VQF: Highly Accurate IMU Orientation Estimation with Bias Estimation and Magnetic Disturbance Rejection

The miniaturization of inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. Orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position/velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Few existing publications systematically compare multiple algorithms on a broad collection of experimental data, and those publications show that out-of-the-box accuracy of existing algorithms is often low and that application-specific tuning is required. In the present work, we propose and extensively evaluate an orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform a comprehensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all literature methods and achieves an average RMSE of 2.9{\deg}, while the errors obtained with literature methods range from 5.3{\deg} to 16.7{\deg}. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method

A Tangible Solution for Hand Motion Tracking in Clinical Applications

Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. The proposed system addresses the specific requirements of clinical applications in two ways: (1) In contrast to glove-based approaches, the proposed solution maintains the sense of touch. (2) In contrast to previous work, the proposed methods avoid the use of complex calibration procedures, which means that they are suitable for patients with severe motor impairment of the hand. To overcome the limited significance of validation in lab environments with homogeneous magnetic fields, we validate the proposed system using functional hand motions in the presence of severe magnetic disturbances as they appear in realistic clinical settings. We show that standard sensor fusion methods that rely on magnetometer readings may perform well in perfect laboratory environments but can lead to more than 15 cm root-mean-square error for the fingertip distances in realistic environments, while our advanced method yields root-mean-square errors below 2 cm for all performed motions.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Relationship between abundance of juvenile rockfishes (Sebastes spp.) and environmental variables documented off northern California and potential mechanisms for the covariation

We estimated annual abundance of juvenile blue (Sebastes mystinus), yellowtail (S. f lavidus), and black (S. melanops) rockfish off northern California over 21 years and evaluated the relationship of abundance to oceanographic variables (sea level anomaly, nearshore temperature, and offshore Ekman transport). Although mean annual abundance was highly variable (0.01−181 fish/minute), trends were similar for the three species. Sea level anomaly and nearshore temperature had the strongest relationship with interannual variation in rockfish abundance, and offshore Ekman transport did not correlate with abundance. Oceanographic events occurring in February and March (i.e., during the larval stage) had the strongest relationship with juvenile abundance, which indicates that year-class strength is determined during the larval stage. Also of note, the annual abundance of juvenile yellowtail rockfish was positively correlated with year-class strength of adult yellowtail rockfish; this finding would indicate the importance of studying juvenile abundance surveys for management purposes

Modular finger and hand motion capturing system based on inertial and magnetic sensors

The assessment of hand posture and kinematicsis increasingly important in various fields. This includesthe rehabilitation of stroke survivors with restricted handfunction. This paper presents a modular, ambulatory mea-surement system for the assement of the remaining handfunction and for closed-loop controlled therapy. The de-vice is based on inertial sensors and utilizes up to fiveinterchangeable sensor strips to achieve modularity and tosimplify the sensor attachment. We introduce the modularhardware design and describe algorithms used to calculatethe joint angles. Measurements with two experimentalsetups demonstrate the feasibility and the potential of such a tracking device.BMBF, 16SV7069K, Verbundprojekt: Bewegungsfähigkeit und Mobilität wiedererlangen - BeMobil -; Teilvorhaben: Nutzerzentrierte Entwicklung technischer Methoden für eine optimale Mensch-Technik-Interaktion in der Bewegungsrehabilitatio

Calibration-free gait assessment by foot-worn inertial sensors

Walking is a central activity of daily life, and there is an increasing demand for objective measurement-based gait assessment. In contrast to stationary systems, wearable inertial measurement units (IMUs) have the potential to enable non-restrictive and accurate gait assessment in daily life. We propose a set of algorithms that uses the measurements of two foot-worn IMUs to determine major spatiotemporal gait parameters that are essential for clinical gait assessment: durations of five gait phases for each side as well as stride length, walking speed, and cadence. Compared to many existing methods, the proposed algorithms neither require magnetometers nor a precise mounting of the sensor or dedicated calibration movements. They are therefore suitable for unsupervised use by non-experts in indoor as well as outdoor environments. While previously proposed methods are rarely validated in pathological gait, we evaluate the accuracy of the proposed algorithms on a very broad dataset consisting of 215 trials and three different subject groups walking on a treadmill: healthy subjects (n = 39), walking at three different speeds, as well as orthopedic (n = 62) and neurological (n = 36) patients, walking at a self-selected speed. The results show a very strong correlation of all gait parameters (Pearson's r between 0.83 and 0.99, p < 0.01) between the IMU system and the reference system. The mean absolute difference (MAD) is 1.4 % for the gait phase durations, 1.7 cm for the stride length, 0.04 km/h for the walking speed, and 0.7 steps/min for the cadence. We show that the proposed methods achieve high accuracy not only for a large range of walking speeds but also in pathological gait as it occurs in orthopedic and neurological diseases. In contrast to all previous research, we present calibration-free methods for the estimation of gait phases and spatiotemporal parameters and validate them in a large number of patients with different pathologies. The proposed methods lay the foundation for ubiquitous unsupervised gait assessment in daily-life environments.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Berlin Robust Orientation Estimation Assessment Dataset (BROAD)

Data files and example code for the BROAD benchmark for inertial orientation estimation. For more details, see the following publication: D. Laidig, M. Caruso, A. Cereatti, T. Seel. BROAD -- A Benchmark for Robust Inertial Orientation Estimation. Submitted to Data. A copy of this dataset is also available at https://github.com/dlaidig/broad

Deriving kinematic quantities from accelerometer readings for assessment of functional upper limb motions

Wearable accelerometers are lightweight, affordable, and allow for even smaller form factors than 9D inertial measurement units. They are therefore a promising tool for assessing the quality of movement of patients during daily life activities. While generic signal features such as signal power and frequency content are widely used, the derivation of kinematic (angular and spatial) quantities remains a challenge. We consider a chain of body segments, such as the arm, equipped with 3D accelerometers and propose a method for calculation of the inclination and relative height of the distal segment. For validation of the method against an optical motion capture system, we consider a setup with accelerometers on the forearm and the upper arm of a subject, who performs a sequence of drinking motions and pick-and-place motions. We obtain a root-mean-square deviation of about 2.5 cm for the wrist height relative to the shoulder and about 6° for the inclination angles of the forearm. We conclude that the proposed method yields measurements of kinematic quantities that are accurate enough for classification of functional versus non-functional motions or well-performed motions versus incomplete motions

Automatic anatomical calibration for IMU-based elbow angle measurement in disturbed magnetic fields

Inertial Measurement Units (IMUs) are increasingly used for human motion analysis. However, two major challenges remain: First, one must know precisely in which orientation the sensor is attached to the respective body segment. This is commonly achieved by accurate manual placement of the sensors or by letting the subject perform tedious calibration movements. Second, standard methods for inertial motion analysis rely on a homogeneous magnetic field, which is rarely found in indoor environments. To address both challenges, we introduce an automatic calibration method for joints with two degrees of freedom such as the combined radioulnar and elbow joint. While the user performs arbitrary movements, the method automatically identifies the sensor-to-segment orientations by exploiting the kinematic constraints of the joint. Simultaneously, the method identifies and compensates the influence of magnetic disturbances on the sensor orientation quaternions and the joint angles. In experimental trials, we obtain angles that agree well with reference values from optical motion capture. We conclude that the proposed method overcomes mounting and calibration restrictions and improves measurement accuracy in indoor environments. It therefore improves the practical usability of IMUs for many medical applications