Zero Angular Rate Update Research Articles

A ubiquitous architecture for wheelchair fall anomaly detection using low-cost embedded sensors and isolation forest algorithm

Falls represent one of the major health risk issues world-wide. In this paper, a wheelchair fall anomaly detection framework based on the hybrid Isolation Forest (IF) and threshold based method (TBM) is proposed. The sensor data was obtained from tri-axial orthogonal accelerometer and gyroscope MPU-6050 sensor. In order to handle the uncertainties due to the sensor noise factor, a method based on Zero Angular Rate Update (ZART) and Complementary Filter (CF) was utilized. The concept of sensor fusion was applied to create multi-dimensional training data from the best features selected using the ReliefF algorithm. The IF detector was trained on the best eight-dimensional features selected from 48 features for the detection of the fall events in contrast to 38 features defined in our previous publication in Sheikh and Jilani. Further, the design of a ubiquitous architecture incorporating the proposed fall detection scheme was proposed. The g-mean score accuracy was achieved up-to ∼97.1% with the proposed IF +threshold hybrid strategy. While using only the IF method, an F1-score of 96.2%, an AUC-ROC score of 95.3% and a g-mean score of 92.9 % was obtained.

Heading constraint algorithm for foot-mounted PNS using low-cost IMU

Foot-mounted pedestrian navigation system (PNS) is a common solution to pedestrian navigation using micro-electro mechanical system (MEMS) inertial sensors. The inherent problems of inertial navigation system (INS) by the traditional algorithm, such as the accumulated errors and the lack of observation of heading and altitude information, have become obstacles to the application and development of the PNS. In this paper, we introduce a heuristic heading constraint method. First of all, according to the movement characteristics of human gait, we use the generalized likelihood ratio test (GLRT) detector and introduce a time threshold to classify the human gait, so that we can effectively identify the stationary state of the foot. In addition, based on zero velocity update (ZUPT) and zero angular rate update (ZARU), the cumulative error of the inertial measurement unit (IMU) is limited and corrected, and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian. After simulation and experiments with low-cost IMU, the method is proved to reduce the localization error of end-point to less than 1% of the total distance, and it has great value in application.

A ubiquitous wheelchair fall detection system using low-cost embedded inertial sensors and unsupervised one-class SVM

Fall detector systems are one of the highly researched areas of Ambient Assisted Living (AAL) applications ensuring safety and autonomous living for the elderly. Today, despite the diversity of methods proposed for fall detectors, there is still a need to develop accurate and robust architectures, methods, and protocols for the detection of the occurrence of falls for a special-class of wheelchair-bound people. To address this issue, this paper proposes a wheelchair fall detector system based on a low-cost, light-weight inertial sensing method utilizing a hybrid scheme and unsupervised One-Class SVM (OCSVM) for detection of cases leading to a ‘fall’ anomaly during wheelchair maneuver and for the case of unassisted transfers. To make the system robust against noise, a novel hybrid multi-sensor fusion strategy combining Zero Angular Rate Update (ZART) and Complementary Filter (CF) to compensate sensor integral errors is utilized. A heterogeneous dataset is constructed using the publicly available Sis-Fall dataset to include possible fall cases due to unassisted transfers from wheelchairs and secondly, a prototype is developed to emulate the wheelchair system with the embedded inertial sensors to capture trends in the sensor measurements due to wheelchair tips and falls. The OCSVM anomaly detection technique is utilized to overcome the major disadvantage of supervised learning methods requiring huge datasets from activities performed by human subjects needed to guarantee higher accuracy rates from these detectors. In this regard, to capture the best features from the generated accelerometer and gyroscope feature set, the ReliefF algorithm is used. The proposed method is compared with the widely reported approaches in the literature for fall-detectors, i.e., threshold-based methods and other one-class learning approaches. It is demonstrated that the fall-detection accuracy (i.e., the g-mean score) was achieved up to 96% with the proposed method.

A Hybrid Framework for Mitigating Heading Drift for a Wearable Pedestrian Navigation System through Adaptive Fusion of Inertial and Magnetic Measurements

Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.

Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

Evaluation of AHRS algorithms for Foot-Mounted Inertial-based Indoor Navigation Systems

Abstract Personal Dead Reckoning based on foot-mounted Inertial Measurement Units is a research hotspot in the field of positioning and navigation in recent years. This paper conducts a targeted research on the application of current mainstream attitude and heading reference system (AHRS) algorithm in the foot inertial navigation positioning. Through open datasets, the positioning accuracy and directional accuracy of 9-state complementary Kalman filter (CKF) are compared and analyzed among the conventional algorithm, Mahony algorithm, and Madgwick algorithm, in which the Madgwick algorithm can achieve the best positioning results. And on this basis, for the Madgwick algorithm, it is verified that it can help improve the positioning accuracy of 15-state CKF under the assistive technologies of zero angular rate update (ZARU) and heuristic heading reduction (HDR). The adaptive zerospeed detection algorithm is designed, and the threshold value of zero-speed detection is set dynamically through tracking the variable of speed in CKF, which can detect the time period of zero-speed state more accurately, thus further improving the correction of directional errors. Finally, the effectiveness of the proposed algorithm is further proved by actual data.

The Standing Calibration Method of MEMS Gyro Bias for Autonomous Pedestrian Navigation System

In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying.