Inertial Sensors Research Articles

The correlation between ball velocity with hip-shoulder rotation and separation during pitching process in baseball pitchers

<p>目的：本研究欲探討成棒選手肩髖的旋轉及肩髖分離程度與球速間的相關性，用以尋找提升球速的方法。方法：以 18 位大專公開一級棒球投手作為研究參與者，同時利用慣性感測 (IMU)、二維影像分析系統及測速槍測量研究參與者之投球運動學參數，包括肩與骨盆在各時間點的旋轉角度、肩髖分離的角度、旋轉峰值時機及球速等。以皮爾森積差相關係數分析肩與骨盆的旋轉情況及肩髖分離程度與球速之間是否具有相關性。結果：本研究顯示除了上軀幹在膝蓋最高點 (r =-.295, p =.441)外，其餘分別為上軀幹在前導腳落地時 (r = -.821, p < .001)、上軀幹在球離手時 (r = .765, p = .016)、骨盆在膝蓋最高點時 (r = -.791, p = .011)、骨盆在前導腳落地時 (r = .906, p= .001)、骨盆在球離手時 (r = .951, p < .001)，都與球速呈顯著相關；上軀幹與骨盆的旋轉速度峰值、時機與球速呈顯著相關 (r = .934, p < .001)、(r = .969, p < .001)、(r = .881, p = .002)、(r = -.749, p = .02)；肩髖分離程度與上軀幹和骨盆的旋轉及球速皆呈顯著相關。結論：本研究顯示不僅是上軀幹，骨盆的旋轉與球速也息息相關，因此在投球動作上應注重投球動作的整體性，而非強調單一肢段的發力，且若能同時妥善運用肩髖分離，則能提升運動表現。</p> <p> </p><p>Purpose: This study aims to investigate the correlation between hip-shoulder and separation degree with ball speed in skilled baseball pitchers to identify methods for enhancing pitching velocity. Methods: Eighteen collegiate level-Ⅰbaseball pitchers participated as research subjects. An inertial measurement unit (IMU), a two-dimensional motion analysis system, and a radar gun were employed to measure the kinematic parameters of the pitching motion. These parameters included rotational angles of the shoulder and pelvic at various time points, the degree of hip-shoulder separation, peak rotation timing, and ball velocity. Pearson correlation coefficients between ball velocity, hip shoulder separation, and all trunk and pelvic kinematic variables were computed, and the level of significance was set at α = .05. Results: The results indicate that, except for the upper trunk at the maximum knee height(r = -.295, p = .441), the rest were significantly related with ball velocity: the upper trunk at the landing of the leading foot (r = -.821, p< .001), the upper trunk at the ball off hand (r = .765, p = .016), the pelvic at the maximum knee height (r = -.791, p = .011), the pelvic at the landing of the leading foot (r = .906, p = .001), and the pelvic at the ball off hand (r = .951, p < .001). The peak rotation velocity and the timing of the peak rotation velocity of the upper trunk and pelvic is also significantly related to ball velocity(r = .934, p < .001)、(r = .969, p < .001)、(r = .881, p = .002)、(r = -.749, p = .02). The degree of shoulder-hip separation is significantly correlated with the rotation of the upper trunk and pelvic and ball velocity. Conclusion: Based on the findings of this study, we know the significance of not only the upper trunk but also pelvic rotation with ball velocity. Therefore, a holistic approach to pitching mechanics, emphasizing overall motion rather than isolating individual segments, is crucial. Additionally, effectively utilizing hip-shoulder separation can enhance overall athletic performance in baseball pitching.</p> <p> </p>

Non-cooperative target recognition and relative motion estimation with inertial measurement unit assistance

This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers. A structure integrating an Inertial Measurement Unit (IMU) and a visual camera was presented. The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames, which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition. To solve the problem of target misidentification caused by new star points entering the field of view, a target-tracking link based on IMU prediction was introduced to track the position of the target in the image. Furthermore, a measurement model was constructed using the line-of-sight vector generated from target recognition, and the relative motion state was estimated using a Huber-based non-linear filter. Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.

Feasibility and Validity of Wearable Sensors for Monitoring Temporal Parameters in Manual Wheelchair Propulsion.

Upper extremity pain and injury are among the most common musculoskeletal complications manual wheelchair users face. Assessing the temporal parameters of manual wheelchair propulsion, such as propulsion duration, cadence, push duration, and recovery duration, is essential for providing a deep insight into the mobility, level of activity, energy expenditure, and cumulative exposure to repetitive tasks and thus providing personalized feedback. The purpose of this paper is to investigate the use of inertial measurement units (IMUs) to estimate these temporal parameters by identifying the start and end time of hand contact with the push-rim during each propulsion cycle. We presented a model based on data collected from 23 participants (14 males and 9 females, including 9 experienced manual wheelchair users) to guarantee the reliability and generalizability of our method. The obtained outcomes from our IMU-based model were then compared against an instrumented wheelchair (SMARTWheel) as a reference criterion. The results illustrated that our model was able to accurately detect hand contact and hand release and predict temporal parameters, including the push duration and recovery duration in manual wheelchair users, with the mean error ± standard deviation of 10 ± 60 milliseconds and -20 ± 80 milliseconds, respectively. The findings of this study demonstrate the potential of hand-mounted IMUs as a reliable and objective tool for analyzing temporal parameters in manual wheelchair propulsion. IMUs offer significant strides towards inclusivity and accessibility due to their portability and user-friendliness and can democratize health monitoring of manual wheelchair users by making it accessible to a broader range of users compared to traditional technologies.

UVIO: Adaptive Kalman Filtering UWB-Aided Visual-Inertial SLAM System for Complex Indoor Environments

Precise positioning in an indoor environment is a challenging task because it is difficult to receive a strong and reliable global positioning system (GPS) signal. For existing wireless indoor positioning methods, ultra-wideband (UWB) has become more popular because of its low energy consumption and high interference immunity. Nevertheless, factors such as indoor non-line-of-sight (NLOS) obstructions can still lead to large errors or fluctuations in the measurement data. In this paper, we propose a fusion method based on ultra-wideband (UWB), inertial measurement unit (IMU), and visual simultaneous localization and mapping (V-SLAM) to achieve high accuracy and robustness in tracking a mobile robot in a complex indoor environment. Specifically, we first focus on the identification and correction between line-of-sight (LOS) and non-line-of-sight (NLOS) UWB signals. The distance evaluated from UWB is first processed by an adaptive Kalman filter with IMU signals for pose estimation, where a new noise covariance matrix using the received signal strength indicator (RSSI) and estimation of precision (EOP) is proposed to reduce the effect due to NLOS. After that, the corrected UWB estimation is tightly integrated with IMU and visual SLAM through factor graph optimization (FGO) to further refine the pose estimation. The experimental results show that, compared with single or dual positioning systems, the proposed fusion method provides significant improvements in positioning accuracy in a complex indoor environment.

Differences in the stride time and lower limb joint angles and their variability during distance running between treadmill and over-ground: a crossover study.

Treadmills have been used in laboratories to assess various measures related to walking and running. However, there has been some skepticism regarding their reliability as a representation of outdoor running. While marathon running has gained popularity as a form of physical activity, there have been few studies examining stride-to-stride variability after distance running, especially in relation to the duration and surface of running. This study compared stride time and lower limb joint angles during distance treadmill running and running over-ground. The hypothesis was that stride-to-stride variability would be influenced by running duration and surface, with greater variability observed during outdoor running. Eleven runners participated in the study, running on a treadmill and over-ground for 31 minutes at their preferred speed. Inertial measurement units were used to measure stride time, total range of motion, and joint angles of the hip, knee, and ankle in different phases of the gait cycle in the sagittal plane movements. Mean and coefficient of variation of each parameter were compared between the initial and final 5 minutes of running on the treadmill and over-ground. There were no significant differences in stride time or its variability based on running duration or surface. However, mean and variability of certain lower limb joint angles were higher during outdoor running, supporting the hypothesis. Variability was higher in the initial duration of running as compared to final phase of running. These findings suggest that treadmill may not fully reflect the dynamics of running over-ground. It is important to consider variability in gait analysis and research, as well as the potential impact on training and clinical practice.

Are Anthropometric Measures, Range of Motion, or Movement Control Tests Associated with Lumbopelvic Flexion during Barbell Back Squats?

Resistance training with the barbell back squat (BBS) exercise is practiced in sports, recreation, and rehabilitation. Although extensively debated, it is commonly believed and recommended that maintaining a neutral lumbopelvic alignment during BBS is an important technical aspect that might reduce the risk of injury. There is limited knowledge of how objectively measurable factors affect the extent to which the lumbopelvic region moves into flexion during a BBS. The aim of the study was to investigate the association among anthropometric measurements, range of motion in the hips and ankle joints, lumbopelvic movement control tests, and flexion of the lumbopelvic region during execution of the BBS. Observational, cross sectional. Eighteen experienced powerlifters and Olympic weightlifters were included and measurements of lumbopelvic movements were collected with inertial measurement units during BBS performed at 70 % of 1RM. Examination of anthropometric properties, range of motion in the hip and ankle joints, and lumbopelvic movement control tests were collected as independent variables. Linear regression analysis was used to investigate which independent variables were associated with lumbopelvic flexion during a BBS. The linear regression showed that a higher range of motion in ankle dorsiflexion could statistically significantly explain an increased amplitude of lumbopelvic flexion during the BBS. Anthropometrics, range of motion of the hips, and performance in lumbopelvic movement control tests did not show any statistically significant associations. The results suggest that strength and conditioning professionals and clinicians who instruct and assess lifting technique in the BBS and/or use the BBS to assess performance or as an intervention should recognize that a higher range of motion in the ankle joints might affect lumbopelvic flexion during the BBS. In practice, the value of an individual assessment of lifting technique focusing on the goal of the movement should be emphasized. 3.

Motion Trajectory Perception of an Earthworm-Like Robot Using an Inertial Measurement Unit and Autonomous ZUPT

Motion Trajectory Perception of an Earthworm-Like Robot Using an Inertial Measurement Unit and Autonomous ZUPT

Learning Position From Vehicle Vibration Using an Inertial Measurement Unit

Learning Position From Vehicle Vibration Using an Inertial Measurement Unit

Accuracy and reliability improved GNSS partial ambiguity resolution assisted by a MEMS inertial measurement unit

Traditional partial ambiguity resolution (PAR) methods based on integer least-squares (ILS-PAR) assume that the ambiguity terms are unbiased integers, which is seldomly true in challenging environments of the Global Navigation Satellite System (GNSS). The bias will lead to unreliable positioning solutions. This study proposes a reliable PAR method based on the tightly coupling of GNSS and inertial navigation system (INS) and the solution separation with external solution (SSE) statistical testing algorithm. First, the inertial measurement unit (IMU) mechanization results are compensated using the latest fixed real-time kinematic (RTK) solutions, thus high-accuracy INS predictions are obtainable at the current GNSS epoch. Second, the SSE method based on double-differenced (DD) carrier-phase measurements are applied to select and validate the ambiguity subset and perform the GNSS/INS integration. A dynamic vehicular experiment with a low-cost microelectromechanical systems (MEMS) device has been conducted and the results indicate that the proposed method can ensure good performance of both accuracy and reliability.

Using accelerometers to identify a high risk of catastrophic musculoskeletal injury in three racing Thoroughbreds.

To describe the process whereby the screening of racing Thoroughbreds with accelerometer-based inertial measurement unit (IMU) sensors followed by clinical evaluation and advanced imaging identified potentially catastrophic musculoskeletal injuries in 3 horses. 3 Thoroughbred racehorses. All cases demonstrated an abnormal stride pattern either during racing (cases 1 and 2) or while breezing (case 3) and were identified as being at very high risk of catastrophic musculoskeletal injury by an algorithm derived from IMU sensor files from > 20,000 horses' race starts. Veterinary examination and 18F-sodium fluoride (18F-NaF) positron emission tomography were performed within 10 days of the respective race or breeze in each of the cases. The intensity and location of the 18F-NaF uptake in the condyles of the third metacarpal bone in cases 1 and 2 identified them as at potential increased risk of condylar fracture. The pattern and intensity of the 18F-NaF uptake in case 3 indicated that the third carpal bone was likely responsible for the horse's lameness, with an impending slab fracture subsequently identified on radiographs. Following periods of convalescence, cases 1 and 2 returned to racing and were identified by the sensor system as no longer being at high risk of catastrophic musculoskeletal injury. Case 3 returned to training but has yet to return to racing. When worn by Thoroughbreds while racing or breezing, these IMU sensors can identify horses at high risk of catastrophic musculoskeletal injury, allowing for veterinary intervention and the potential avoidance of such injuries.

Distinguishing (pre)frail from non-frail older adults based on walking pattern: A Scoping Review on gait parameters derived from inertial sensors

Distinguishing (pre)frail from non-frail older adults based on walking pattern: A Scoping Review on gait parameters derived from inertial sensors

Perspective on Quantum Sensors from Basic Research to Commercial Applications

Quantum sensors represent a new generation of sensors with improved precision, accuracy, stability, and robustness to environmental effects compared to their classical predecessors. After decades of laboratory development, several types of quantum sensors are now commercially available or are part-way through the commercialization process. This paper provides a brief description of the operation of a selection of quantum sensors that employ the principles of atom–light interactions and discusses progress toward packaging those sensors into products. This paper covers quantum inertial and gravitational sensors, including gyroscopes, accelerometers, gravimeters, and gravity gradiometers that employ atom interferometry, nuclear magnetic resonance gyroscopes, atomic and spin-defect magnetometers, and Rydberg electric field sensors.

AstroSLAM: Autonomous monocular navigation in the vicinity of a celestial small body—Theory and experiments

We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown celestial target small body. AstroSLAM is predicated on the formulation of the SLAM problem as an incrementally growing factor graph, facilitated by the use of the GTSAM library and the iSAM2 engine. By combining sensor fusion with orbital motion priors, we achieve improved performance over a baseline SLAM solution and outperform state-of-the-art methods predicated on pre-integrated inertial measurement unit factors. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics—RelDyn—factor, which links the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body. We demonstrate AstroSLAM’s performance and compare against the state-of-the-art methods using both real legacy mission imagery and trajectory data courtesy of NASA’s Planetary Data System, as well as real in-lab imagery data produced on a 3 degree-of-freedom spacecraft simulator test-bed.

240 Does fall prevention also have to hinder physical activity?

Abstract Purpose Physical activity is effective for preventing falls and improves physical and mental health, increase function and quality of life. Regarding people at high risk of falling, such as residents at nursing homes, global guidelines recommend individually adapted exercise programs to prevent falls. These guidelines are not followed. We aimed to map out physical activity among people living at a nursing home. To our knowledge, this is the first study measuring physical activity 24 hours per day among residents at a nursing home. Method In this pilot study, persons who lived at one nursing home and could walk without a walking aid wore an Inertial Measurement Unit for 24 hours per day for 3 months. Main outcome measure was steps per day. For each participant, the mean steps per day during the period was calculated and for the total group, the median value of the mean steps per day was calculated. Results The participants (n = 5), three women and 2 men, 72 to 98 years old, walked in a median of 65 steps per day (10-356). Conclusion In Sweden, instead of following guidelines, technical solutions that monitor if a person tries to move are introduced at nursing homes, often in the form of web cameras. This means that falls are prevented by hindering physical activity and making sure the residents do not move at all. Our study shows that people living in nursing homes can have a very low level of physical activity. We call on all responsible organizations to initiate activities and efforts that enable rather than hinder physical activity. For example, use technology that promotes physical activity, build gyms located in nursing homes, use Virtual Reality to make the bike ride seem to take place outside in a beautiful landscape, and employ physiotherapists who can do balance-training exercises on an everyday basis.

Asymmetric Dual Redundant Inertial Sensors: A Method for Fault Detection and Diagnosis

Asymmetric Dual Redundant Inertial Sensors: A Method for Fault Detection and Diagnosis

RLI-SLAM: Fast Robust Ranging-LiDAR-Inertial Tightly-Coupled Localization and Mapping.

Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment.

Sensor placement determination for a wearable device in dual-arm manipulation tasks

To enhance the quality of life for individuals in various sectors, such as industry, healthcare, and everyday activities, the development of a highly efficient and cost-effective wearable device is essential. This paper presents a feasibility study for a general estimation methodology for human activity assessment using a single sensor placed on the back. The methodology considers the minimal variability in human body movements when carrying different weights. Our approach involves optimizing the sensor's position through a simple pre-processing technique. This technique utilizes virtual sensor position and orientation data derived from a virtual mannequin based on real inertial measurement unit (IMU) measurements obtained during two-arm manipulation tasks in a warehouse setting. The primary contribution is the optimization of a single, non-invasive sensor placement on the back to accurately assess activities involving two-hand manipulation with low variability in movement. We identified 54 different activity classes with an accuracy of 91.77% using a hybrid two-dimensional Convolutional Neural Network (CNN) combined with a Bidirectional Long Short-Term Memory (BiLSTM) network (2D CNN-BiLSTM) model. This model does not require additional sensors that measure other physical quantities, such as electromyography activity. Identifying repetitive tasks involving significant bending, stretching, and twisting, which pose risks of musculoskeletal disorders and back pain, offers a solution for designing wearable devices. The portability and autonomy of these devices are crucial, and current sensor technology meets these needs with low size, cost, and consumption. Wearable medical devices can thus be effectively used for self-monitoring health, preventive medicine, and rehabilitation.

Research on Adaptive Closed-Loop Control of Microelectromechanical System Gyroscopes under Temperature Disturbance.

Microelectromechanical System (MEMS) gyroscopes are inertial sensors used to measure angular velocity. Due to their small size and low power consumption, MEMS devices are widely employed in consumer electronics and the automotive industry. MEMS gyroscopes typically use closed-loop control systems, which often use PID controllers with fixed parameters. These classical PID controllers require a trade-off between overshoot and rise time. However, temperature variations can cause changes in the gyroscope's parameters, which in turn affect the PID controller's performance. To address this issue, this paper proposes an adaptive PID controller that adjusts its parameters in response to temperature-induced changes in the gyroscope's characteristics, based on the error value. A closed-loop control system using the adaptive PID was developed in Simulink and compared with a classical PID controller. The results demonstrate that the adaptive PID controller effectively tracked the changes in the gyroscope's parameters, reducing overshoot by 96% while maintaining a similar rise time. During gyroscope startup, the adaptive PID controller achieves faster stabilization with a 0.036 s settling time, outperforming the 0.06 s of the conventional PID controller.

A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment.

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management.