Six-axis Inertial Measurement Unit Research Articles

Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors' Motor Ability.

Frequent and objective monitoring of motor recovery progression holds significant importance in stroke rehabilitation. Despite extensive studies on wearable solutions in this context, the focus has been predominantly on evaluating limb activity. This study aims to address this limitation by delving into a novel measure of wrist kinematics more intricately related to patients' motor capacity. To explore a new wearable-based approach for objectively and reliably assessing upper-limb motor ability in stroke survivors using a single inertial sensor placed on the stroke-affected wrist. Seventeen stroke survivors performed a series of daily activities within a simulated home setting while wearing a six-axis inertial measurement unit on the wrist affected by stroke. Inertial data during point-to-point upper-limb movements were decomposed into movement segments, from which various kinematic variables were derived. A data-driven approach was then employed to identify a kinematic variable demonstrating robust internal reliability, construct validity, and convergent validity. We have identified a key kinematic variable, namely the 90th percentile of movement segment distance during point-to-point movements. This variable exhibited robust reliability (intra-class correlation coefficient of .93) and strong correlations with established clinical measures of motor capacity (Pearson's correlation coefficients of .81 with the Fugl-Meyer Assessment for Upper-Extremity; .77 with the Functional Ability component of the Wolf Motor Function Test; and -.68 with the Performance Time component of the Wolf Motor Function Test). The findings underscore the potential for continuous, objective, and convenient monitoring of stroke survivors' motor progression throughout rehabilitation.

A Low-Cost Inertial Measurement Unit Motion Capture System for Operation Posture Collection and Recognition.

In factories, human posture recognition facilitates human-machine collaboration, human risk management, and workflow improvement. Compared to optical sensors, inertial sensors have the advantages of portability and resistance to obstruction, making them suitable for factories. However, existing product-level inertial sensing solutions are generally expensive. This paper proposes a low-cost human motion capture system based on BMI 160, a type of six-axis inertial measurement unit (IMU). Based on WIFI communication, the collected data are processed to obtain the displacement of human joints' rotation angles around XYZ directions and the displacement in XYZ directions, then the human skeleton hierarchical relationship was combined to calculate the real-time human posture. Furthermore, the digital human model was been established on Unity3D to synchronously visualize and present human movements. We simulated assembly operations in a virtual reality environment for human posture data collection and posture recognition experiments. Six inertial sensors were placed on the chest, waist, knee joints, and ankle joints of both legs. There were 16,067 labeled samples obtained for posture recognition model training, and the accumulated displacement and the rotation angle of six joints in the three directions were used as input features. The bi-directional long short-term memory (BiLSTM) model was used to identify seven common operation postures: standing, slightly bending, deep bending, half-squatting, squatting, sitting, and supine, with an average accuracy of 98.24%. According to the experiment result, the proposed method could be used to develop a low-cost and effective solution to human posture recognition for factory operation.

Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication.

This study has designed and developed a smart data glove based on five-channel flexible capacitive stretch sensors and a six-axis inertial measurement unit (IMU) to recognize 25 static hand gestures and ten dynamic hand gestures for amphibious communication. The five-channel flexible capacitive sensors are fabricated on a glove to capture finger motion data in order to recognize static hand gestures and integrated with six-axis IMU data to recognize dynamic gestures. This study also proposes a novel amphibious hierarchical gesture recognition (AHGR) model. This model can adaptively switch between large complex and lightweight gesture recognition models based on environmental changes to ensure gesture recognition accuracy and effectiveness. The large complex model is based on the proposed SqueezeNet-BiLSTM algorithm, specially designed for the land environment, which will use all the sensory data captured from the smart data glove to recognize dynamic gestures, achieving a recognition accuracy of 98.21%. The lightweight stochastic singular value decomposition (SVD)-optimized spectral clustering gesture recognition algorithm for underwater environments that will perform direct inference on the glove-end side can reach an accuracy of 98.35%. This study also proposes a domain separation network (DSN)-based gesture recognition transfer model that ensures a 94% recognition accuracy for new users and new glove devices.

Development of a New Wearable Device for the Characterization of Hand Tremor.

Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.

Robotic Hummingbird Axial Dynamics and Control near Hovering: A Simulation Model

After a short overview of the COLIBRI project, this paper considers the cycle-averaged flight dynamics of a flapping-wing robot near hovering, taking advantage of the weak coupling between the roll and pitch axes. The system is naturally unstable; it needs to be stabilized actively, which requires an attitude reconstruction. Due to the flapping of the wings, the system is subject to a strong periodic noise at the flapping frequency and its higher harmonics; the resulting axial forces and pitch moments are characterized from experimental data. The flapping noise propagates to the six-axis Inertial Measurement Unit (IMU) consisting of three accelerometers and three gyros. The paper is devoted to attitude reconstruction in the presence of flapping noise representative of flight conditions. Two methods are considered: (i) the complementary filter based on the hovering assumption and (ii) a full-state dynamic observer (Kalman filter). Unlike the complementary filter, the full-state dynamic observer allows the reconstruction of the axial velocity, allowing us to control the hovering without any additional sensor. A numerical simulation is conducted to assess the merit of the two methods using experimental noise data obtained with the COLIBRI robot. The paper discusses the trade-off between noise rejection and stability.

A novel sensor-embedded holding device for monitoring upper extremity functions.

There are several causes that can lead to functional weakness in the hands or upper extremities (UE), such as stroke, trauma, or aging. Therefore, evaluation and monitoring of UE rehabilitation have become essential. However, most traditional evaluation tools (TETs) and assessments require clinicians to assist or are limited to specific clinical settings. Several novel assessments might apply to wearable devices, yet those devices will still need clinicians or caretakers to help with further tests. Thus, a novel UE assessment device that is user-friendly and requires minimal assistance would be needed. The cylindrical grasp is one of the common UE movements performed in daily life. Therefore, a cylindrical sensor-embedded holding device (SEHD) for training and monitoring was developed for a usability test within this research. The SEHD has 14 force sensors with an array designed to fit holding positions and a six-axis inertial measurement unit (IMU) to monitor grip strength, hand dexterity, acceleration, and angular velocity. Six young adults, six healthy elderly participants, and three stroke survivors had participated in this study to see if the SEHD could be used as a reference to TETs. During result analyses, where the correlation coefficient analyses were applied, forearm rotation smoothness and the Purdue Pegboard Test (PPT) showed a moderate negative correlation [r (16) = -0.724, p < 0.01], and the finger independence showed a moderate negative correlation with the PPT [r (10) = -0.615, p < 0.05]. There was also a highly positive correlation between the maximum pressing task and Jamar dynamometer in maximum grip strength [r (16) = 0.821, p < 0.01]. These outcomes suggest that the SEHD with simple movements could be applied as a reference for users to monitor their UE ability.

Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation

A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ashdod, Israel in a car equipped with a global navigation satellite system (GNSS) real time kinematic (RTK) positioning device and a synchronized IMU. Ground truth labels for the car speed were calculated using the position measurements obtained at the high rate of 50 Hz. A DNN architecture with long short-term memory layers is proposed to enable high-frequency speed estimation that accounts for previous inputs history and the nonlinear relation between speed, acceleration and angular velocity. A simplified aided dead reckoning localization scheme is formulated to assess the trained model which provides the speed pseudo-measurement. The trained model is shown to substantially improve the position accuracy during a 4 minutes drive without the use of GNSS position updates.

Motion Analysis of Football Kick Based on an IMU Sensor.

A greater variety of technologies are being applied in sports and health with the advancement of technology, but most optoelectronic systems have strict environmental restrictions and are usually costly. To visualize and perform quantitative analysis on the football kick, we introduce a 3D motion analysis system based on a six-axis inertial measurement unit (IMU) to reconstruct the motion trajectory, in the meantime analyzing the velocity and the highest point of the foot during the backswing. We build a signal processing system in MATLAB and standardize the experimental process, allowing users to reconstruct the foot trajectory and obtain information about the motion within a short time. This paper presents a system that directly analyzes the instep kicking motion rather than recognizing different motions or obtaining biomechanical parameters. For the instep kicking motion of path length around 3.63 m, the root mean square error (RMSE) is about 0.07 m. The RMSE of the foot velocity is 0.034 m/s, which is around 0.45% of the maximum velocity. For the maximum velocity of the foot and the highest point of the backswing, the error is approximately 4% and 2.8%, respectively. With less complex hardware, our experimental results achieve excellent velocity accuracy.

RestEaze: An Emerging Technology to Characterize Leg Movements During Sleep

Abstract Several sleep disorders are characterized by periodic leg movements during sleep including Restless Leg Syndrome, and can indicate disrupted sleep in otherwise healthy individuals. Current technologies to measure periodic leg movements during sleep are limited. Polysomnography and some home sleep tests use surface electromyography to measure electrical activity from the anterior tibilias muscle. Actigraphy uses three-axis accelerometers to measure movement of the ankle. Electromyography misses periodic leg movements that involve other leg muscles and is obtrusive because of the wires needed to carry the signal. Actigraphy based devices require large amplitude movements of the ankle to detect leg movements (missing the significant number of more subtle leg movements) and can be worn in multiple configurations precluding precision measurement. These limitations have contributed to their lack of adoption as a standard of care for several sleep disorders. In this study, we develop the RestEaze sleep assessment tool as an ankle-worn wearable device that combines capacitive sensors and a six-axis inertial measurement unit to precisely measure periodic leg movements during sleep. This unique combination of sensors and the form-factor of the device addresses current limitations of periodic leg movements during sleep measurement techniques. Pilot data collected shows high correlation with polysomnography across a heterogeneous participant sample and high usability ratings. RestEaze shows promise in providing ecologically valid, longitudinal measures of leg movements that will be useful for clinicians, researchers, and patients to better understand sleep.

Evaluating daily physical activity and biomechanical measures using wearable technology in people with Achilles tendinopathy: A descriptive exploratory study

BackgroundLoad management is considered an important factor for prevention and treatment of Achilles tendinopathy. However, little attention has been given to monitoring daily load objectively in this population. ObjectivesWe aimed to explore patterns in proxies of daily load derived from a six-axis inertial measurement unit (IMU) over a one-week period and the concordance between day-to-day fluctuation in pain intensity and IMU measures. DesignDescriptive exploratory study. MethodTen participants with Achilles tendinopathy (age: 53.00 ± 12.37) wore an IMU on the affected ankle for one week. Participants were contacted via text message three times daily to rate their worst pain intensity. Physical activity and biomechanical measures derived from the IMU signals including daily number of steps, peak stride rate, peak shank acceleration, and peak shank angular velocity were calculated. ResultsThe relationship between weekly worst pain and physical activity levels appeared modest; with increased steps not seeming to be linked to increased or reduced pain levels. According to the daily pain and daily IMU measures, a concordant pattern was evident in younger, highly active participants. However, in the middle-aged/older less active participants, there was either a fluctuation in pain intensity without fluctuation in the IMU measures, or a stable pattern of both pain and IMU measures. ConclusionsOur exploratory study results suggest that continuous monitoring of proxies of daily load measures in parallel with pain may provide information about load management strategies in people with Achilles tendinopathy. Monitoring of these proxies may ultimately have a role in improving Achilles tendinopathy management.

Dynamic Compensation of Ultra-Low-Range Pressure Sensors

A technique has been developed to compensate pressure readings from arrays of highly sensitive membrane-type pressure sensors for deflections caused by acceleration normal to the plane of the membrane using a single inertial measurement unit. By normalizing the fourth-order unsteady Kirchoff-Love equation, it can be shown that inertial body pseudoforces and applied surface pressure elicit a similar and additive response from the sensors. Inertial effects arising from linear and angular acceleration as well as angular velocity may therefore be converted to ‘pseudopressures’ and eliminated by means of a simple linear compensation process which can be calibrated using only gravity. To demonstrate, signals from a conventional six-axis inertial measurement unit (including three orthogonal components each of angular velocity and linear acceleration) are used to provide an approximation of the acceleration of the sensing dies within a seven-channel distributed array of ultra-low pressure sensors. Applying the proposed correction reduces the maximum full-scale uncertainty of the measurements by as much as 50%.

Accurate and Robust Walking Speed Estimation With Adaptive Regression Models for Wrist-Worn Devices

With the rise of interest in watch-type and wristband-type devices, wrist-worn devices have become a growing market in the field of wearable activity tracking devices. However, activity tracking with wrist-worn device is more challenging than the task with the other parts of the body, since upper limb movements are uncoupled from the action of walking and reveal irregular movements. This paper proposes a walking speed estimation method with data from a six-axis inertial measurement unit (IMU), which is commonly mounted in wrist-worn devices, and user’s height information. The proposed method provides accurate walking speed estimation results under different sensor-carrying modes and walking speeds. The estimation is based on sensor-carrying mode detection with the TreeBagger model, and on Gaussian process regression (GPR) models, which are adapted to seven predetermined sensor-carrying modes. The speed estimation is done by calculating a weighted sum of multiple GPR models with the probabilities from TreeBagger model. To evaluate the superiority of our method, we implement it on a hardware. An experimental evaluation is performed on 18 healthy subjects with a treadmill. The experimental results show that the proposed method outperforms existing studies and comparable commercial devices for all sensing conditions. The averaged error of the proposed method is about 5% for all sensing conditions, while others show error of more than 15% in different sensing conditions. The results show that proposed method provides better accuracy compared to existing studies in terms of estimating walking speed accurately under changing sensing conditions only with single IMU sensor.

Wireless Epidermal Six-Axis Inertial Measurement Units for Real-Time Joint Angle Estimation

Technological advances in wireless communications, miniaturized sensors, and low-power electronics have made it possible to implement integrated wireless body area networks (WBANs). These developments enable the applications of wireless wearable systems for diagnosis, health monitoring, rehabilitation, and dependency care. Across the current range of commercial wearable devices, the products are not firmly fixed to the human body. To minimize data error caused by movement of the human body and to achieve accurate measurements, it is essential to bring the wearable device close to the skin. This paper presents the implementation of a patch-type, six-axis inertial measurement unit (IMU) with wireless communication technology. The device comprises hard-electronic components on a stretchable elastic substrate for application in epidermal electronics, to collect precise data from the human body. Instead of the commonly used cleanroom processes of implementing devices on a stretchable substrate, a simple and inexpensive “cut-solder-paste” method is adopted to fabricate complex, convoluted interconnections. Thus, the signal distortions in the proposed device can be minimized during various physical activities and skin deformations when used in gait analysis. The inertial sensor data measured from the motion of the body can be sent in real-time via Bluetooth to any processing unit enabled with such a widespread standard wireless interface. For performance evaluation, the implemented device is mounted on a rotation plate in order to compare performance with the conventional product. In addition, an experiment on joint angle estimation is performed by attaching the device to an actual human body. The preliminary results of the device indicate the potential to monitor people in remote settings for applications in mobile health, human-computer interfaces (HCIs), and wearable robots.

Robocentric visual–inertial odometry

In this paper, we propose a novel robocentric formulation of the visual–inertial navigation system (VINS) within a sliding-window filtering framework and design an efficient, lightweight, robocentric visual–inertial odometry (R-VIO) algorithm for consistent motion tracking even in challenging environments using only a monocular camera and a six-axis inertial measurement unit (IMU). The key idea is to deliberately reformulate the VINS with respect to a moving local frame, rather than a fixed global frame of reference as in the standard world-centric VINS, in order to obtain relative motion estimates of higher accuracy for updating global pose. As an immediate advantage of this robocentric formulation, the proposed R-VIO can start from an arbitrary pose, without the need to align the initial orientation with the global gravitational direction. More importantly, we analytically show that the linearized robocentric VINS does not undergo the observability mismatch issue as in the standard world-centric counterparts that has been identified in the literature as the main cause of estimation inconsistency. Furthermore, we investigate in depth the special motions that degrade the performance in the world-centric formulation and show that such degenerate cases can be easily compensated for by the proposed robocentric formulation, without resorting to additional sensors as in the world-centric formulation, thus leading to better robustness. The proposed R-VIO algorithm has been extensively validated through both Monte Carlo simulation and real-world experiments with different sensing platforms navigating in different environments, and shown to achieve better (or competitive at least) performance than the state-of-the-art VINS, in terms of consistency, accuracy, and efficiency.

Determination of Motor Torque for Power-Assist Electric Bicycles Using Observer-Based Sensor Fusion

This paper proposes a sensor fusion algorithm to determine the motor torque for power-assist electric bicycles. Instead of using torque sensors to directly measure the pedaling torque, outputs from a wheel encoder and a six-axis inertial measurement unit (IMU) are processed by the fusion algorithm to estimate the slope angle of the road and the longitudinal acceleration of the bicycle for conducting mass compensation, gravity compensation, and friction compensation. The compensations allow the ride of the electric bicycle on hills to be as effortless as the ride of a plain bicycle on the level ground regardless of the weight increase by the battery and the motor. The sensor fusion algorithm is basically an observer constructed on the kinematic model which describes the time-varying characteristics of the gravity vector observed from a frame moving with the bicycle. By exploiting the structure of the observer model, convergence of the estimation errors can be easily achieved by selecting two constant, subgain matrices in spite of the time-varying characteristics of the model. The validity of the sensor fusion is verified by both numerical simulations and experiments on a prototype bicycle.

Active rehabilitation training system for upper limb based on virtual reality

In this article, an active rehabilitation training system based on the virtual reality technology is designed for patients with the upper-limb hemiparesis. The six-axis inertial measurement unit se...

Data Glove System Embedded With Inertial Measurement Units for Hand Function Evaluation in Stroke Patients.

This paper proposes a data glove system integrated with six-axis inertial measurement unit sensors for evaluating the hand function of patients who have suffered a stroke. The modular design of this data glove facilitates its use for stroke patients. The proposed system can use the hand's accelerations, angular velocities, and joint angles as calculated by a quaternion algorithm, to help physicians gain new insights into rehabilitation treatments. A clinical experiment was performed on 15 healthy subjects and 15 stroke patients whose Brunnstrom stages (BSs) ranged from 4 to 6. In this experiment, the participants were subjected to a grip task, thumb task, and card turning task to produce raw data and three features, namely, the average rotation speed, variation of movement completion time, and quality of movement; these features were extracted from the recorded data to form 2-D and 3-D scatter plots. These scatter plots can provide reference information and guidance to physicians who must determine the BSs of stroke patients. The proposed system demonstrated a hit rate of 70.22% on average. Therefore, this system can effectively reduce physicians' load and provide them with detailed information about hand function to help them adjust rehabilitation strategies for stroke patients.

IMU-Based Wrist Rotation Control of a Transradial Myoelectric Prosthesis.

This paper describes a control method intended to facilitate improved control of a myoelectric prosthesis containing a wrist rotator. Rather than exclusively utilizing electromyogram (EMG) for the control of all myoelectric components (e.g., a hand and a wrist), the proposed controller utilizes inertial measurement (from six-axis inertial measurement unit (IMU)) to sense upper arm abduction/adduction, and uses this input to command a wrist rotation velocity. As such, the controller essentially substitutes shoulder abduction/adduction in place of agonist/antagonist EMG to control wrist angular velocity, which preserves EMG for control of the hand (or other arm components). As a preliminary assessment of efficacy, the control method was implemented on a transradial prosthesis prototype with a powered wrist rotator and hand, and experimentally assessed on five able-bodied subjects who wore the prototype using an able-bodied adaptor and one transradial amputee subject while performing assessments representative of activities of daily living. The assessments compared the (timed) performance of the combined EMG/ IMU-based control method with a (conventional) sequential EMG control approach. Results of the assessment indicate that the able-bodied subjects were able to perform the tasks 33% faster on average with the EMG/IMU-based method, relative to a conventional sequential EMG method. The same assessment was subsequently conducted using a single transradial amputee subject, which resulted in similar performance trends, although with a somewhat lessened effect size-specifically, the amputee subject was on average 22% faster in performing tasks with the IMU-based controller.