Inertial Sensor Measurements Research Articles

Assessment of Spinal Motion in Young Adults (15 to 26 Years of Age) Without Spine Deformity Using Inertial Sensor and Manual Measurements

Introduction: The evaluation of spinal range of motion is paramount in the context of spinal disorders, especially considering emerging surgical techniques focused on motion preservation and circumventing spinal fusion. Manual measurement techniques, which utilize a goniometer and tape measure, demand proficiency to accurately assess spinal motion. This becomes further complicated in patients with spinal deformities. Inertial sensors emerge as a potential clinical solution. By assessing electronic inertial sensor performance in capturing thoracolumbar spinal range of motion, this study evaluates the level of association observed between the range of motion measurements captured by manual and sensor methods. Methods: Participants included 19 healthy young adults (74% female, average age 20 years [range 15-26]) without spinal conditions. Each performed a series of manual spinal motion evaluations quantified using a standard goniometer and a tape measure. Participants repeated the motions with an electronic inertial sensor attached to their C7 spinous process. Each manual and electronic motion sequence was performed three times. Data were analyzed with a Pearson’s correlation to assess congruence between the datasets, and a paired t-test compared the mean values between the two groups to examine the two motion measurement methodologies. Results: Association between the different planes of motion for manual and electronic repeated clinical motions were moderate (r=0.44) to strong (r=0.70). Manual measurements showed similar levels of variation to that of the electronic measurements. Upon comparing the manual and electronic measurement sets through a paired t-test, the mean values exhibited no statistically significant differences. Conclusion: The electronic motion measurements were congruent with manual measurements based on the correlation values and t-tests presented. Thus, inertial sensors can approximate the measurements of manual methods in assessing spinal range of motion. This demonstrates the potential for clinical adaptation of these sensors into spine centers to objectively assess patient outcomes in spinal motion preserved surgeries.

SiFu: A Generic and Robust Multimodal Signal Fusion Platform for Pervasive Localization

We consider pervasive localization for a roaming user who may sample widely varying signal modes (GPS, WiFi, geomagnetism, Bluetooth low energy, etc.) and values over time and space. Previous works can only apply to specific (two or three) modes and environment, and cannot accommodate arbitrary signal modes and environmental changes due to signal noise, device heterogeneity, phone carriage states, etc. We propose SiFu, a simple, accurate, and generic multimodal signal fusion platform robust against environmental deviation from its design point. As a generic framework, SiFu treats a single-modal localization algorithm as a black box to embrace any existing, emerging or future signals with only incremental training. It employs Bayesian deep learning and data augmentation to mitigate the location bias of the single-modal localization algorithm and run-time deviation from the training data, respectively. Using a unified multimodal likelihood formulation and particle filter, it fuses with inertial sensor measurements for localization. We conduct extensive experiments in different venues (campus, mall, and subway station), and show that SiFu achieves significantly higher localization accuracy as compared to state-of-the-art (cutting the error by more than 20%). It is also robust against environmental variations (reducing error by 30%), even when the signal values deviate greatly from their original design settings.

The Effects of 3D Custom Foot Orthotics with Mechanical Plantar Stimulation in Older Individuals with Cognitive Impairment: A Pilot Study.

Recent scientific evidence supports the idea that foot plantar stimulation increases the functional connectivity of brain regions involved in visuo-spatial and sensory-motor integration. In this before-after, non-randomised intervention study we assessed the change in several gait and postural parameters using inertial sensor measurements after acute plantar stimulation using custom 3D-printed insoles. The pilot study was performed on 22 institutionalised, older individuals with a high comorbidity burden who either walked autonomously or with the help of a cane. The intensity of the effects in the first mechanical plantar stimulation session (at one week) strongly predicted a change in the 180° turn duration (p < 0.05) and the standard deviation of the step duration (p < 0.05) during the timed up-and-go test. Based on these effects, researchers also predicted decreases in some postural parameters such as the root mean square of displacement on the anterior-posterior axis (p < 0.01). Thus, these preliminary findings provide a strong rationale for performing controlled clinical trials with larger samples to investigate the efficacy and mechanisms of mechanical plantar stimulation in frail elderly individuals.

Validity of the Baiobit Inertial Measurements Unit for the Assessment of Vertical Double- and Single-Leg Countermovement Jumps in Athletes.

Jump tests are simple, quick to execute, and considered the most reliable tool to measure lower extremities power and explosiveness in athletes. Wearable inertial sensors allow the assessment of jumping performance on any surface. The validity of inertial sensors measurements is a pivotal prerequisite to reliably implement their utilization in the clinical practice. Twenty-seven athletes (20 M/7 F, age: 27 ± 7 years old) performed five double-leg countermovement jumps (CMJs) and three single-leg CMJs per side with their hands on their hips. Jump height was measured/computed simultaneously with the optoelectronic system, force platforms, and the Baiobit inertial sensor system. The athletes completed the international physical activity questionnaire (IPAQ). When comparing the methods (Baiobit vs. force platforms), a non-statistically significant bias of 1.8 cm was found for two-leg CMJs and -0.6 cm for single-leg CMJs. The intraclass correlation coefficients (ICCs) was "excellent" for double-leg CMJs (ICC = 0.92, 95% CI = 0.89-0.94) and "good" for single-leg CMJs (ICC = 0.89, 95% CI = 0.85-0.91). When comparing the methods (Baiobit vs. force platforms + optoelectronic system), a non-statistically significant bias of -0.9 cm was found for two-leg CMJs and -1.2 cm for single-leg CMJs. The intraclass correlation coefficient (ICC) was "good" for both double-leg CMJs (ICC = 0.80, 95% CI = 0.73-0.85) and for single-leg CMJs (ICC = 0.86, 95% CI = 0.80-0.89). Baiobit tends to overestimate double- and single-leg CMJ height measurements; however, it can be recommended in the world of rehabilitation and sport analysis.

Discrete-Time Rigid Body Pose Estimation Based on Lagrange–d’Alembert Principle

The problem of rigid body pose estimation is treated in discrete-time via discrete Lagrange–d’Alembert principle and discrete Lyapunov methods. The position and attitude of the rigid body are to be estimated simultaneously with the help of vision and inertial sensors. For the discrete-time estimation of pose, the continuous-time rigid body kinematics equations are discretized appropriately. We approach the pose estimation problem as minimizing the energies stored in the errors of estimated quantities. With the help of measurements obtained through optical sensors, artificial rotational and translation potential energy-like terms have been designed. Similarly, artificial rotational and translation kinetic energy-like terms have been devised using inertial sensor measurements. This allows us to construct a discrete-time Lagrangian as the difference of the kinetic and potential energy-like terms, to which a Lagrange–d’Alembert principle is applied to obtain an optimal pose estimation filter. The dissipation terms in the optimal filter are designed through discrete Lyapunov analysis on a suitably constructed Morse–Lyapunov function, and the overall scheme is proved to be almost globally asymptotically stable. The filtering scheme is simulated using noisy sensor data to verify the theoretical properties.

SmartStim: A Recurrent Neural Network Assisted Adaptive Functional Electrical Stimulation for Walking

Abstract According to the Neuro Patience report of the Neurological Alliance, 1 in 6 people in the UK has a neurological condition. With the growth in technology, rehabilitation for neurological problems is one of the fastgrowing fields. Functional Electrical Stimulation (FES) is one of those neuro-rehabilitation methods that uses electrical nerve stimulation to restore functional muscle movements that are lost due to neurological problems such as stroke and multiple sclerosis. This neuroprosthetic device is frequently used to assist walking by treating a condition called Drop Foot, a result of paralysis of the pretibial muscles. This study proposes a two-channel FES device called the SmartStim, which has the ability to modulate its stimulation levels according to various obstacles such as stairs and ramps. This system employs a sensor-based module with a Recurrent Neural Network to classify these different walking scenarios. The module is built with Inertial Measurement sensors embedded in a pair of shoes, and the Recurrent Neural Network uses data from these sensors to predict various obstacles as the user is walking. These predictions are then used by a Fuzzy Logic Controller to control and regulate the stimulation current in two channels of the SmartStim system. In the two channels of the system, one channel will help aid with drop foot, while the other will be used to stimulate another muscle group to help access stairs and ramps by the user. The Recurrent Neural Network module in this system has been trained and tested using the k-fold cross-validation. The evaluation of this trained model shows that it can predict obstacles from sensor data at 97 percent accuracy. Currently, further testing is being performed to assess the workings of the fuzzy logic controller in combination with the Recurrent Neural Network in healthy individuals. It is expected that the SmartStim system may aid users in accessing various walking scenarios more efficiently.

Leg Shaping and Event-Driven Control of a Small-Scale, Low-DoF, Two-Mode Robot

Among small-scale mobile robots, multi-modal locomotion can help compensate for limited actuator capabilities. However, supporting multiple locomotion modes or gaits in small terrestrial robots typically requires complex designs with low locomotion efficiency. In this work, legged and rolling gaits are achieved by a 10~cm robot having just two degrees of freedom (DoF). This is acheived by leg shaping that facilitates whole body rolling and event-driven control that maintains motion using simple inertial sensor measurements. Speeds of approximately 0.4 and 2.2 body lengths per second are achieved in legged and rolling modes, respectively, with low cost of transport. The proposed design approach and control techniques may aid in design of further miniaturized robots reliant on transducers with small range-of-motion.

Validity and Reliability of the Leomo Motion-Tracking Device Based on Inertial Measurement Unit with an Optoelectronic Camera System for Cycling Pedaling Evaluation.

Background: The use of inertial measurement sensors (IMUs), in the search for a more ecological measure, is spreading among sports professionals with the aim of improving the sports performance of cyclists. The kinematic evaluation using the Leomo system (TYPE-R, Leomo, Boulder, CO, USA) has become popular. Purpose: The present study aimed to evaluate the reliability and validity of the Leomo system by measuring the angular kinematics of the lower extremities in the sagittal plane during pedaling at different intensities compared to a gold-standard motion capture camera system (OptiTrack, Natural Point, Inc., Corvallis, OR, USA). Methods: Twenty-four elite cyclists recruited from national and international cycling teams performed two 6-min cycles of cycling on a cycle ergometer at two different intensities (first ventilatory threshold (VT1) and second ventilatory threshold (VT2)) in random order, with a 5 min rest between intensity conditions. The reliability and validity of the Leomo system versus the motion capture system were evaluated. Results: Both systems showed high validity and were consistently excellent in foot angular range Q1 (FAR (Q1)) and foot angular range (FAR) (ICC-VT1 between 0.91 and 0.95 and ICC-VT2 between 0.88 and 0.97), while the variables leg angular range (LAR) and pelvic angle showed a modest validity (ICC-VT1 from 0.52 to 0.71 and ICC-VT2 between 0.61 and 0.67). Compared with Optitrack, Leomo overestimated all the variables, especially the LAR and pelvic angle values, in a range between 12 and 15°. Conclusions: Leomo is a reliable and valid tool for analyzing the ranges of motion of the cyclist’s lower limbs in the sagittal plane, especially for the variables FAR (Q1) and FAR. However, its systematic error for FAR and Pelvic Angle values must be considered in sports performance analysis.

A Novel Privacy Preservation and Quantification Methodology for Implementing Home-Care-Oriented Movement Analysis Systems.

Human movement is generally evaluated through both observations and clinical assessment scales to identify the state and deterioration of a patient’s motor control. Lately, technological systems for human motion analysis have been used in clinics to identify abnormal movement states, while they generally suffer from privacy challenges and concerns especially at home or in remote places. This paper presents a novel privacy preservation and quantification methodology that imitates the forgetting process of human memory to protect privacy in patient-centric healthcare. The privacy preservation principle of this methodology is to change the traditional data analytic routines into a distributed and disposable form (i.e., DnD) so as to naturally minimise the disclosure of patients’ health data. To help judge the efficacy of DnD-based privacy preservation, the researchers further developed a risk-driven privacy quantification framework to supplement the existing privacy quantification techniques. To facilitate validating the methodology, this research also involves a home-care-oriented movement analysis system that comprises a single inertial measurement sensor and a mobile application. The system can acquire personal information, raw data of movements and indexes to evaluate the risk of falls and gait at homes. Moreover, the researchers conducted a technological appreciation survey of 16 health professionals to help understand the perception of this research. The survey obtains positive feedback regarding the movement analysis system and the proposed methodology as suitable for home-care scenarios.

Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things.

Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset.

Capturing High Resolution Plant Movement in the Field.

Lodging of small grains due to environmental stresses results in yield loss, quality reduction, and difficulties with mechanical harvesting, which lead to economic consequences. New technological discoveries allow for faster and in situ measurements for determining the mechanics of loading stress and plant movement. The overall measurement of plant movement can be a very sophisticated method to mechanically test and predict the behavior of stems when exposed to wind. We investigated the inertial measurement of plants during different magnitude wind events. This type of analysis captures real time quantitative stem behavior during wind events. Using a 1.5 cm2 inertial measurement sensor attached to the upper panicle of a plant, we recorded the ranges and extremes of instantaneous linear acceleration and rotational velocity. When this technology was applied to historically known varieties of different lodging classification, the measurements were able to distinguish between cereal species and differences between movement of lodging susceptible and resistant plants without physical lodging. This type of technology could be used to improve field based lodging models and quantify movement resulting from micro changes in structural and composition of the stem, and to analyze plant movement in natural conditions with a resolution and specificity that has so far been prohibitively expensive and technologically challenging to achieve.

Solution to Long-Range Continuous and Precise Positioning in Deep Ocean for Autonomous Underwater Vehicles Using Acoustic Range Estimation and Inertial Sensor Measurements

Solution to Long-Range Continuous and Precise Positioning in Deep Ocean for Autonomous Underwater Vehicles Using Acoustic Range Estimation and Inertial Sensor Measurements

Non-gravitational force measurement and correction by a precision inertial sensor of TianQin-1 satellite

Non-gravitational force models are critical not only for the applications of satellite orbit determination and prediction, but also for the studies of gravitational reference sensors in space-based gravitational wave detection missions and accelerometers in gravity satellite missions. In this paper, based on the inertial sensor data from the TianQin-1 (TQ-1) mission, a correction has been made in the non-gravitational force models by applying additional terms related to the orbital periods. After taking into account this correction, about 37 hours of TQ-1 inertial sensor data is calibrated in the sensitive axes, i.e. y- and z-axes, by comparing with the simulated non-gravitational accelerations. It is indicated that the peak-to-peak value of the non-gravitational acceleration correction terms are about 2% and 13% of the measured accelerations in the y- and z-axes, respectively. Within the frequency band below 0.01 Hz, the root mean square of calibration residual errors in y- and z-axes are suppressed from 1.03 × 10−9 and 3.872 × 10−9 m s−2 to 8.14 × 10−10 and 1.343 × 10−9 m s−2, respectively. The bias and scale factor of the inertial sensor are also obtained from the calibration by the method of least-squares fit. Meanwhile, the inertial sensor measurements are validated and their signal compositions are analyzed.

Review of pose estimation within European ship-automation projects using GNSS-based navigation

Abstract Accurate and reliable navigation systems are necessary to deploy autonomous shipping. For this purpose, georeferenced vehicle-pose information is often generated using sensor data fusion consisting of GNSS data, inertial measurement, and other applicable aiding sensors. This technical overview summarizes current European projects and international work in the field of GNSS-based maritime navigation and introduces and discusses the sensors and methods involved. The review focuses on approaches with proprioceptive sensor technology. Therefore, as a limitation, environment sensing algorithms (e. g., SLAM) are explicitly not included. It is concluded that highly accurate and reliable sensor fusion should include complementary and redundant onboard sensors, which are tightly coupled.

QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning.

Quadrotor usage is continuously increasing for both civilian and military applications such as surveillance, mapping, and deliveries. Commonly, quadrotors use an inertial navigation system combined with a global navigation satellite systems receiver for outdoor applications and a camera for indoor/outdoor applications. For various reasons, such as lighting conditions or satellite signal blocking, the quadrotor’s navigation solution depends only on the inertial navigation system solution. As a consequence, the navigation solution drifts in time due to errors and noises in the inertial sensor measurements. To handle such situations and bind the solution drift, the quadrotor dead reckoning (QDR) approach utilizes pedestrian dead reckoning principles. To that end, instead of flying the quadrotor in a straight line trajectory, it is flown in a periodic motion, in the vertical plane, to enable peak-to-peak (two local maximum points within the cycle) distance estimation. Although QDR manages to improve the pure inertial navigation solution, it has several shortcomings as it requires calibration before usage, provides only peak-to-peak distance, and does not provide the altitude of the quadrotor. To circumvent these issues, we propose QuadNet, a hybrid framework for quadrotor dead reckoning to estimate the quadrotor’s three-dimensional position vector at any user-defined time rate. As a hybrid approach, QuadNet uses both neural networks and model-based equations during its operation. QuadNet requires only the inertial sensor readings to provide the position vector. Experimental results with DJI’s Matrice 300 quadrotor are provided to show the benefits of using the proposed approach.

Implementation of Sequence-Based Classification Methods for Motion Assessment and Recognition in a Traditional Chinese Sport (Baduanjin).

This study aimed to assess the motion accuracy of Baduanjin and recognise the motions of Baduanjin based on sequence-based methods. Motion data of Baduanjin were measured by the inertial sensor measurement system (IMU). Fifty-four participants were recruited to capture motion data. Based on the motion data, various sequence-based methods, namely dynamic time warping (DTW) combined with classifiers, hidden Markov model (HMM), and recurrent neural networks (RNNs), were applied to assess motion accuracy and recognise the motions of Baduanjin. To assess motion accuracy, the scores for motion accuracies from teachers were used as the standard to train the models on the different sequence-based methods. The effectiveness of Baduanjin motion recognition with different sequence-based methods was verified. Among the methods, DTW + k-NN had the highest average accuracy (83.03%) and shortest average processing time (3.810 s) during assessing. In terms of motion reorganisation, three methods (DTW + k-NN, DTW + SVM, and HMM) had the highest accuracies (over 99%), which were not significantly different from each other. However, the processing time of DTW + k-NN was the shortest (3.823 s) compared to the other two methods. The results show that the motions of Baduanjin could be recognised, and the accuracy can be assessed through an appropriate sequence-based method with the motion data captured by IMU.

Impact of outgassing and its corrective methods to improve the vacuum stability in the Coriolis Vibratory Gyroscope

Coriolis Vibratory Gyroscope (CVG) is an inertial angular rate measurement sensor. CVG sensor comprises of metal coated hemispherical quartz vibrating structure as the rotation sensing element. It is forced to vibrate at one of its resonant modes by electrostatic excitation. Because of the limited flexibility of the quartz sensing element, the amplitude is limited to the submicron level. An ultra-high vacuum environment is required for the sensing element to sustain vibration for a long time. The criticality in the sensor development is to maintaining an ultra-high vacuum environment for the sensing element. Sensor suffers a problem of vacuum instability during the operation due to the outgassing from its components. This paper presents a novel approach in the identification of various outgassing sources that exist in the sensor, mitigation plan to minimize outgassing rate by selection of suitable raw material, suitable fabrication process of the components, and the surface characterization etc. The research work also presents the cost-effective experimental methodology to measure the outgassing rate from the sensor components to assess the CVG vacuum life, the requirement of the getter to maintain the ultra-high vacuum level throughout the CVG operation.

SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies.

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.

Relationship between ball speed and acceleration on the elbow in collegiate baseball pitcher －Analysis by elbow joint acceleration using an inertial measurement sensor－

Relationship between ball speed and acceleration on the elbow in collegiate baseball pitcher －Analysis by elbow joint acceleration using an inertial measurement sensor－

IMU-Based Estimation of Lower Limb Motion Trajectory With Graph Convolution Network

In recent years, the motion capture system has received a lot of attention because of its wide application, such as movie animation, sport and medical applications. In the field of rehabilitation, motion capture systems are often used to collect the motion information of the patient when he/she is performing rehabilitation tasks. It can be used to quantify the patient’s rehabilitation effectiveness and provide the physician with more objective data as a reference. Using inertial sensors is one of motion capture methods. However, the major challenge to reconstruct accurate human posture with sensor measurements are signal noise, bias and inaccurate gyroscope estimation during long-term wearing. Based on the reason mentioned above, this paper proposes a graph convolutional network (GCN)-based deep learning architecture that uses effective information collected by inertial sensors to predict a sequence of position of each joint of a human’s lower limbs in the 3D space throughout the entire motion. Such motion prediction result based on the deep learning model is to reduce the joint’s tracking error relying only on direct calculations based on measurements of inertial sensors. In the final experiment, the lower limb motion trajectory during walking is used to verify that the method proposed in this work can actually outperform the traditional ones as just mentioned by achieving 2.9 mm drop in mean per joint position error (MPJPE) in 3.6M dataset, resulting in 12.2 mm in MPJPE, and mitigate the drift in real scenario. In our future work, we plan to verify the effectiveness of the proposed model and system for rehabilitation through clinical studies.