Military applications of soldier physiological monitoring.

In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems.

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework.

A foot-mounted pedestrian navigation system (PNS) that uses MEMS inertial measurement units (IMUs) can track the position of a person when walking normally on two-dimensional (2D) plane. However, the positioning accuracy is decreased under complex gaits. This article proposes a pedestrian navigation method based on gait classification for three-dimensional (3D) positioning. The commonly used gaits are divided into seven types and a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) is constructed as gait classifier. When the detected gaits are walking sideways or running, the step length models are built to calculate single step length. Otherwise the inertial navigation algorithm with zero velocity update (ZUPT) is used to calculate travel distance. If the pedestrian is going upstairs or downstairs, a barometer together with stair height constraint is applied to obtain altitude variation. Meanwhile, the heading drift is reduced combining zero integrated heading rate (ZIHR) method and simplified heuristic drift reduction (HDR) method. The gait classification results show that the classification accuracy of designed BLSTM-RNN reaches 99.4%. The results of mixed gaits experiment and multi-floor experiment show that the positioning errors of 2D and 3D trajectories are less than 2%.

Foot-Mounted Pedestrian Navigation Method Based on Gait Classification for Three-Dimensional Positioning

Wearable motion tracking systems are becoming increasingly popular in human-machine interfaces. For inertial measurement, it is vital to efficiently fuse inertial, gyroscopic, and magnetometer data for spatial orientation. We introduce a new algorithm for this fusion based on using gradient descent to correct for the integral error in calculating the orientation quaternion of a rotating body. The algorithm is an improved formulation of the well-known estimation of orientation using a gradient descent algorithm. The new formulation ensures that the gradient descent algorithm uses the steepest descent, resulting in a five order of magnitude increase in the precision of the calculated orientation quaternion. We have also converted the algorithm to use fixed point integers instead of floating point numbers to more than double the speed of the calculations on the types of processors used with Inertial Measurement Units (IMUs) and Magnetic, Angular Rate and Gravity sensors (MARGs). This enables the corrections to not only be faster than the original formulations, but also remain valid for a larger range of inputs. The improved efficiency and accuracy show significant potential for increasing the scope of inertial measurement in applications where low power or greater precision is necessary such as very small wearable or implantable systems.

Improved formulation of the IMU and MARG orientation gradient descent algorithm for motion tracking in human-machine interfaces

Inertial sensors with microelectromechanical systems technology are an integral part of many modern electronic devices such as wearable medical products, which are inherently subject to memory, bandwidth, and energy constraints due to their size and purpose. One of the important challenges for the progress in this area is the storage, transmission, and processing of large quantities of inertial sensors signal. To address this issue, this paper presents a method for near-lossless compression of multi-axis inertial signals. To improve the inertial signal compression capability, the proposed compression method employs the independent component analysis method with a principal component analysis preprocessing step to extract independent components from the signals. A deep autoencoder is used to compress the independent components and later to estimate them in the reconstruction phase. The reconstruction error is also quantized and coded using arithmetic coding and transmitted alongside the compressed components. This paper also proposes a new approach for improving the quality of the reconstructed signals. In this approach, on the receiver side, the reconstruction error is fed to the Madgwick filter as an external noise and is compensated using this filter. The experimental results demonstrate the high compression rate and low reconstruction error of the proposed method compared to the state-of-the-art methods.

A Deep Learning-Based Compression Algorithm for 9-DOF Inertial Measurement Unit Signals Along With an Error Compensating Mechanism

Even though technology-aided personal navigation is an extensively studied research topic, approaches based on inertial sensors remain challenging. In this study, the authors present a comparison between different inertial systems, investigating the impacts of on-body placement of Inertial Measurement Units (IMUs) and, consequently, of different algorithms for the estimation of the travelled path on the navigation accuracy. In particular, the system performance is investigated considering two IMU placements: (i) on the feet and (ii) on the lower back. Sensor fusion is then considered in order to take advantage of the strengths of each placement. The results are validated through an extensive data collection in indoor and outdoor environments.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-wss.2017.0087

Impact of on-body IMU placement on inertial navigation

This paper describes the development of a multifloor hybrid inertial/barometric navigation system. The prototype integrates several Magnetic Angular Rate and Gyroscope (MARG) sensors and a barometer. The inertial (MARG) sub-system, by properly processing the signals collected by the MARG sensors placed on the test subject's feet, reconstructs the two-dimensional navigation pattern by applying a Zero velocity UPdaTe (ZUPT) technique. Three different sensors' configurations are investigated in order to find the best performing set-up. A simpler configuration with a single MARG sensor is also considered to derive a reference performance benchmark without multiple MARG sensor fusion. The barometer, connected via usb to a Freakduino board, is used to detect the floor change. The fusion of inertial and barometric signals allows to fully reconstruct the movement of a person in both indoor and outdoor environments. The main goal of the proposed system is to allow accurate personal navigation without any external reference (i.e., radio signals, satellite signals, etc.). Considering a closed path, the relative distance error between the starting point and the final estimated position is below 2.5% of the total traveled distance.

A multifloor hybrid inertial/barometric navigation system

In this paper, we compare two novel algorithms for pedestrian navigation based on signals collected by a single wearable Magnetic, Angular Rate, and Gravity (MARG) sensor. The two navigation algorithms, denoted as Enhanced Pedestrian Dead Reckoning (EPDR) and De-Drifted Propagation (DDP), require the placement of the MARG sensor on the foot or on the chest of the test subject, respectively. Different methods for gait characterization are compared, evaluating navigation dynamics by using data collected through an extensive experimental campaign. The main goal of this research is to investigate the peculiarities of different inertial navigation algorithms, in order to highlight the impact of the sensor's placement, together with inertial sensor issues. Considering a closed path (i.e., ending at the starting point), the relative distance error between the starting point and the final estimated position is about 2% of the total travelled distance for both DDP and EPDR navigation algorithms. On the other hand, the error between the initial heading angle and the final estimated one is approximately 10° for EPDR and 7° for DDP, respectively.

On single sensor-based inertial navigation

In this paper, we present an innovative inertial navigation system based on the data collected through the Inertial Measurement Unit (IMU) embedded in a commercial smart-phone. We propose an innovative step detection algorithm which is independent of the holding mode, the only assumption being that the device is hand-held (i.e., the user is texting/navigating or phoning) and its movement is related to the upper body displacement during walking. We also present a new approach able to automatically calibrate the step length estimation formula according to the smartphone positioning. The developed algorithms have been validated through a test campaign in which we have evaluated the system performance considering three different smartphone models and different path lengths. The obtained results show that the maximum step detection error is always below 4% (average: 2.08%; standard deviation: 1.82%) whereas the maximum path length estimation error is below 8.1% (average: 3.6%; standard deviation: 1.81%) in all the considered cases.

A Novel Step Detection and Step Length Estimation Algorithm for Hand-held Smartphones

In this article, a performance analysis on the estimation of the so-called observation filter for the Virtual Microphone Technique (VMT) in a realistic automotive environment is presented. A performance comparison between adaptive and fixed observation filter estimation methods, namely Least Mean Square (LMS) and Minimum Mean Square Error (MMSE), respectively, was carried on. Two different experimental setups were implemented on a popular B-segment car. Eight microphones were placed at the monitoring and virtual positions in order to sense environmental acoustic noise propagating within the cabin of the car running at variable speed on a smooth asphalt. Our experimental results show that a large spectral coherence between monitoring and virtual microphone signals indicates a potentially effective and relatively wide-band virtual microphone signal reconstruction. The fixed observation filter estimation method achieves better performance than the adaptive one, guaranteeing remarkable broadband estimation accuracy. Moreover, for each considered setup, design guidelines are proposed to obtain a good trade-off between estimation accuracy and material costs.

Nicolo Strozzi

Papers

Impact of on-body IMU placement on inertial navigation

A multifloor hybrid inertial/barometric navigation system

On single sensor-based inertial navigation

A Novel Step Detection and Step Length Estimation Algorithm for Hand-held Smartphones

Experimental Analysis and Design Guidelines for Microphone Virtualization in Automotive Scenarios