experiment consisting of 20 human subjects with consent, the microcontroller’s on-device accuracy score for decision-making by TinyML was found to be 0.9276. The resource efficiency of the model based on memory profiling has been further discussed. Also, the prediction performance of the microcontroller with the proposed optimization was found to be only 8% slower compared to a personal computer, given that several thousands of processes run parallel on a personal computer. The work needs to be further tested for a larger sample space, and data privacy needs to be addressed.

A biomechanical comparison of track spikes with advanced footwear technology to a traditional track spike in female distance runners.

The addition of highly responsive lightweight foam and a stiff plate in the midsole of long-distance road racing shoes has yielded significant energetic cost savings that have translated to notable improvements in performance. This new foam and stiff plate technology have since been implemented in long-distance track spikes, where performances have also improved. However, the impact of spikes with advanced footwear technology (AFT) on distance running biomechanics has been studied minimally to date. Therefore, the purpose of this study was to compare running biomechanics between two spikes which incorporate AFT (Nike ZoomX Dragonfly, Nike Air Zoom Victory) to a traditional spike (Nike Zoom Matumbo). Seventeen competitive collegiate female runners completed 60 m trials at their 5k race pace in each spike condition while outfitted with IMU sensors and plantar pressure insoles. We observed significantly lower peak ankle dorsiflexion in the Dragonfly and Victory compared to the Matumbo and lower whole foot, forefoot and rearfoot peak and average pressure in the Dragonfly compared to the Matumbo and Victory. The acute biomechanical alterations observed in this study warrant future investigation into the association between running biomechanics and racing performance in track spikes with advanced footwear technology.

Online Handwriting Recognition Method with a Non-Inertial Reference Frame Based on the Measurement of Linear Accelerations and Differential Geometry: An Alternative to Quaternions.

This work describes a mathematical model for handwriting devices without a specific reference surface (SRS). The research was carried out on two hypotheses: the first considers possible circular segments that could be made during execution for the reconstruction of the trace, and the second is the combination of lines and circles. The proposed system has no flat reference surface, since the sensor is inside the pencil that describes the trace, not on the surface as in tablets or cell phones. An inertial sensor was used for the measurements, in this case, a commercial Micro-Electro Mechanical sensor of linear acceleration. The tracking device is an IMU sensor and a processing card that allows inertial measurements of the pen during on-the-fly tracing. It is essential to highlight that the system has a non-inertial reference frame. Comparing the two proposed models shows that it is possible to construct shapes from curved lines and that the patterns obtained are similar to what is recognized; this method provides an alternative to quaternion calculus for poorly specified orientation problems.

Estimation of Normal Ground Reaction Forces in Multiple Treadmill Skiing Movements Using IMU Sensors With Optimized Locations

Estimation of Normal Ground Reaction Forces in Multiple Treadmill Skiing Movements Using IMU Sensors With Optimized Locations

DeepIOD: Towards A Context-Aware Indoor-Outdoor Detection Framework Using Smartphone Sensors.

Accurate indoor-outdoor detection (IOD) is essential for location-based services, context-aware computing, and mobile applications, as it enhances service relevance and precision. However, traditional IOD methods, which rely only on GPS data, often fail in indoor environments due to signal obstructions, while IMU data are unreliable on unseen data in real-time applications due to reduced generalizability. This study addresses this research gap by introducing the DeepIOD framework, which leverages IMU sensor data, GPS, and light information to accurately classify environments as indoor or outdoor. The framework preprocesses input data and employs multiple deep neural network models, combining outputs using an adaptive majority voting mechanism to ensure robust and reliable predictions. Experimental results evaluated on six unseen environments using a smartphone demonstrate that DeepIOD achieves significantly higher accuracy than methods using only IMU sensors. Our DeepIOD system achieves a remarkable accuracy rate of 98-99% with a transition time of less than 10 ms. This research concludes that DeepIOD offers a robust and reliable solution for indoor-outdoor classification with high generalizability, highlighting the importance of integrating diverse data sources to improve location-based services and other applications requiring precise environmental context awareness.

Pseudo-Normalization via Integer Fast Inverse Square Root and Its Application to Fast Computation without Division

Vector normalization is an important process in several algorithms. It is used in classical physical calculations, mathematical techniques, and machine learning, which has witnessed significant advancements in recent years. Normalization and regularization ensure the stability of solutions and play an important role in algorithm convergence. Normalization typically refers to the division of elements by their norm. Division should not be used in algorithmic implementations because its computational cost is considerably higher than that of multiply–add operations. Based on this, there is a well-known method referred to as the fast inverse square root (FISR) algorithm in floating-point calculations (IEEE754). In deeper-level embedded systems that require fast responses or power efficiency, integer instead of real number arithmetic (floating-point number arithmetic) should be used to increase speed. Conversely, in deeper-level embedded systems that require fast responses or power efficiency, integer arithmetic should be used instead of real number arithmetic (floating-point number arithmetic) to increase speed. Therefore, embedded engineers encounter problems in instances in which they use integer arithmetic for implementation, but real number arithmetic is required to compute vectors and other higher-dimensional algebra. There is no conventional normalization algorithm similar to the FISR algorithm for integer arithmetic; however, the proposed pseudo-normalization achieves vector normalization within a restricted domain using only multiply–add operations and bit shifts. This allows for fast and robust operations, even for low-performance MCUs that do not have power-efficient FPUs. As an example, this study demonstrates the computation of the arctangent (Arctan2 function; atan2(y, x)) with high precision using only integer multiply–add operations. In this study, we proposed a method of vector normalization using only integer arithmetic for embedded systems and confirmed its effectiveness by simulation using Verilog. The research results can contribute to various fields such as signal processing of IMU sensor data, faster artificial intelligence training, and efficient rendering of computer graphics.