Inertial Measurement Unit Sensor Research Articles

IMU positioning affects range of motion measurement during squat motion analysis

Inertial Measurement Units (IMUs) provides embedded and accessible (financially and technically speaking) motion analysis for sports or clinical applications (rehabilitation, therapy…). Despite being advertised for it ease of use, the very nature of IMU sensor makes it prone to errors which are usually corrected through calibration processes thus adding extra complexity for the users. The main goal of this study is to estimate the effect of sensor positioning on the thigh for a simple assessment of squat motion range of motion (ROM) as could be done in a pragmatic clinical approach (i.e., without prior calibration). Kinematics, squat counts and timing of three IMU sensors along the thigh were recorded during squat motion and compared to an optoelectronic reference system. Results showed concordance coefficients of the IMU system over 0.944 without the need for calibration with a preference for placement on the distal part of the segment regarding kinematics data.

ARD‐VO: Agricultural robot data set of vineyards and olive groves

AbstractThe availability of real‐world data in agricultural applications is of paramount importance to develop robust and effective robotic‐based solutions for farming operations. In this application context, however, very few data sets are available to the community and for some important crops, such as grapes and olives, they are almost absent. Therefore, the aim of this paper is to introduce and release ARD‐VO, a data set for agricultural robotics applications focused on vineyards and olive cultivations. Its main purpose is to provide the researchers with a real‐world extensive set of data to support the development of solutions and algorithms for precision farming technologies in the aforementioned crops. ARD‐VO has been collected with an unmanned ground vehicle (UGV) equipped with different heterogeneous sensors that capture information essential for robot localization and plant monitoring tasks. It is composed of sequences gathered in 11 experimental sessions between August and October 2021, navigating the UGV for several kilometers in four cultivation fields in Umbria, a central region of Italy. In addition, to highlight the utility of ARD‐VO, two application case studies are presented. In the first one, the data set is used to compare the performance of simultaneous localization and mapping and odometry estimation methods using vision systems, light detection and ranging, and inertial measurement unit sensors. The second one shows how the multispectral images included in ARD‐VO can be used to compute Normalized Difference Vegetation Index maps, which are crucial to monitor the crops and build prescription maps.

Are 4D Motion Sensors Valid and Reliable for Studying Baseball Pitching?

Background: Baseball pitching injuries are on the rise. Inertial measurement units (IMUs) provide immediate feedback to players and coaches, allowing for collection outside of the traditional laboratory setting with real-world application. The 4D Motion system provides kinematics throughout the pitching motion and may be beneficial for individualized programs in the throwing athlete. A systematic analysis of these sensors has not been completed. Purpose: To evaluate the validity of the 4D Motion IMU system for analyzing the baseball pitching motion compared with marker-based motion capture, and evaluate the internal reliability and consistency of the device. Study Design: Controlled laboratory study. Methods: Ten high school pitchers participated in this study (10 male; 9 right-hand dominant; mean age, 16.6 ± 1.3 years; mean body mass index, 24.1 ± 3.9). Participants were simultaneously outfitted with six 4D Motion IMU sensors and retroreflective markers. The pitchers threw fastballs at maximum effort off a mound at the standard height and distance. A comparison was made between the IMUs and corresponding motion capture values for shoulder external rotation, elbow flexion, chest extension, pelvis and chest rotation velocity, and rotation acceleration. Results: Significant differences were found for 5 of 7 metrics analyzed. The IMU overreported most metrics, except for elbow flexion and pelvis rotation angular acceleration, where both positive and negative errors were observed. The root mean square error and percentage errors indicated smaller discrepancies for chest extension (4°± 5°) and pelvis (38 ± 19 deg/s) and chest (96 ± 42 deg/s) rotation velocity, with elbow flexion having the largest variance (21°± 9°). Conclusion: The values of the 4D Motion IMU system should not be considered equivalent when compared with marker-based motion capture studies. The system lacked internal consistency and reliability, with angular velocities being the most consistent. Caution should be used when using the metrics provided by an IMU-based system for individualized monitoring. Clinical Relevance: If found valid and reliable, IMUs could be used for longitudinal workload monitoring, individualized throwing and rehabilitation programs, and ultimately injury prevention. This study demonstrates that the data obtained from a 4D Motion system using Gen 3 sensors are not equivalent to the data obtained from a marker-based motion capture system.

Shape Reconstruction for Continuum Robot Based on Pythagorean Hodograph–Bézier Curve With IMU and Vision Sensors

The shape information is the key to realize real-time, accurate, and effective control of continuum robots. Traditional image processing method and start-to-end-point fitting method are combined. Inertial measurement unit (IMU) and camera are used to capture the end position and attitude information of continuum robot and do not rely on the acquisition of the global image to solve the problem of limited vision in a traditional image processing method. In addition, a new start-to-end-point fitting method is proposed, which is the Pythagorean hodograph—Bézier (PH-B) fitting method. The new method simplifies the solving steps of control points of fitting curve. The control points can be obtained only through the geometric relationship and do not need to rely on an optimization algorithm for iterative solution. This method has good shape fitting accuracy and applicability in the curvature range from 0° to 90° through the simulation analysis. A variety of experiments are designed to verify the results. The experimental results show that the maximum shape reconstruction error by the proposed method is about 2 mm. This proves the applicability of the PH-B method in the shape reconstruction of continuum robots.

Evaluation of In-Cloth versus On-Skin Sensors for Measuring Trunk and Upper Arm Postures and Movements.

Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the accuracy of sensors placed in the workwear systems for research and practice purposes. This study aimed to compare in-cloth and on-skin sensors for assessing upper arms and trunk postures and movements, with the on-skin sensors as the reference. Five simulated work tasks were performed by twelve subjects (seven women and five men). Results showed that the mean (±SD) absolute cloth-skin sensor differences of the median dominant arm elevation angle ranged between 1.2° (±1.4) and 4.1° (±3.5). For the median trunk flexion angle, the mean absolute cloth-skin sensor differences ranged between 2.7° (±1.7) and 3.7° (±3.9). Larger errors were observed for the 90th and 95th percentiles of inclination angles and inclination velocities. The performance depended on the tasks and was affected by individual factors, such as the fit of the clothes. Potential error compensation algorithms need to be investigated in future work. In conclusion, in-cloth sensors showed acceptable accuracy for measuring upper arm and trunk postures and movements on a group level. Considering the balance of accuracy, comfort, and usability, such a system can potentially be a practical tool for ergonomic assessment for researchers and practitioners.

Data Glove with Integrated Polyethylene‐Carbon Composite‐Based Strain Sensor for Virtual Reality Applications

AbstractA data glove enables fine motion control in virtual reality (VR). This work presents a cost‐effective data glove made of a commercial nylon‐based glove with integrated polyethylene‐carbon composites (Velostat). The resistance of the Velostat varies when a finger is bent. This piezoresistive behavior was explored by designing the Velostat as the strain sensor. The strain sensor was characterized for its flexure sensing by connecting it to a data acquisition circuit. The circuit is designed to process the output of the joint angles and feed it to the computer. A linear resistance output was measured with a sensitivity/gauge factor of 1.45 % per degree with a response time of 0.0158 seconds when the strain sensor bends from 0° to 30°. To control the 3D virtual hand's movement, the data glove was coupled with two inertial measurement unit sensors at the forearm and upper arm to identify its coordinates. The fabricated data glove successfully performs a proof‐of‐concept by picking‐and‐placing multiple objects in a VR environment.

Fine-Grained and Real-Time Gesture Recognition by Using IMU Sensors

Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. Also if we change the person to perform the same gesture, the algorithms fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in realtime and also recognize complicated gestures. It utilizes the accelerometer information and proposes comprehensive localization algorithms for each deployed sensor attached on the human body. Then, it takes the correlation and limitation among body parts into account to recognize the gesture. Our experiments results show that, the successful recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach 0.06m.

Attention‐based sensor fusion for emotion recognition from human motion by combining convolutional neural network and weighted kernel support vector machine and using inertial measurement unit signals

AbstractThe remarkable development of human–computer interactions has created an urgent need for machines to be able to recognise human emotions. Human motions play a key role in emphasising and conveying emotions to meet the complexity of daily application scenarios, such as medical rehabilitation and social education. Therefore, this paper aims to explore hidden emotional states from human motions. Accordingly, we proposed a novel approach for emotion recognition using multiple inertial measurement unit (IMU) sensors worn on different body parts. First, the mapping relationship between emotion and human motion was established through fuzzy comprehensive evaluation, and data were collected for six emotional states: sleepy, bored, excited, tense, angry, and distressed. Second, the preprocessed data were used as input in a lightweight convolutional neural network to extract discriminative features. Third, an attention‐based sensor fusion module was developed to obtain the importance scores of each IMU sensor for generating a fused feature representation. In the recognition phase, we constructed a weighted kernel support vector machine (SVM) model with an auxiliary fuzzy function to improve the weight calculation method of kernel functions in a multiple kernel SVM. Finally, the results obtained are compared with those of similar state‐of‐the‐art studies, the proposed method showed a higher accuracy (99.02%) for the six emotional states mentioned above. These findings may promote the development of social robots with non‐verbal emotional communication capabilities.

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter.

High-precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch-Tung-Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF-RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF-RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF-RTS algorithm is more robust and provides more accurate localization.

Investigation of Body Locations for Cardiac and Respiratory Monitoring With Skin-Interfaced Inertial Measurement Unit Sensors

Investigation of Body Locations for Cardiac and Respiratory Monitoring With Skin-Interfaced Inertial Measurement Unit Sensors

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait.

Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the IMU-2-segment (I2S) pairing work properly only for a limited range of gait speeds or require a similar sensor configuration. Our goal was to propose an algorithm that works over a wide range of gait speeds with different sensor configurations while being robust to footwear type and generalizable to pathologic gait patterns. Eight IMU sensors were attached to both feet, shanks, thighs, sacrum, and trunk, and 12 healthy subjects (training dataset) and 22 patients (test dataset) with medial compartment knee osteoarthritis walked at different speeds with/without insole. First, the mean stride time was estimated and IMU signals were scaled. Using a decision tree, the body segment was recognized, followed by the side of the lower limb sensor. The accuracy and precision of the whole algorithm were 99.7% and 99.0%, respectively, for gait speeds ranging from 0.5 to 2.2 m/s. In conclusion, the proposed algorithm was robust to gait speed and footwear type and can be widely used for different sensor configurations.

3D Deformation Capture via a Configurable Self-Sensing IMU Sensor Network

Motion capture technologies reconstruct human movements and have wide-ranging applications. Mainstream research on motion capture can be divided into vision-based methods and inertial measurement unit (IMU)-based methods. The vision-based methods capture complex 3D geometrical deformations with high accuracy, but they rely on expensive optical equipment and suffer from the line-of-sight occlusion problem. IMU-based methods are lightweight but hard to capture fine-grained 3D deformations. In this work, we present a configurable self-sensing IMU sensor network to bridge the gap between the vision-based and IMU-based methods. To achieve this, we propose a novel kinematic chain model based on the four-bar linkage to describe the minimum deformation process of 3D deformations. We also introduce three geometric priors, obtained from the initial shape, material properties and motion features, to assist the kinematic chain model in reconstructing deformations and overcome the data sparsity problem. Additionally, to further enhance the accuracy of deformation capture, we propose a fabrication method to customize 3D sensor networks for different objects. We introduce origami-inspired thinking to achieve the customization process, which constructs 3D sensor networks through a 3D-2D-3D digital-physical transition. The experimental results demonstrate that our method achieves comparable performance with state-of-the-art methods.

Convolutional Neural Network-Based Low-Powered Wearable Smart Device for Gait Abnormality Detection

Gait analysis is a powerful technique that detects and identifies foot disorders and walking irregularities, including pronation, supination, and unstable foot movements. Early detection can help prevent injuries, correct walking posture, and avoid the need for surgery or cortisone injections. Traditional gait analysis methods are expensive and only available in laboratory settings, but new wearable technologies such as AI and IoT-based devices, smart shoes, and insoles have the potential to make gait analysis more accessible, especially for people who cannot easily access specialized facilities. This research proposes a novel approach using IoT, edge computing, and tiny machine learning (TinyML) to predict gait patterns using a microcontroller-based device worn on a shoe. The device uses an inertial measurement unit (IMU) sensor and a TinyML model on an advanced RISC machines (ARM) chip to classify and predict abnormal gait patterns, providing a more accessible, cost-effective, and portable way to conduct gait analysis.

StemRad MD, An Exoskeleton-Based Radiation Protection System, Reduces Ergonomic Posture Risk Based on a Prospective Observational Study

Objective: Poor ergonomic posture during interventional procedures might lead to increased physical discomfort and work-related musculoskeletal disorders. Adjunctive equipment such as lead aprons (LAs) has been shown to increase ergonomic posture risk (EPR). The objective of this study was to evaluate the effectiveness of StemRad MD (StemRad Ltd., Tel Aviv, Israel), a weightless exoskeleton-based radiation protective ensemble, in reducing EPR on the operator using wearable inertial measurement unit (IMU) sensors. Methods: A prospective, observational study was conducted at an academic hospital. Inertial measurement unit sensors were affixed to the upper back of 9 interventionalists to assess ergonomic risk posture during endovascular procedures while wearing a traditional LA or the StemRad MD radiation protection system. Total fluoroscopy time, procedure type, and ergonomic risk postures were recorded and analyzed. Results: Twenty-one cases were performed with StemRad MD and 30 with LAs. Mean procedure time for the StemRad MD procedures was 48.4±23.3 minutes (range: 24–106 min), and for LA procedures, it was 34.66±25.83 minutes (range: 6–100 min) (p=.060). The operators assumed low-risk ergonomic positions in 96.1% of StemRad MD cases and in 62.9% of LA cases (p=.001), and high-risk ergonomic positions in 0% and 6.2%, respectively (p=.80). Mean EPR score for StemRad MD was 1.16, and for the LA, it was 1.49 (p=.001). Conclusions: StemRad MD significantly reduces the EPR to the torso compared with a LA-based radiation protection system. Clinical Impact Poor ergonomic posture during interventional procedures might leas to work-related musculoskeletal disorders for healthcare workers. StemRad MD, a weightless, exoskeleton-based radiation protection system was shown to significantly reduce ergonomic posture risk to the torso compared to conventional lead aprons. This might lead to reduced physical discomfort for procedure-based specialists.

Monitoring Framework for Riding Safety of Delivery Scooters using 100 Naturalistic Riding Study (NRS) Data

A traffic safety issue of two-wheeled delivery scooters is emerging because of the rapid increase in demand for food delivery services. In particular, the strict restriction of delivery time leads to aggressive and dangerous riding behavior that causes a high risk of crash occurrence. Systematic traffic safety management is required to effectively prevent crashes of delivery scooters. The objective of this study is to develop a monitoring framework for riding safety that informs when, where, and how serious safety problems occur. High-resolution riding behavior data obtained by an inertial measurement unit sensor installed on delivery scooters, as part of the Korean 100 naturalistic riding study (K-100NRS), were used for developing the methodology. The proposed monitoring framework consists of two components: an unsafe riding event detection algorithm and a method to identify the spatial and temporal identification of riding risks. The ratio of frequency of unsafe events to total riding time for each rider is defined as a monitoring index, which is referred to as the riding risk index in this study. Approximately 95% detection accuracy was achievable by the developed detection algorithm. In addition, the level of riding safety for each rider was evaluated based on the proposed methodology. As an application, a visualization of detected unsafe events was presented for the purpose of riding safety monitoring.

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

The lack of intuitive and active human-robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study's detailed analysis can improve the usability of the assistive/rehabilitation robots.

A mixed reality system combining augmented reality, 3D bio-printed physical environments and inertial measurement unit sensors for task planning

Successful surgical operations are characterized by preplanning routines to be executed during actual surgical operations. To achieve this, surgeons rely on the experience acquired from the use of cadavers, enabling technologies like virtual reality (VR) and clinical years of practice. However, cadavers, having no dynamism and realism as they lack blood, can exhibit limited tissue degradation and shrinkage, while current VR systems do not provide amplified haptic feedback. This can impact surgical training increasing the likelihood of medical errors. This work proposes a novel Mixed Reality Combination System (MRCS) that pairs Augmented Reality (AR) technology and an inertial measurement unit (IMU) sensor with 3D printed, collagen-based specimens that can enhance task performance like planning and execution. To achieve this, the MRCS charts out a path prior to a user task execution based on a visual, physical, and dynamic environment on the state of a target object by utilizing surgeon-created virtual imagery that, when projected onto a 3D printed biospecimen as AR, reacts visually to user input on its actual physical state. This allows a real-time user reaction of the MRCS by displaying new multi-sensory virtual states of an object prior to performing on the actual physical state of that same object enabling effective task planning. Tracked user actions using an integrated 9-Degree of Freedom IMU demonstrate task execution This demonstrates that a user, with limited knowledge of specific anatomy, can, under guidance, execute a preplanned task. In addition, to surgical planning, this system can be generally applied in areas such as construction, maintenance, and education.

An Indoor Autonomous Inspection and Firefighting Robot Based on SLAM and Flame Image Recognition

Indoor fire accidents have become increasingly common in recent years. More and more firefighting robots have been designed to solve the problem of fires. However, the indoor environment is very complex, with high temperatures, thick smoke, more turns, and various burning substances. In this study, a firefighting robot with autonomous inspection and automatic fire-extinguishing functions intended for use in unknown indoor environments was designed. Considering water’s poor efficiency and its inability to extinguish some combustion materials, other fire extinguishers were applied to design the extinguishing system. The robot can install four different extinguishers as required and select the appropriate fire extinguisher to spray it automatically. Based on the Cartographer SLAM (simultaneous localization and mapping) theory, a robot map-building system was built using Lidar scanners, IMU (inertial measurement unit) sensors, encoders, and other sensors. The accurate identification and location of the fire source were achieved using an infrared thermal imager and the YOLOv4 deep learning algorithm. Finally, the performance of the firefighting robot was evaluated by creating a simulated-fire experimental environment. In an autonomous inspection process of the on-fire environment, the firefighting robot could identify the flame in real-time, trigger the fire-extinguishing system to carry out automatic fire extinguishing, and contain the fire in its embryonic stage.

Cooperative Localization of Firefighters Based on Relative Ranging Constraints of UWB and Autonomous Navigation

There are many demands for the cooperative localization (CL) of multiple people, such as firefighter rescue. The classical foot-mounted inertial navigation based on zero velocity update (ZUPT) suffers from accumulating error due to the low-cost inertial sensor, and the pre-placed anchors in the ultra-wideband (UWB) system limit the application in an unknown environment. In this study, a group of sensors including the inertial measurement unit (IMU), magnetometer, barometer, and UWB sensor is used. Through the different characteristics of sensors and the position relationship between people, a cooperative localization system using an extended Kalman filter for three-dimensional firefighter tracking is proposed. Ranging information between firefighters from UWB is utilized, and couplings introduced by relative measurement are estimated. Two experiments are designed to verify the proposed algorithm in building and forest environments. Compared with the results of single-person inertial navigation, the average positioning precision of the algorithm in the building and forest is, respectively, improved by 38.93% and 79.01%. This approach successfully suppresses the divergence of positioning errors, and fixed UWB anchors are not needed.

Motion Recognition Method for Construction Workers Using Selective Depth Inspection and Optimal Inertial Measurement Unit Sensors

The construction industry holds the worst safety record compared to other industrial sectors, and approximately 88% of accidents result in worker injury. Meanwhile, after the development and wide application of deep learning in recent years, image processing has greatly improved the accuracy of human motion detection. However, owing to equipment limitations, it is difficult to effectively improve depth-related problems. Wearable devices have also become popular recently, but because construction workers generally work outdoors, the variable environment makes the application of wearable devices more difficult. Therefore, reducing the burden on workers while stabilizing the detection accuracy is also an issue that needs to be considered. In this paper, an integrated sensor fusion method is proposed for the hazard prevention of construction workers. First, a new approach, called selective depth inspection (SDI), was proposed. This approach adds preprocessing and imaging assistance to the ordinary depth map optimization, thereby significantly improving the calculation efficiency and accuracy. Second, a multi-sensor-based motion recognition system for construction sites was proposed, which combines different kinds of signals to analyze and correct the movement of workers on the site, to improve the detection accuracy and efficiency of the specific body motions at construction sites.