Sensor fusion for estimating ground reaction force using insole-type foot pressure and inertial sensors

Sensors and measurement systems is an introduction to microsensors for engineering students in the final undergraduate or early graduate level, technicians who wants to know more about the systems they are using, and anybody curious enough to know what microsystems and microsensors can do. The book discusses five families of sensors: - Thermal sensors - Force and pressure sensors- Inertial sensors - Magnetic field sensors- Flow sensorsFor each sensor, theoretical, technology and application aspects are examined. The sensor function is modelled to understand sensitivity, resolution and noise. We ask ourselves: What do we want to measure? What are possible applications? How are the sensor chips made in the cleanroom? How are they mounted and integrated in a system?After reading this book, you should be able to:- Understand important thermal, mechanical, inertial and magnetic sensors- Work with characterization parameters for sensors- Choose sensors for a given application and apply them- Understand micromachining technologies for sensors

The purpose of this research was to propose a method to acquire the three-dimensional movement of a roller skiing athlete utilizing a global positioning system (GPS), an atmospheric pressure sensor (APS), and an inertial measurement unit (IMU) to distinguish between diagonal skiing and double poling techniques. In this study, we developed a lightweight wearable sensor that was composed of a GPS, APS, and IMU. For the data collection, two national level athletes roller skied a 1700 m long course where the maximum difference in elevation was 5.3 m. The planar positions of the athletes were based on the longitudinal and latitudinal data provided by the GPS, and the altitude was calculated by the International barometric formula and Laplace transform from the APS. The longitudinal and latitudinal data acquired through GPS had errors within 2 m range at each point. The altitude of Laplace transform from the APS showed errors of less than 1 m at each point. The range of motion of pelvis from the IMU was 34.0 ± 3.8° for diagonal skiing, which is wider than the 21.2 ± 6.9° for double poling.

Background/Purpose: The acquisition of three-dimensional ground reaction forces (3D-GRF) during running is crucial for monitoring and assessing running performance. However, limitations often arise in collecting 3D-GRF due to equipment and environmental constraints. Inertial sensors are gaining popularity in running applications because of their convenience, broad applicability, and cost-effectiveness. This study aimed to utilize an inertial sensor for gathering kinematic data during running, establish a high-precision neural network model for continuous 3D-GRF estimation, and investigate estimation variances across different body segments. Method: A total of thirty-three college students (17 male, 16 female) participated in this study. Kinematic and kinetic data were concurrently recorded using inertial sensors placed on the lower back, lateral thigh, anterior lower leg, and dorsum of the foot, while three-dimensional force plates were utilized during running at speeds of 8/10/12 km/h. The dataset was partitioned into training, validation, and test sets at a ratio of 24:3:6. A Long Short-Term Memory Neural Network (LSTM) was developed for training to predict 3D-GRF. Evaluation metrics included coefficients of determination, Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Models customized for specific body segments underwent a nine-fold cross-validation procedure, employing one-way ANOVA to evaluate estimation variances across different anatomical sites. Results: Estimation outcomes at different sites indicated optimal estimation accuracy at the right thigh. The coefficients of determination for Anteroposterior (AP)-GRF, Mediolateral (ML)-GRF, and Vertical (V)-GRF were 0.81±0.00, 0.55±0.14, and 0.97±0.02, respectively. RMSE values were 0.06±0.01BW, 0.06±0.01BW, and 0.14±0.03BW. Significant variances were observed between the right thigh and other sites in the horizontal plane (p < 0.05). The results from the test set showed correlation coefficients, RMSE, and NRMSE for AP-GRF, ML-GRF, and V-GRF: AP-GRF, 0.94±0.01, 0.06±0.01BW, 4.78%±0.50%; ML-GRF, 0.83±0.08, 0.10±0.01BW, 9.70%±2.41%; V-GRF, 0.99±0.00, 0.14±0.02BW, 2.79%±0.24%. Conclusion/Discussion: 1. The neural network model developed in this study demonstrated exceptional accuracy across three dimensions, facilitating continuous estimation of 3D ground reaction forces during running. Correlation coefficients surpassed 0.8 in every direction, with RMSE values below 0.2BW and NRMSE below 5%. 2. Estimation results from inertial sensors positioned at different sites exhibited diverse levels of disparity. The lateral thigh emerged as the most proficient location for estimation, with the lower back excelling in vertical ground reaction force estimation but displaying comparatively weaker performance in horizontal ground reaction force estimation.

Since the center of mass (CoM) is related to ground reaction force (GRF), the CoM position is expected to be used in evaluation of walking ability. The purpose of this study was to examine the possibilities of using the CoM acceleration calculated from the CoM trajectory estimated by inertial sensor signals to obtain similar information as the GRF. In this paper, 16 m straight walking measurements were performed under different walking speeds with healthy subjects. CoM acceleration, which was calculated from CoM positions estimated by signals measured with inertial sensors, were analyzed for the steady state gait. Waveforms of the CoM acceleration were similar among subjects. Four peak values (negative peak near the toe off (TO) and positive peak near the initial contact (IC) of the opposite lower limb in the anteroposterior component, and the positive peak before the TO of the opposite lower limb and the negative peak of the mid-stance phase) showed significant correlations with walking speed, which were similar to the relationships between GRF and walking speed in previous studies. These results suggest that anteroposterior and vertical components of CoM acceleration estimated from inertial sensor signals have a possibility of estimating similar information as the GRF during walking. The relationship between positive peak value of the vertical component near the IC of the opposite side and walking speed was seemed to be different from a previous study, although it showed significant correlation with walking speed. Simultaneous measurements with inertial sensors and GRF are expected in the next study.

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95–96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.

With NASA's current trajectory under the Commercial Crew program, spacesuits will become a critical system for the successful implementation of commercial orbital transportation services (COTS). All the current flown spacesuits are gas pressurized and require astronauts to exert a substantial amount of energy in order to move the suit into a desired position. The pressurization of the suit therefore limits human mobility, causes discomfort, and leads to a variety of contact and strain injuries. While suit-related injuries have been observed for many years and some basic countermeasures have been implemented, there is still a lack of understanding of how humans move within the spacesuit. The objective of this research is to gain a greater understanding of this human-spacesuit interaction and potential for injury by analyzing the suit-induced pressures against the body along with joint kinematics of how astronauts move inside the space suit. The rise of wearable technologies is changing the paradigm of biomechanics and allowing a continuous monitoring of motion performance in fields like athletics or medical rehabilitation. Similarly, pressure sensors allow an in-suit sensing capability to better locate the areas of contact between the human and the suit and reduce the risk of injuries. Coupled together these sensors allow a better understanding of the complex interactions between the astronaut and his suit, enhance astronaut's performance through a real time monitoring and reducing the risk of injury. An experiment was conducted in conjunction with David Clark Incorporated Company on the Launch Entry Development spacesuit. The experiment analyzed the mobility and human-spacesuit system behavior for isolated and functional upper body movement tasks, with each motion repeated 15 times: elbow flexion/extension, shoulder flexion/extension, shoulder abduction/adduction and cross body reach, which is a complex succession of critical motions for astronaut and pilot task. The contact pressure between the person and the spacesuit was measured by a high-pressure sensor located on the shoulder (Novel). Joint angles were measured internally and externally to the suit with 6 inertial measurement units (Opal IMUs): three external and three internal. The spacesuit was tested in its natural recumbent position. The analysis of the mobility of the spacesuit, and the interactions between the suit and the person are analyzed and conclusion and recommendations are given.

<p>Dynamic performance of footbridges is still a great concern to designers, operators and users, with many structures requiring investigation before, during and after construction to manage performance.</p> <p>We have been investigating the use of wireless inertial measurement units (IMUs) designed for biomechanics, health and sports science application for estimating human dynamic loads or ground reaction forces (GRFs) on structures. The aim has been to move from direct measurements using force plates and treadmills, via optical motion capture in the laboratory (with application of Newton's Second law), to unconstrained field conditions. Initially we used IMUs to evaluate pedestrian synchronisation, but we found that a single IMU attached to the C7 neck vertebra can provide a remarkably accurate estimate of vertical GRF. With an ability to communicate and synchronise within a group wirelessly, to identify orientation and transform accelerations into world coordinates, IMUs can identify both the GRF force vectors and their time varying location with a moving pedestrian. As a side-benefit, the signal to noise ratio and synchronisation accuracy are sufficient to enable low-cost wireless footbridge ambient vibration testing and monitoring. So far we have used IMUs for ambient and forced vibration testing (the latter using a human shaker), moving pedestrian load and response measurement and crowd tracking. There are many more possibilities.</p>

This paper presents a new method for 3D position and orientation estimation based on the magnetic field generated by a single coil and the fusion of low power magnetometers and inertial sensors. The fixed spatial relationship between the sensors is exploited to solve the ambiguity problem that occurs when only a single coil is employed. In this context a new positioning algorithm is developed and verified by simulations. Using a single coil instead of three coils as common in conventional magnetic field based systems brings a benefit both regarding power consumption and construction size. Moreover, as the proposed system requires no line of sight between source and sensors, it is ideally suited for portable, location-independent applications such as the mobile assessment of a persons' head to torso posture and movement e.g. for the purpose of examining causes for chronic pain in the cervical spine area under conditions of daily life.

Inertial measurement units (IMU) are included in uncountable consumer electronic products, e.g. smart phones. IMUs are state of the art for research in human motion analysis, context and activity recognition. Sensor setups to estimate the activity of a subject, e.g. standing, sitting, walking, climbing stairs etc., typically require a multitude of sensor nodes to provide reliable estimations. In our paper, we present a sensor system for enhanced context and activity recognition while requiring a minimal set of sensor nodes. Our system combines a pressure sensor featuring high spatial resolution (1248 sensitive points) with an IMU. The pressure sensor is typically placed inside a shoe and measures the force exerted on the feet. Our system eases the detection of many situations: differentiating between sitting and standing, walking and climbing stairs etc. It is adjustable to virtually any shoe and therefore could be included in projects requiring specialized footwear (e.g. running, weight lifting or biking shoes). Data can be stored on the sensor node or it can be transferred wirelessly to a remote aggregator device, i.e. a device responsible for data stream reception, sensor control and analysis. The aggregator can be implemented in a smart phone or a PC. Our system can in real-time detect situations of sitting, standing and walking automatically. The system's low-power implementation allows for run-times exceeding 14 hours. In a multi-system setup the systems can be synchronized with a node-to-node offset in the order of 10μs. The pressure can be sampled at a maximum of 120Hz, the inertial data is available at a rate of 512 Hz. Our small-footprint real-time classifier can distinguish between the situations standing, sitting, and walking with an accuracy above 99%.

This study reports on a wave height measurement system that combines a micro-electromechanical system (MEMS)-based differential pressure (DP) sensor with an inertial measurement unit (IMU). The proposed system integrates a DP sensor with a mechanical bandpass filter and an IMU to achieve high temporal resolution and high accuracy. Combinatorial linear complementation of the sensor outputs enables the system to compensate for IMU-based errors such as integral drift and uncertainty in gravity direction estimation. In sea experiments, the correlation coefficient with commercial GPS sensor values increased from 0.874 to 0.876. This confirmed the effectiveness of the proposed system and the correction process.

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.

Accuracy and tracking update rates play a vital role in determining the quality of Augmented Reality(AR) and Virtual Reality(VR) applications. Applications like soldier training, gaming, simulations & virtual conferencing need a high accuracy tracking with update frequency above 20Hz for an immersible experience of reality. Current research techniques combine more than one sensor like camera, infrared, magnetometers and Inertial Measurement Units (IMU) to achieve this goal. In this paper, we develop and validate a novel algorithm for accurate positioning and tracking using inertial and vision-based sensing techniques. The inertial sensing utilizes accelerometers and gyroscopes to measure rates and accelerations in the body fixed frame and computes orientations and positions via integration. The vision-based sensing uses camera and image processing techniques to compute the position and orientation. The sensor fusion algorithm proposed in this work uses the complementary characteristics of these two independent systems to compute an accurate tracking solution and minimizes the error due to sensor noise, drift and different update rates of camera and IMU. The algorithm is computationally efficient, implemented on a low cost hardware and is capable of an update rate up to 100 Hz. The position and orientation accuracy of the sensor fusion is within 6mm & 1.5°. By using the fuzzy rule sets and adaptive filtering of data, we reduce the computational requirement less than the conventional methods (such as Kalman filtering). We have compared the accuracy of this sensor fusion algorithm with a commercial infrared tracking system. It can be noted that outcome accuracy of this COTS IMU and camera sensor fusion approach is as good as the commercial tracking system at a fraction of the cost.

Many species of fish use the lateral line for underwater information extraction. In spite of the extensive work on artificial lateral line (ALL) and inertial measurement unit (IMU), the ALL and IMU are rarely used together to estimate the speed of robotic fish. Here we show that an artificial lateral line and an inertial measurement unit can be used cooperatively to estimate the speed of a swimming robotic fish. Based on the analysis of the robotic fish, we use an optimal information fusion decentralized filter algorithm to efficiently fuse the information of ALL and IMU for speed estimation. Our robotic fish has an artificial lateral line consisting of 11 pressure sensors and an inertial measurement unit. Experiments conducted with the freely swimming robotic fish demonstrate that the proposed scheme is able to efficiently estimate the robot speed with small errors in real time.

Here we introduce a motion tracking or navigation module for medical simulation systems. Our main contribution is a sensor fusion method for proximity or distance sensors integrated with inertial measurement unit (IMU). Since IMU rotation tracking has been widely studied, we focus on the position or trajectory tracking of the instrument moving freely within a given boundary. In our experiments, we have found that this module reliably tracks instrument motion.

In this paper, we present a wireless micro inertial measurement unit (IMU) with the smallest volume and weight requirements available at the moment. With a size of 22 mm × 14 mm × 4 mm (1.2 cm3), this IMU provides full control over the data of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. It meets the design prerequisites of a space-saving design and eliminates the need for hard-wired data communication, while still being competitive with state-of-the-art commercially available MEMS IMUs. A CC430 microcontroller sends the collected raw data to a base station wirelessly with a maximum sensor sample rate of 640 samples/s. Thereby, the IMU performance is optimized by moving data post processing to the base station. This development offers important features in portable applications with their significant size and weight requirements. Due to its small size, the IMU can be integrated into clothes or shoes for accurate position estimation in mobile applications and location-based services. We demonstrate the performance of the wireless micro IMU in a localization experiment where it is placed on a shoe for pedestrian tracking. With sensor data-fusion based on a Kalman filter combined with the zero velocity update, we can precisely track a person in an indoor area.