Set Of Inertial Sensors Research Articles

Rider Skill Affects Time and Frequency Domain Postural Variables When Performing Shoulder-in

In equestrian sports the novice rider learns first to follow the movements of the horse's back and then how to influence the horse's performance. One of the rider's challenges is to overcome inherent horse and/or rider asymmetry patterns when riding in straight lines, mirroring the movements on the left, and right sides when turning. This study compares the performance of novice and advanced riders when riding in sitting trot on straight lines and when riding shoulder-in to the left and right sides. Eight novice and eight advanced horse-rider combinations performed sitting trot in a straight line, shoulder-in left and shoulder-in right while wearing a full body set of inertial sensors. An experienced dressage judge indicated when the movements were being performed correctly and assigned scores on a scale of 0-10 for the quality of performance. Kinematic data from the inertial sensors were analyzed in time and frequency domain. Comparisons were made between trotting on the straight, shoulder-in left, and shoulder-in right. Advanced riders received higher dressage scores on all three movements, but significantly (P < .05) lower scores were found for shoulder-in right across the two groups. When riding shoulder-in, advanced riders had greater hip extension (advanced = -5.8 ± 17.7; novice = 7.8 ± 8.9 degrees) and external rotation (advanced = -32.4 ± 15.5; novice = -10.8 ± 13.2 degrees) in the outside leg compared with novices (P < .05), which reflects an important cue in achieving the required body rotation in the horse. Lower scores for shoulder-in right may be linked to significant (P < .05) changes in harmonics of trunk to pelvis rotation.

Ergonomic Assessment Methods Enhanced by IoT and Simulation Tools

AbstractThe advent of Industry 4.0 (I4.0) principles and technologies has been a great boost for manufacturing companies, constantly focused on the production processes optimization in order to reach the double objective of improving productivity and minimizing related costs. Today, the use of I4.0 technologies seems to be more accessible for a large number of companies, allowing to perform a more detailed analysis on their processes by means of experimental data coming directly from the system. In this context, ergonomics, which has been underestimated until 30 years ago, is assuming more and more importance. This paper aims at proposing a methodology focused on an ergonomic analysis carried out by implementing data collected from the field; in particular, a set of inertial sensors integrated in the worker suit detects the body posture. Then, thanks to the Industrial Internet of Things (IIoT), data are transferred to a simulation software in order to quickly evaluate the current ergonomic effort. In this way, it is possible to perform a real‐time evaluation on the workstation design and consider a possible re‐design with the aim of improving ergonomics.

Youth With Concussion Have Less Adaptable Gait Patterns Than Their Uninjured Peers: Implications for Concussion Management.

To compare cross-recurrence quantification analysis measurements obtained during gait between adolescents who sustained a diagnosed concussion within 14 days of assessment and healthy adolescents. Cross-sectional study. Youth athletes with concussion (n = 43; mean ± SD age, 14.4 ± 2.3 years; 56% female; tested median, 7 days post concussion) and healthy controls (n = 38; age, 14.9 ± 2.0 years; 55% female) completed a single-task and dual-task gait protocol while wearing a set of inertial sensors. We used cross-recurrence quantification analysis techniques to quantify the similarity between accelerations obtained from the sensor on the dorsum of each foot. Four outcome variables were compared between groups: percent determinism, average diagonal-line length, laminarity, and trapping time. Athletes with concussion had significantly higher percent determinism, laminarity, and trapping time than the control group in single-task and dual-task conditions (P<.05). Gait patterns, when simultaneously completing a secondary cognitive task (dual task), were no different from gait patterns under a single-task condition. Higher percent determinism, laminarity, and trapping time among athletes with concussion suggest that concussion may be associated with a more stuck and predictable gait pattern. These altered movement patterns may be one reason for underlying slower gait speeds that have been observed following concussion. J Orthop Sports Phys Ther 2020;50(8):438-446. Epub 22 May 2020. doi:10.2519/jospt.2020.9133.

Prediction of energy expenditure during activities of daily living by a wearable set of inertial sensors

Physical inactivity is responsible for 7–10% of all premature deaths worldwide. Thus, valid, reliable and unobtrusive methods for monitoring activities of daily living (ADL) to predict total energy expenditure (TEE) is desired. Multiple methods exist to quantify TEE, but microelectromechanical systems (MEMSs) are the only method, which has shown promising results and are applicable for long-term monitoring in the field. However, no perfect method exists for predicting TEE on a daily basis. The present study evaluates TEE estimation based on a MEMS (Xsens Link system) taking gender and heart rate into account. Fifteen individuals performed seven ADL wearing the Xsens Link system, a heart rate belt and an oxygen mask. Multiple linear regression models were established for sedentary and dynamic activities and evaluated by leave-one-out cross-validation and compared with indirect calorimetry. The linear regression model showed better prediction for dynamic activities (adjusted R2 0.95±0.16) compared to sedentary activities (adjusted R2 0.61±0.19). The root-mean-square error for the TEE estimation ranged between 0.02 and 0.08 kJ/min/kg for the sedentary and dynamic models, respectively. The study showed a viable approach to predict TEE in ADL compared to previously published results. Further studies are warranted to reduce the number of sensors in the estimation of TEE.

A control system for preventing cavitation of centrifugal pumps

Cavitation is a well-known phenomenon that may occur, among other turbo-machines, in centrifugal pumps and can result in severe damage of both the pump and the whole hydraulic system. There are situations in which, in principle, the cavitation could be avoided by detecting the condition of incipient cavitation, and changing slightly the working point of the whole system in order to move away from that condition. In the present paper two simple closed-loop control strategies are implemented, acting on the pump’s rotational speed and fed by the measurements of a set of inertial sensors. In particular, the research is focused on a centrifugal pump normally employed in hydraulic systems. The pump operates in a dedicated test rig, where cavitation can be induced by acting on a reservoir’s pressure. Three accelerometers are installed in the pump body along three orthogonal axes. An extensive set of experiments has been carried out at different flow rates and a number of signals’ features both in the time domain and in the frequency domain have been considered as indicators of incipient cavitation. The amount of energy of the signal captured by the accelerometer in the component orthogonal to the flow direction, in the band from 10 to 12.8 kHz, demonstrated to be effective in detecting the incipient cavitation, by selecting a proper (condition-dependent) threshold. Therefore, two simple controllers have been designed: the first regulates the speed of the pump, to recover from cavitation, bringing the indicator back to the nominal value, while the second allows to reduce the pump’s rotational speed when the cavitation detector indicates the incipient cavitation and restoring the nominal speed when possible. The latter approach is rather general, because the threshold-based detector can be substituted by any detector providing binary output. Experimental results are reported that demonstrate the effectiveness of the approach.

Tele-monitoring and tele-rehabilitation of the shoulder muscular-skeletal diseases through wearable systems.

In this work we present the development and preliminary testing of a wearable-technology-enabled platform for the remote rehabilitation of a large number of shoulder muscular-skeletal diseases. The presented system (Shoulphy) is conceived to lead and assess the patient, wearing a minimal set of inertial sensors, through personalized physical rehabilitation programs under the remote supervision of the physician/therapist. We have introduced a minimal inertial sensor set and an associated biomechanical reconstruction method based on a bi-articular model of the shoulder. We have tested the system in classical shoulder rehabilitation exercises and we have demonstrated that the system is able to discriminate between correct and compensatory movement strategies.

Natural locomotion based on a reduced set of inertial sensors: Decoupling body and head directions indoors.

Inertial sensors offer the potential for integration into wireless virtual reality systems that allow the users to walk freely through virtual environments. However, owing to drift errors, inertial sensors cannot accurately estimate head and body orientations in the long run, and when walking indoors, this error cannot be corrected by magnetometers, due to the magnetic field distortion created by ferromagnetic materials present in buildings. This paper proposes a technique, called EHBD (Equalization of Head and Body Directions), to address this problem using two head- and shoulder-located magnetometers. Due to their proximity, their distortions are assumed to be similar and the magnetometer measurements are used to detect when the user is looking straight forward. Then, the system corrects the discrepancies between the estimated directions of the head and the shoulder, which are provided by gyroscopes and consequently are affected by drift errors. An experiment is conducted to evaluate the performance of this technique in two tasks (navigation and navigation plus exploration) and using two different locomotion techniques: (1) gaze-directed mode (GD) in which the walking direction is forced to be the same as the head direction, and (2) decoupled direction mode (DD) in which the walking direction can be different from the viewing direction. The obtained results show that both locomotion modes show similar matching of the target path during the navigation task, while DD’s path matches the target path more closely than GD in the navigation plus exploration task. These results validate the EHBD technique especially when allowing different walking and viewing directions in the navigation plus exploration tasks, as expected. While the proposed method does not reach the accuracy of optical tracking (ideal case), it is an acceptable and satisfactory solution for users and is much more compact, portable and economical.

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: i making use of a realistic statistical body model that includes anthropometric constraints and ii using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser SIP enables motion capture using only 6 sensors attached to the wrists, lower legs, back and head and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.

A new algorithm for 3D registration and its application in self-monitoring and early detection of lymphedema

Early detection and management of lymphedema (LE) can significantly reduce the potential of new symptoms and complications. In fact, effective diagnostic of LE can potentially affect the lives in the U.S. of nearly 500 000 current LE patients and over 2.4 million breast cancer survivors who are at-risk for developing this disease at some point in their lives. However, many cancer patients fail to seek medical assistance at the first sign of the disease in part due to the additional burden that constant monitoring imposes on patients. In that sense, a self-monitoring system could represent a major improvement in health-care management and delivery.The main challenges in self monitoring limb volume are in the design of a system that is at the same time inexpensive, easy to use, and accurate, despite being operated by a person without any training. In this paper, we present such a system. The proposed framework relies on off-the-shelf video gaming devices – the Kinect infrared device with a set of inertial sensors. In order to achieve high accuracy despite the typical low-texture and smoothness of the human skin, a new algorithm for 3D registration of clouds of points – Iterative Clustered Closest Points (ICCP) – is also proposed. The final result is a device that can be operated in the comfort of the patient's homes – i.e. it can be operated by professionals as well as non-professionals.In order to validate the system, we first tested its individual parts, more specifically the proposed ICCP algorithm for 3D registration and reconstruction of challenging objects. Then, we tested the complete system for the target application of limb-volume measurement by comparing our system against the gold standards: water displacement and perometer.

A new smart fall-down detector for senior healthcare system using inertial microsensors.

A new smart fall-down detector for senior healthcare system using inertial microsensors and Wi-Fi technology has been designed, prototyped and characterized in this work. The detector can reduce the risk of severe injury or death caused by falling down with minimum false alarm rate. The different patterns of motion are sensed by a set of inertial sensors composed of a tri-axial accelerometer and a tri-axial gyroscope. The signals of motion are sampled and processed by a microcontroller with integrated algorithms. The smart algorithm integrated with machine learning can be customized according to different habits of different seniors to reduce false alarms. The fall-down signal is transmitted through Wi-Fi to the client via Internet.

Underwater localization and mapping: observability analysis and experimental results

Purpose – The authors aim to investigate the observability properties of the process of simultaneous localization and mapping of an autonomous underwater vehicle (AUV), a challenging and important problem in marine robotics, and illustrate the derived results through computer simulations and experimental results with a real AUV. Design/methodology/approach – The authors address the single/multiple beacon observability analysis of the process of simultaneous localization and mapping of an AUV by deriving the nonlinear mathematical model that describes the process; then applying a suitable coordinate transformation, and subsequently a time-scaling transformation to obtain a linear time varying (LTV) system. The AUV considered is equipped with a set of inertial sensors, a depth sensor, and an acoustic ranging device that provides relative range measurements to a set of stationary beacons. The location of the beacons does not need to be necessarily known and in that case, the authors are also interested to localize them. Numerical tests and experimental results illustrate the derived theoretical results. Findings – The authors show that if either the position of one of the beacons or the initial position of the AUV is known, then the system is at least locally weakly observable, in the sense that the set of indistinguishable states from a given initial configuration contains a finite set of isolated points. The simulations and experiments results illustrate the findings. Originality/value – In the single and multiple beacon case and for manoeuvres with constant linear and angular velocities both expressed in the body-frame, known as trimming or steady-state trajectories, the authors derive conditions under which it is possible to infer the state of the resulting system (and in particular the position of the AUV). The authors also describe the implementation of an advanced continuous time constrained minimum energy observer combined with multiple model techniques. Numerical tests and experimental results illustrate the derived theoretical results.

S.P.7 Validation trial of a movement Holter monitor based on accelerometry for the non-ambulatory neuromuscular patients

Abstract One of the most important and challenging issues regarding the clinical trials in non-ambulatory neuromuscular patients is the lack of a reliable and reproducible evaluation method, sensitive enough to quantitatively assess the function of the upper limbs of disabled patients. A movement Holter monitor composed of tri-axial accelerometer, gyroscope and magnetometer is being developed in France at the Institute of Myology in collaboration with SYSNAV Company. The measuring principle is based on inertial sensors. The device is sufficiently light and non-intrusive to be worn as a watch and has 18 h of autonomy. A validation trial of a wireless version of the system is ongoing in 10 non-ambulant neuromuscular patients. The upper limb function of the patients wearing the device is evaluated during a series of different filmed tasks. The evaluations take place at the Institute of Myology at the day of the inclusion and are repeated 14 days after. The system contains a second set of inertial sensors placed on the wheelchair in order to detect its movement. Between the two visits, the patients are continuously recorded in their own environment during daytime. The measurements were feasible even in a very weak 29 year-old Duchenne patient. The data permit to explore new variables representing the upper limb activity level and quantify specific movements. In a preliminary phase, the orientation of the wrist and periods of activity could be identified during a given period of time. Subsequent analyses will assess the identification, characterization and quantification of movements of the wrist taking into account the wheelchair movement. The novelty of this project resides in the possibility of a quantitative evaluation of non-ambulatory neuromuscular patients with limited abilities both in a standardized setting and in their natural environment, thanks to the high-autonomy of this wireless device and its high accuracy.

Biomimetic Underwater Vehicle Modeling Based on Neural Network

AbstractThis paper introduces the basic control methods of a Biomimetic Underwater Vehicle, and a neural network model is designed for the vehicle. The concerned vehicle is propelled by two undulating long-fins installed on both sides. Ten motors are employed to drive the long-fin and sine wave function is employed for motor control. The real-time control system is designed for controlling the long-fins by adjusting its oscillating frequency and oscillating amplitude to control the yaw angle of the vehicle. A set of inertial sensors is employed for collecting pose information including yaw angle. Analyses show that the yaw angle increment is closely related to the long-fin oscillating frequency, the long-fin oscillating amplitude and the vehicle yaw angle velocity. Hence a neural network with one hidden layer is designed for building a nonlinear MISO model. Based on qualitative hydrodynamic analysis, oscillating frequency of the two long-fins and angular velocity are chosen as the model input. And the yaw angle increment is chosen as the model output. Based on analysis of the experimental thrust data, linear function is used as activation function of the neurons. Finally, experimental data are used for neural network training and validation. The results show that the designed neural network model is valid.

A system for understanding violin bowing technique through measurement of physical dynamics

Presented here is a custom-built sensor system for the measurement of violin bowing parameters. An improvement on previous designs, the system includes a set of accelerometers, gyroscopes, and force sensors, installed on a carbon fiber violin bow. In addition, another set of inertial sensors, as well as an electric field position sensor, are mounted on the test violin. When taken together, the data from the violin and bow are combined to achieve estimates (relative to the violin) of the three primary bowing parameters: force, speed, and bow-bridge distance. Because the system is relatively compact and lightweight, it is suitable for operation in realistic performance environments outside the laboratory setting. As such, it is possible to obtain detailed physical gesture data from real performances. The potential of the system is explored in several early studies to investigate the interaction of bowing parameters in common strokes. Distinctions between novices and experts, as well as differences between technique and style among experts, are discussed.

A Multifunction Integrated Approach to Providing Aircraft Inertial Data

This paper describes an approach for providing all the inertial information required by an advanced fighter aircraft from a single set of inertial sensors (gyros and accelerometers). Current aircraft have numerous subsystems performing a wide variety of functions, all requiring inertial reference data. The avionics subsystems requiring inertial data include navigation, reference, flight control, autopilot, cockpit displays, mission avionics, and weapon delivery. Each of these functions obtain inertial data from a dedicated set of inertial sensors. This conventional approach has redundancy of inertial sensors and duplication of inertial data, but since there is no communication between the subsystems, there is no advantage in this redundancy and duplication. Duplication can be eliminated and the advantages of redundancy achieved by providing all required inertial data from a common set of inertial sensors. The challenge of this new approach is to produce a cost effective integrated system architecture that can satisfy all inertial requirements and meet aircraft safety-of-flight and survivability constraints. The Air Force Wright Aeronautical Laboratories is conducting the Integrated Inertial Reference Assembly (IIRA) program to validate this new concept of providing all inertial data from a common set of inertial sensors. When demonstrated, the IIRA concept would have direct application to advanced fighter aircraft as well as advanced transports, trainers, cruise missiles, and bombers.

Strapdown inertial system alignment using statistical filters - A simplified formulation.

Introduction I of Strapdown inertial systems requires determining the attitude (direction cosine) matrix which relates a vehicle-fixed set of axes to a reference or navigation coordinate frame. If the vehicle motion is measured in the reference frame by some auxiliary means such as another set of inertial sensors or doppler radar, transfer alignment can be accomplished by comparing similar quantities measured by the Strapdown system gyros and accelerometers. Several techniques to accomplish transfer alignment exist, including gyrocompassing and the use of Kalman-like (recursive) filters. Typically, alignment schemes employing the Kalman optimum technique involve many state variables to describe relative motion between the vehicle and reference axes. While such filters give generally better accuracy and faster alignment than gyrocompassing they impose a heavy computation burden on the alignment system. In particular, they require solution of a large number of simultaneous differential equations with coefficients dependent upon vehicle motion. The formulation of the Strapdown system transfer alignment problem developed in this paper requires only three state variables which do not vary with time. Consequently, a good deal of the computer burden imposed by other recursive filter implementations is eliminated.