This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in this special issue

Kok, Manon

McGinnis, Ryan S.

Seel, Thomas

Sensors

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sensorsEditorialInertial Sensors—Applications and Challenges ina NutshellThomas Seel 1,*,†, Manon Kok 2,† and Ryan S. McGinnis 3,†1 Control Systems Group, Technische Universität Berlin, 10587 Berlin, Germany2 Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands;m.kok-1@tudelft.nl3 Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA;Ryan.McGinnis@uvm.edu* Correspondence: thomas.seel@tu-berlin.de† These authors contributed equally to this work.Received: 29 October 2020; Accepted: 29 October 2020; Published: 31 October 2020Abstract: This editorial provides a concise introduction to the methods and applications of inertialsensors. We briefly describe the main characteristics of inertial sensors and highlight the broad rangeof applications as well as the methodological challenges. Finally, for the reader’s guidance, we give asuccinct overview of the papers included in this special issue.Keywords: accelerometers; gyroscopes; magnetometers; sensor fusion; body area networks; inertialmotion tracking; error modeling and calibration; localization and mapping; machine learning appliedto inertial sensor data; inertial sensors in vehicle motion estimation and control; inertial sensors inrobotics and manufacturing; inertial sensors in health care and sports engineeringInertial sensors have become a key enabling technology in a wide variety of applications, and arecurrently found in almost every digital device and intelligent vehicle. Inertial sensor information is,for instance, used to facilitate localization, navigation, and mapping [1–6]. Wearable inertial sensorsare also frequently used in health care and sporting applications for capturing movement patternsoutside of typical laboratory environments [7–10].Most of the large number of new applications strongly benefit from the hardware miniaturizationand advances in micro-electromechanical systems that have led to sensor designs with much smallerdimensions, energy demands, weights, and costs [9,11]. However, despite these decisive advantages,inertial sensors impose several substantial signal processing challenges, which we briefly summarizein the following.The measurements of an inertial measurement unit (IMU) are obtained in the moving coordinatesystem of the sensor, which complicates their time integration and direct interpretation [12].Three-dimensional strapdown integration is required, as well as suitable sensor fusion methods,since the measured angular rates, accelerations and magnetic field vectors rarely coincide with theactual motion states of interest, such as orientations, velocities or joint angles, or even more high-levelparameters. Moreover, the sensor coordinate systems are often not sufficiently well-aligned witha meaningful coordinate system of the object of interest, to which the sensor is attached [13,14].Furthermore, especially in biomechanics, this connection is often not completely rigid [5]. This has ledto a large interest in methods for non-restrictive sensor-to-segment calibration or anatomical calibrationon the one hand and soft-tissue motion artifact compensation on the other hand [6,15].Magnetic disturbances are another major challenge that frequently appear in wearable systemsand other applications, in particular in indoor environments. The magnetic field inside buildingsand near electronic devices or ferromagnetic material is known to be inhomogeneous to the extentSensors 2020, 20, 6221; doi:10.3390/s20216221 www.mdpi.com/journal/sensorsSensors 2020, 20, 6221 2 of 5that magnetometer measurements no longer yield accurate heading information [16]. Although theseinhomogeneities have been exploited to facilitate indoor navigation [17–19], they complicate theestimation of absolute orientations [20–22] and complete motion states in kinematic chains [23–26],and necessitate the use of additional models or measurement information.Finally, and likewise notably, the measurement signals of inertial sensors are well-known tobe corrupted by errors such as time-variant sensor biases and measurement noise [27]. Therefore,pure strap-down integration of the sensor signals to orientations and positions yields substantialintegration drift and erroneous results [12]. This challenge is commonly addressed by combininginertial measurements with complementary information provided by, for example, additional sensormeasurements or mathematical models [28,29]. This explains why, much more than for most othersensors, the use of inertial measurement units entails the employment of advanced sensor fusionmethods, kinematic models, mathematical methods and algorithms.In recent years, advanced sensor fusion and state estimation methods have been developed tocombine this information and minimize the influence of the sensor errors on the motion states ofinterest. Amongst these are filtering approaches, such as extended and unscented Kalman filters andcomplementary filters, which enable real-time estimation. Offline smoothing approaches have alsobeen developed that allow the use of all recorded data to improve estimation performance.In human motion tracking, it has become increasingly common to include additional informationfrom kinematic models to aid the estimation. Including these models overcomes the need for additionalmeasurement modalities (e.g., magnetometer measurements) to estimate the relative orientation ofdifferent body segments [23–26]. Furthermore, promising new directions of research focus on the useof sparse sensor setups, where fewer inertial sensors are placed on the body than the number of bodysegments of interest [30].Machine learning methods have also emerged as a promising new direction. These methodscan be used both for classification and regression. For instance, they can be used to classify humanactivities [7], detect disease [31] or risk status [32], or estimate continuous variables like walkingspeed [33] when deployed on inertial sensor data. They can also be used to learn the orientation of thesensor [34], for vehicle motion estimation [35] and for localization and mapping [36].The papers in this special issue cover a large variety of the outlined topics and recent progressin the methods and applications of inertial sensors. Both model-based and data-based approachesare considered on the methodological side, while the covered fields of applications include inertialnavigation [37], robotics [38–40], and the use of wearable inertial sensors for efficiently capturinghuman biomechanics, gestures, and activities [41–50].Three of the papers in this special issue find their motivation in the area of robotics, albeit fromdiverse perspectives. Problems tackled include orientation estimation for mobile robots [38], a controlscheme for stabilizing optical payloads in unmanned aerial vehicles [40], and trajectory estimationand mapping for ground robots [39]. Each of these topics target important engineering goals thatenhance the performance of these robots and leverage the complementary data sources inherent to allinertial sensors.Ten of the papers in this special issue present advances in the use of wearable inertial sensorsfor efficiently capturing human biomechanics, gestures and activities. Motivations for these studiesinclude enabling novel assessment and intervention, reducing communication loads for wirelesssensor networks, and improving human–computer interaction. Notably, several of the papers presentnew approaches for estimating knee [43] and hip [44] joint angles, and knee joint forces [47] that arevalidated in human subjects. The use of wearable inertial sensors for estimating human joint kinematicsis also explored in a systematic review [46]. These studies are important, as they add to the body ofevidence demonstrating the ability of wearable inertial sensors to provide objective measurements ofbiomechanical quantities important for characterizing human health.In summary, this special issue presents a broad collection of current research contributions to therapidly evolving field of inertial sensor methods and applications. Many of the presented works andSensors 2020, 20, 6221 3 of 5future works that will build thereon can be expected to help solve the aforementioned challenges andcontribute to the further spread and establishment of inertial sensor technologies in a wide range ofapplication domains.Funding: This research received no external fundingConflicts of Interest: The authors declare no conflict of interest.References1. Foxlin, E. Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graph. Appl. 2005, 25,38–46. [CrossRef]2. Angermann, M.; Robertson, P. FootSLAM: Pedestrian Simultaneous Localization and Mapping WithoutExteroceptive Sensors—Hitchhiking on Human Perception and Cognition. Proc. IEEE 2012, 100, 1840–1848.[CrossRef]3. Woodman, O.; Harle, R. Pedestrian localisation for indoor environments. In Proceedings of the 10thInternational Conference on Ubiquitous Computing; Association for Computing Machinery: New York, NY,USA, 2008; pp. 114–123, ISBN 978-1-60558-136-1.4. 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Global indoor self-localization based on the ambient magnetic field. Robot.Auton. Syst. 2009, 57, 1028–1035. [CrossRef]Sensors 2020, 20, 6221 4 of 519. Angermann, M.; Frassl, M.; Doniec, M.; Julian, B.J.; Robertson, P. Characterization of the indoor magneticfield for applications in Localization and Mapping. In Proceedings of the 2012 International Conference onIndoor Positioning and Indoor Navigation (IPIN), Sydney, Australia, 13–15 November 2012.20. Seel, T.; Ruppin, S. Eliminating the Effect of Magnetic Disturbances on the Inclination Estimates of InertialSensors. IFAC-Pap 2017, 50, 8798–8803. [CrossRef]21. Afzal, M.H.; Renaudin, V.; Lachapelle, G. Magnetic field based heading estimation for pedestrian navigationenvironments. In Proceedings of the 2011 International Conference on Indoor Positioning and IndoorNavigation, Guimaraes, Portugal, 21–23 September 2011.22. Vitali, R.V.; McGinnis, R.S.; Perkins, N.C. Robust Error-State Kalman Filter for Estimating IMU Orientation.IEEE Sens. 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Sensors 2020, 20, 890. [CrossRef] [PubMed]Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutionalaffiliations.© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Inertial Sensors—Applications and Challenges in a Nutshell

This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in this special issue.Team Jan-Willem van Wingerde

Seel, Thomas (author)

Kok, M. (author)

McGinnis, Ryan S. (author)

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Inertial sensors—applications and challenges in a nutshell

This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in this special issue.</p

Kok, M.

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Inertial Sensors—Applications and Challenges in a Nutshell

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