Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.

This paper revisits the stationary attitude initialization problem, i.e., the stationary alignment, of Attitude and Heading Reference Systems (AHRSs). A detailed and comprehensive error analysis is proposed for four of the most representative accelerometer- and magnetometer-based stationary attitude determination methods, namely, the Three-Axis Attitude Determination (TRIAD), the QUaternion ESTimator (QUEST), the Factored Quaternion Algorithm (FQA), and the Arc-TANgent (ATAN). For the purpose of the error analysis, constant biases in the accelerometer and magnetometer measurements are considered (encompassing, hence, the effect of hard-iron magnetism), in addition to systematic errors in the local gravity and Earth magnetic field models (flux density magnitude, declination angle, and inclination angle). The contributions of this paper are novel closed-form formulae for the residual errors (normality, orthogonality, and alignment errors) developed in the computed Direction Cosine Matrices (DCM). As a consequence, analytical insight is provided into the problem, allowing us to properly compare the performance of the investigated alignment formulations (in terms of ultimate accuracy), as well as to remove some misleading conclusions reported in previous works. The adequacy of the proposed error analysis is validated through simulation and experimental results.

The main purpose of this paper is design and implementation of a new linear observer for an attitude and heading reference system (AHRS), which includes three-axis accelerometers, gyroscopes, and magnetometers in the presence of sensors and modeling uncertainties. Since the increase of errors over time is the main difficulty of low-cost micro electro mechanical systems (MEMS) sensors producing instable on-off bias, scale factor (SF), nonlinearity and random walk errors, development of a high-precision observer to improve the accuracy of MEMS-based navigation systems is considered. First, the duality between controller and estimator in a linear system is presented as the base of design method. Next, Legendre polynomials together with block-pulse functions are applied for the solution of a common linear time-varying control problem. Through the duality theory, the obtained control solution results in the block-pulse functions and Legendre polynomials observer (BPLPO). According to product properties of the hybrid functions in addition to the operational matrices of integration, the optimal control problem is simplified to some algebraic equations which particularly fit with low-cost implementations. The improved performance of the MEMS AHRS owing to implementation of BPLPO has been assessed through vehicle field tests in urban area compared with the extended Kalman filter (EKF).

The nonlinear problem of sensing the attitude of a solid body is solved by a novel implementation of the Kalman Filter. This implementation combines the use of quaternions to represent attitudes, time-varying matrices to model the dynamic behavior of the process and a particular state vector. This vector was explicitly created from measurable physical quantities, which can be estimated from the filter input and output. The specifically designed arrangement of these three elements and the way they are combined allow the proposed attitude estimator to be formulated following a classical Kalman Filter approach. The result is a novel estimator that preserves the simplicity of the original Kalman formulation and avoids the explicit calculation of Jacobian matrices in each iteration or the evaluation of augmented state vectors.

There are limited structural brain and retina alteration data from schizophrenia patients who experience visual disturbances (VDs) with or without auditory hallucinations (AHs). We compared brain and retina alterations between first-episode untreated schizophrenia patients with VDs (FUSCH-VDs) with versus without AHs, and between patients and healthy controls (HCs)(N = 30/group). VDs, AHs, gray matter volumes (GMVs), and retinal thicknesses were evaluated with the Bonn Scale for Assessment of Basic Symptoms (BSABS) scale, the Auditory Hallucinations Rating Scale (AHRS), magnetic resonance imaging (MRI), and optical coherence tomography (OCT), respectively. Compared to HCs, FUSCH-VDs had reduced GMVs, mainly in dorsal V3/V3A and V5 regions, the fusiform gyrus, and ventral V4 and V8 regions. Most FUSCH-VDs (85.0%; 51/60) had primary visual cortex-retina co-impairments. FUSCH-VDs with AHs had more serious and larger scope GMV reductions than FUSCH-VDs without AHs. FUSCH-VDs with AHs had significant retinal thickness reductions compared to HCs. Primary visual cortex-retina co-impairments were found to be more common, and more pronounced when present, in FUSCH-VDs with AHs than in FUSCH-VDs without AHs. The present findings support the notion that VDs and AHs may have reciprocal deteriorating actions in patients with schizophrenia.

The global positioning system (GPS) is an essential technology that provides positioning capabilities and is used in various applications such as navigation, surveying, mapping, robot simultaneous localization and mapping (SLAM), location-based service (LBS), etc. However, the GPS is known to be vulnerable to intentional attacks such as spoofing because of its simple signal structure. In this study, a direct method is proposed for GPS spoofing detection, using Attitude and Heading Reference System (AHRS) accelerometer and analyzing the detection performance with corresponding probability density functions (PDFs). The difference in the acceleration between the GPS receiver and the accelerometer is used to detect spoofing. The magnitude of the acceleration error may be used as a decision variable. Additionally, using the magnitude of the north (or east) component of the acceleration error as another decision variable is proposed, which shows better performance in some conditions. The performance of the two decision variables is compared by calculating the probability of spoofing detection and the detectable minimum spoofing acceleration (DMSA), given a pre-defined false alarm probability and a pre-defined detection probability. It turns out that both decision variables need to be used together to obtain the best spoofing detection performance.

Real-time kinematic (RTK) technique is widely used in modern society because of its high accuracy and real-time positioning. The appearance of Android P and the application of BCM47755 chipset make it possible to use single-frequency RTK and dual-frequency RTK on smartphones. The Xiaomi Mi 8 is the first dual-frequency Global Navigation Satellite System (GNSS) smartphone equipped with BCM47755 chipset. However, the performance of RTK in urban areas is much poorer compared with its performance under the open sky because the satellite signals can be blocked by the buildings and trees. RTK can\'t provide the positioning results in some specific areas such as the urban canyons and the crossings under an overpass. This paper combines RTK with an IMU-based pedestrian navigation algorithm. We utilize attitude and heading reference system (AHRS) algorithm and zero velocity update (ZUPT) algorithm based on micro electro mechanical systems (MEMS) inertial measurement unit (IMU) in smartphones to assist RTK for the sake of improving positioning performance in urban areas. Some tests are carried out to verify the performance of RTK on the Xiaomi Mi 8 and we respectively assess the performances of RTK with and without the assistance of an IMU-based pedestrian navigation algorithm in urban areas. Results on actual tests show RTK with the assistance of an IMU-based pedestrian navigation algorithm is more robust and adaptable to complex environments than that without it.

Within this work we present AMiCUS, a Human-Robot Interface that enables tetraplegics to control a multi-degree of freedom robot arm in real-time using solely head motion, empowering them to perform simple manipulation tasks independently. The article describes the hardware, software and signal processing of AMiCUS and presents the results of a volunteer study with 13 able-bodied subjects and 6 tetraplegics with severe head motion limitations. As part of the study, the subjects performed two different pick-and-place tasks. The usability was assessed with a questionnaire. The overall performance and the main control elements were evaluated with objective measures such as completion rate and interaction time. The results show that the mapping of head motion onto robot motion is intuitive and the given feedback is useful, enabling smooth, precise and efficient robot control and resulting in high user-acceptance. Furthermore, it could be demonstrated that the robot did not move unintendedly, giving a positive prognosis for safety requirements in the framework of a certification of a product prototype. On top of that, AMiCUS enabled every subject to control the robot arm, independent of prior experience and degree of head motion limitation, making the system available for a wide range of motion impaired users.

Background: If blood pressure (BP) measurement is important to monitor blood hypertension and other cardiac diseases, and can be taken using a wrist device, learned societies recommend to take it in specific conditions. In a telemedicine context, patients are likely to perform it without any help from a medical practitioner. Therefore, the device must guide individuals using it. Materials and Methods: A smartwatch application integrating an Attitude and Heading Reference System algorithm was developed. It was combined with a wrist BP monitor to help users position the BP monitor properly. Results: The system was tested on 30 individuals and a survey conducted to evaluate its usability. The experiment showed that individuals needed to be guided to measure correctly their BP and our application helped them in positioning the wrist BP monitor in a user-friendly way. Conclusions: In a telemedicine context, it is possible to guide easily individuals to position correctly any commercialized wrist BP monitor using a smartwatch. Manufacturers could also integrate affordable sensors into their BP monitors to provide this assistance without the need of external devices.

BACKGROUND: Turning is a challenging mobility task requiring proper planning, coordination, and postural stability to be executed efficiently. Turn deficits can impair mobility and lead to falls in patients with neurodegenerative disease, such as Parkinson\'s disease (PD). It was previously shown that the cranio-caudal sequence involved during a turn (i.e., motion is initiated by the head, followed by the trunk) exhibits a signature that can be captured using an inertial system and analyzed through the Kinematics Theory. The so-called cranio-caudal kinematic turn signature (CCKS) metrics derived from this approach could, therefore, be a promising avenue to develop and track markers to measure early mobility deficits.
OBJECTIVE: The current study aims at exploring the discriminative validity and sensitivity of CCKS metrics extracted during turning tasks performed by patients with PD.
METHODS: Thirty-one participants (16 asymptomatic older adults (OA): mean age = 69.1 ± 7.5 years old; 15 OA diagnosed with early PD ON and OFF medication, mean age = 65.8 ± 8.4 years old) performed repeated timed up-and-go (TUG) tasks while wearing a portable inertial system. CCKS metrics (maximum head to trunk angle reached and commanded amplitudes of the head to trunk neuromuscular system, estimated from a sigma-lognormal model) were extracted from kinematic data recorded during the turn phase of the TUG tasks. For comparison purposes, common metrics used to analyze the quality of a turn using inertial systems were also calculated over the same trials (i.e., the number of steps required to complete the turn and the turn mean and maximum velocities).
RESULTS: All CCKS metrics discriminated between OA and patients (p ≤ 0.041) and were sensitive to change in PD medication state (p ≤ 0.033). Common metrics were also able to discriminate between OA and patients (p < 0.014), but they were unable to capture the change in medication state this early in the disease (p ≥ 0.173).
CONCLUSIONS: The enhanced sensitivity to change of the proposed CCKS metrics suggests a potential use of these metrics for mobility impairments identification and fluctuation assessment, even in the early stages of the disease.