Optisense: Computational Optimization for Strain Sensor Placement in Wearable Motion Tracking Systems

Despite significant progress in developing wearable systems for hand tracking, most devices are still bulky, restrictive to the user or to the placement of the exoskeleton systems, and sensitive to skin preparation and impedance. In this work, we develop a wristband that integrates an array of 10 skin‐conformal strain sensors based on laser‐induced graphene, which is optimized for continuous measurement of skin strain. The device is characterized to identify several hand gestures and tasks while simultaneously using an optical camera‐based hand‐tracking system to estimate the joint locations for ground truth generation. Machine learning models are developed to predict gestures as well as specific hand joint angles with high accuracy of >90% and >95%, respectively. The findings show that the sensors placed closer to actuation‐specific anatomical features contribute more toward the high accuracy. The sensor array is also integrated with a wearable readout system that wirelessly transmits the data in real time in order to control a robotic arm as a proof of concept for human–robot interaction applications. The developed skin‐conformal device is expected to find wide applications in rehabilitation, sports sciences, and human–computer interaction, paving the way for low‐profile prosthetic and orthotic control systems.

Introduction Wearable motion tracking technology often focuses on reducing the number of sensors to simplify design and lower costs. Research has shown that single IMUs can reconstruct leg kinematics (Gholami et al., 2020; Hossain et al., 2022; Lim et al., 2020) and ground reaction forces (Jiang et al., 2020) effectively. Additionally, model-based methods have demonstrated the feasibility of using fewer gyroscopes to estimate stride length and motion range in healthy individuals and patients with coxarthritis (Salarian et al., 2013). In this study, we aim to assess the precision of sagittal joint angle estimations using strain sensors while minimizing sensor count. Methods We conducted a study with ten participants based on our previous work that involved collecting single-leg treadmill running data to monitor lower limb joint angles with piezoresistive strain sensors. Subjects ran on an instrumented treadmill at 8-10 km/h, wearing athletic pants embedded with nine strain sensors located on the hip, knee, and ankle. Optical motion capture provided reference kinematics. Our prior research achieved less than 1.5° error in the sagittal plane using a machine-learning approach. The current study explores the extent to which sensor reduction is possible without meaningful loss of accuracy. Three evaluation measures were used for assessment: Pearson correlation, dynamic time warping, and root-mean-squared error. Results The results from our correlation analysis will be used to develop a model that optimally balances between accuracy and minimizing the number of sensors. This has practical implications in sports science, where athletes could benefit from less intrusive and more comfortable performance monitoring, and in healthcare, for remote monitoring of patients with mobility issues. References Gholami, M., Napier, C., & Menon, C. (2020). Estimating lower extremity running gait kinematics with a single accelerometer: A deep learning approach. Sensors, 20(10), Article 2939. https://doi.org/10.3390/s20102939 Hossain, M. S., Bin, Dranetz, J., Choi, H., & Guo, Z. (2022). DeepBBWAE-Net: A CNN-RNN based deep superlearner for estimating lower extremity sagittal plane joint kinematics using shoe-mounted IMU sensors in daily living. IEEE Journal of Biomedical and Health Informatics, 26(8), 3906-3917. https://doi.org/10.1109/jbhi.2022.3165383 Jiang, X., Napier, C., Hannigan, B., Eng, J. J., & Menon, C. (2020). Estimating vertical ground reaction force during walking using a single inertial sensor. Sensors, 20(15), Article 4345. https://doi.org/10.3390/s20154345 Lim, H., Kim, B., & Park, S. (2020). Prediction of lower limb kinetics and kinematics during walking by a single IMU on the lower back using machine learning. Sensors, 20(1), Article 130. https://doi.org/10.3390/s20010130 Salarian, A., Burkhard, P. R., Vingerhoets, F. J. G., Jolles, B. M., & Aminian, K. (2013). A novel approach to reducing number of sensing units for wearable gait analysis systems. IEEE Transactions on Biomedical Engineering, 60(1), 72–77. https://doi.org/10.1109/TBME.2012.2223465

This paper investigates the problem of an optimal sensor placement for better shape deformation sensing of a new antenna structure with embedded or attached Fiber Bragg grating (FBG) strain sensors. In this paper, the deformation shape of the antenna structure is reconstructed using a strain–displacement transformation, according to the measured discrete strain data from limited FBG strain sensors. Moreover, a two-stage sensor placement method is proposed using a derived relative reconstruction error equation. In this method, the initial sensor locations are determined using the principal component analysis based on orthogonal trigonometric (i.e., QR) decomposition, and then a new location is sequentially added into the initial sensor locations one by one by minimizing the relative reconstruction error considering information redundancy. The numerical simulations are conducted, and the comparisons show that the proposed method is advantageous in terms of the sensor distribution and computational cost. Experimental validation is performed using an antenna experimental platform equipped with an optimal FBG strain sensor configuration, and the reconstruction results show good agreements with those measured directly from displacement sensors. The proposed method has a large potential for the strain sensor placement of complex structures, and the proposed antenna structure with FBG strain sensors can be applied to the future wing-skin antenna or flexible space-based antenna.

BACKGROUND The development of wearable solutions for tracking upper limb motion has gained research interest over the past decade. This paper provides a systematic review of related research on the type, feasibility, signal processing techniques, and feedback of wearable systems for tracking upper limb motion, mostly in rehabilitation applications, to understand and monitor human movement. OBJECTIVE The aim of this article is to investigate how wearables are used to capture upper limb functions, especially related to clinical and rehabilitation applications. METHODS A systematic literature search identified 27 relevant studies published in English from 2011 to 2023, across 4 databases: ACM Digital Library, IEEE Xplore, PubMed, and ScienceDirect. We included papers focusing on motion or posture tracking for the upper limbs, wearable devices, feedback given to end users, and systems having clinical or rehabilitation purposes. We excluded papers focusing on exoskeletons, robotics, prosthetics, orthoses, or activity recognition systems; reviews; and books. RESULTS The results from this research focus on wearable devices that are designed to monitor upper limb movement. More specifically, studies were divided into 2 distinct categories: clinical motion tracking (15/27, 56%) and rehabilitation (12/27, 44%), involving healthy individuals and patients, with a total of 439 participants. Among the 27 studies, the majority (19/27) used inertial measurement units to track upper limb movement or smart textiles embedded with sensors. These devices were attached to the body with straps (mostly Velcro), providing flexibility and stability. The developed wearable devices positively influenced user motivation through the provided feedback, with visual feedback being the most common owing to the high level of independence provided. Moreover, a variety of signal processing techniques, such as Kalman and Butterworth filters, were applied to ensure data accuracy. However, limitations persist and include sensor positioning, calibration, and battery life, as well as a lack of clinical data on the effectiveness of these systems. The sampling rate of the data collection ranged from 50 Hz to 2000 Hz, which notably affected data quality and battery life. In addition, several findings were inconclusive, and thus, further future research is needed to understand and improve upper limb posture to develop progressive wearable systems. CONCLUSIONS This paper offers a comprehensive overview of wearable monitoring systems, with a focus on upper limb motion tracking and rehabilitation. It emphasizes the various types of available solutions; their efficacy, wearability, and feasibility; and proposed processing techniques. Finally, it presents robust findings regarding feedback accuracy derived from experiments and outlines potential future research directions.

The development of wearable solutions for tracking upper limb motion has gained research interest over the past decade. This paper provides a systematic review of related research on the type, feasibility, signal processing techniques, and feedback of wearable systems for tracking upper limb motion, mostly in rehabilitation applications, to understand and monitor human movement. The aim of this article is to investigate how wearables are used to capture upper limb functions, especially related to clinical and rehabilitation applications. A systematic literature search identified 27 relevant studies published in English from 2011 to 2023, across 4 databases: ACM Digital Library, IEEE Xplore, PubMed,andScienceDirect. We included papers focusing on motion or posture tracking for the upper limbs, wearable devices, feedback given to end users, andsystems having clinical or rehabilitation purposes. We excluded papers focusing on exoskeletons, robotics, prosthetics, orthoses, or activity recognition systems; reviews; and books. The results from this research focus on wearable devices that are designed to monitor upper limb movement. More specifically, studies were divided into 2 distinct categories: clinical motion tracking (15/27, 56%) and rehabilitation (12/27, 44%), involving healthy individuals and patients, with atotal of 439 participants. Among the 27 studies, the majority (19/27) used inertial measurement units to track upper limb movement or smart textiles embedded with sensors. These devices were attached to the body with straps (mostly Velcro), providing flexibility and stability. The developed wearable devices positively influenced user motivation through the provided feedback, with visual feedback being the most common owing to the high level of independence provided. Moreover, a variety of signal processing techniques, such as Kalman and Butterworth filters, were applied to ensure data accuracy. However, limitations persist and include sensor positioning, calibration, and battery life, as well as a lack of clinical data on the effectiveness of these systems. The sampling rate of the data collection ranged from 50 Hz to 2000 Hz, which notably affected data quality and battery life. In addition, several findings were inconclusive, and thus, further future research is needed to understand and improve upper limb posture to develop progressive wearable systems. This paper offers a comprehensive overview of wearable monitoring systems, with a focus on upper limb motion tracking and rehabilitation. It emphasizes the various types of available solutions; their efficacy, wearability, and feasibility; and proposed processing techniques. Finally, it presents robust findings regarding feedback accuracy derived from experiments and outlines potential future research directions.

Strategy for sensor number determination and placement optimization with incomplete information based on interval possibility model and clustering avoidance distribution index

Proper monitor planning is a vital component of structural health monitoring (SHM) project. An extremely important part of the monitor planning is the placement of sensors, usually in the form of acceleration sensors. For the placement of three-dimensional acceleration sensors, the state of practice is to select the sensor configuration by previous experiences. However, this results in a waste of many sensors. A novel method called siege ant colony algorithm (SAC) is proposed in this paper. This method is built on the previous ant colony optimization (ACO) in the direction of improving efficiency and accuracy when applied to optimal sensor placement (OSP) problems in large-scale structure monitoring. This method is applied and compared with standard approaches using the Hanjiang transmission tower.

Multi-objective sensor placement optimization of helicopter rotor blade based on Feature Selection

A method for determining the optimal two-dimensional spatial placement of multiple sensors participating in a robot perception task is introduced in this paper. This work is motivated by the fact that sensor data fusion is an effective means of reducing uncertainties in sensor observations, and that the combined uncertainty varies with the relative placement of the sensors with respect to each other. The problem of optimal sensor placement is formulated and a solution is presented in two dimensional space. The algebraic structure of the combined sensor uncertainty with respect to the placement of sensors is studied. A necessary condition for optimal placement is derived and this necessary condition is used to obtain an efficient closed-form solution for the global optimal placement. Numerical examples are provided to illustrate the effectiveness and efficiency of the solution.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Optimal sensor placement is key issues of structures health monitoring (SHM). In study of sensor placement, the main achievement focus on optimal criterions of sensor locations based on modal test, while optimal criterions of sensor locations based on damage identification, optimal method of sensor locations and optimal sensor number should be investigated further. In this study, a novel optimal sensor placement strategy based on sensitivity is proposed. Optimal sensor placement based on sensitivity analysis is an alternation method to consider damage identification. The basic idea of the proposed methodology is that influence range of different damage parameters is different. First, damage sensitivities in every element based on modal parameters are calculated. Then the elements that are sensitive to damage are selected. According to the detection of damage sensitivity in these elements, minimums number can be found by sensitivity. At last, the elements that are not selected are considered as not sensitive to modal parameters and would be placed the strain sensors. Numerical simulation of a three-dimensional truss structure is implemented to evaluate the minimum sensor number of different damage parameters according to the above methods. Moreover, damage location can be detected under singledamage situation and the element with most severe damage can be identified in multi-damage case using the proposed sensor placement.

A method to identify optimal strain sensor placement for examining structural static responses is presented. The method is based on the use of numerical optimization. Based on an assumed set of applied static forces, the optimal sensor placement can be obtained, and the measured strains can be used to provide the information needed to describe the structural stiffness. For example, the cross-sectional area can be determined by minimizing the difference between the analytical and measured strains. This approach is used to identify the optimized sensor placement. The objective of this study is to identify the minimum number of static strain sensors and the optimal sensor layout needed to evaluate a bridge’s structural condition. This study includes an automatic model parameter identification method, optimal static strain sensor placement, damage detection, and application to an actual bridge.

Deformation sensing and electrical compensation of smart skin antenna structure with optimal fiber Bragg grating strain sensor placements

BackgroundThe aim of this study was to evaluate the concept of a wearable device and, specifically: 1) to design and implement analysis procedures to extract clinically relevant information from data recorded using the wearable system; 2) to evaluate the design and placement of the strain sensors.MethodsDifferent kinds of trunk movements performed by a healthy subject were acquired as a comprehensive data set of 639 multivariate time series and off-line analyzed. The space of multivariate signals recorded by the strain sensors was reduced by means of Principal Components Analysis, and compared with the univariate angles contemporaneously measured by an inertial sensor.ResultsVery high correlation between the two kinds of signals showed the usefulness of the garment for the quantification of the movements’ range of motion that caused at least one strain sensor to lengthen or shorten accordingly. The repeatability of signals was also studied. The layout of a next garment prototype was designed, with additional strain sensors placed across the front and hips, able to monitor a wider set of trunk motor tasks.ConclusionsThe proposed technologies and methods would offer a low-cost and unobtrusive approach to trunk motor rehabilitation.

Introduction Monitoring biomechanics is crucial in sports and rehabilitation, and frontal knee angle is of special interest in these applications. Current solutions – optical motion capture (OMC), or inertial measurement units suits – are costly, spatially constrained, and impractical for use in daily life. Textile-based wearable systems are a valuable alternative for unobtrusive movement monitoring. Textile-based wearables for knee angle monitoring have mostly been used for sagittal angle prediction, however, frontal knee angle measurement is more challenging. We investigated the design and performance of a smart garment for the detection of knee joint angles in three planes during different activities. Methods We equipped a pair of tight pants with four helical auxetic yarn capacitive strain sensors (Cuthbert et al., 2022) placed close to the knees. The exact positioning was optimized with an OMC study: markers were placed in potential sensor locations (Gholami et al., 2019) and the pairs of markers whose distance had the highest mutual information with knee angles were selected for sensor placement. A healthy participant performed walking and turning around, and knee ab/adduction activities wearing the sensorized prototype. The latter activity emphasized knee motion in the frontal and transverse planes. The capacitances from the sensors were recorded with a custom electronics board that transmitted data wirelessly to a smartphone. Multiple regression algorithms were implemented to predict knee angles from the strain sensors data, with the ground truth obtained from the OMC data recorded simultaneously during the experiments. Results The optimal sensor placements were above the kneecaps, orientated as the vastus medialis and the rectus femoris. Xgboost regression algorithm yielded best performance for walking with root mean square errors (RMSE) of 10.79°, 3.77°, and 2.49° for the sagittal, frontal, and transverse angles, respectively. Linear regression performed the best for knee ab/adduction with RMSEs of 8.96°, 6.33°, and 1.58° for the sagittal, frontal, and transverse angles (Fornaciari, 2023). Discussion/Conclusion The smart garment system was overall able to track the knee angle in three planes. The larger errors, compared with previous works (Gholami et al., 2019), reported for the walking and turning around movement are likely because of high variations in the movements of the participants during turning around. Additionally, the proposed system showed capability to monitor frontal and transverse angles with an average RMSE of 3.5°. The larger error values of the sagittal angles are likely because of higher range of motion in that plane. The proposed system allows for continuous and unobtrusive knee angle monitoring outside of the laboratory settings in the comfortable form factor of smart clothing. References Cuthbert, T. J., Hannigan, B. C., Roberjot, P., Shokurov, A. V., &amp; Menon, C. (2022). HACS: Helical auxetic yarn capacitive strain sensors with sensitivity beyond the theoretical limit. Advanced materials, 35(10) Article 2209321. https://doi.org/10.1002/adma.202209321 Fornaciari, A. (2023). Wearable technology for lower limb movement monitoring [Master’s thesis]. Politecnico di Milano. Gholami, M., Rezaei, A., Cuthbert, T. J., Napier, C., &amp; Menon, C. (2019). Lower body kinematics monitoring in running using fabric-based wearable sensors and deep convolutional neural networks. Sensors, 19(23), Article 5325. https://doi.org/10.3390/s19235325

In this article, a microhabitat frog-leaping algorithm is proposed based on original shuffled frog-leaping algorithm and effective independence method to make the algorithm more efficient to optimize the 3-axis acceleration sensor configuration in the vibration test of structural health monitoring. Optimal sensor placement is a vital component of vibration test in structural health monitoring technique. Acceleration sensors should be placed such that all of the important information is collected. The resulting sensor configuration should be optimal such that the testing resources are saved. In addition, sensor configuration should be calculated automatically to facilitate engineers. However, most of the previous methods focus on the sensor placement of 1-axis sensors. Then, the 3-axis acceleration sensors are calculated by the method of 1-axis sensors, which results in non-optimal placement of many 3-axis acceleration sensors. Moreover, the calculation precisions and efficiencies of most of the previous methods cannot meet the requirement of practical engineering. In this work, the microhabitat frog-leaping algorithm is proposed to solve the optimal sensor placement problems of 3-axis acceleration sensors. The computation precision and efficiency are improved by microhabitat frog-leaping algorithm. Finally, microhabitat frog-leaping algorithm is applied and compared with other algorithms using Dalian South Bay Cross-sea Bridge.