Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Han, Sunyoung; Meng, Zhen; Omisore, Olatunji Mumini; Akinyemi, Toluwanimi Oluwadara; Yan, Yuepeng

doi:10.3390/mi11111021

Cited by 58 publications

(23 citation statements)

References 109 publications

(190 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This may lead to cumulative error propagation due to the integration process, which may differ depending on sensor orientation: different sensor orientations may lead to distinct error propagation profiles. Given the random nature of the noise properties of gyroscope measurements [ 36 ], no particular tendency for an increased error towards a specific rotation can be observed. All gait parameters extracted using synthetically rotated data remained practically equivalent (see Table 6 ), which demonstrates the invariance of the method to differences in orientation.…”

Section: Discussionmentioning

confidence: 99%

Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors

Guimar�ães

Sousa

Correia

2021

Sensors

View full text Add to dashboard Cite

Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking. In addition to being unrealistic to assume that the sensor can be aligned perfectly with the body, the robustness of gait analysis with respect to differences in sensor orientation has not yet been investigated—potentially hindering use in clinical settings. To address this gap in the literature, we introduce an orientation-invariant gait analysis approach and propose a method to quantitatively assess robustness to changes in sensor orientation. We validate our results in a group of young adults, using an optical motion capture system as reference. Overall, good agreement between systems is achieved considering an extensive set of gait metrics. Gait speed is evaluated with a relative error of −3.1±9.2 cm/s, but precision improves when turning strides are excluded from the analysis, resulting in a relative error of −3.4±6.9 cm/s. We demonstrate the invariance of our approach by simulating rotations of the sensor on the foot.

show abstract

Section: Discussionmentioning

confidence: 99%

Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors

Guimar�ães

Sousa

Correia

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Thus, the opinion of designing the Kalman Filter due to the presence of inaccurate model and noise statistics can lead to optimality loss, great reduction in estimation accuracy, and filter divergence. To solve the problem of Kalman Filter divergence, various improved adaptive Kalman Filters have been developed in recent years to deal with measurement uncertainties [ 36 ]. Wavelet thresholding is a common method for denoising MEMS gyro output signals, and it has the characteristics of multiresolution analysis.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

et al. 2021

Self Cite

View full text Add to dashboard Cite

Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.

show abstract

“…Due to the nature of external vibration, and/or sensor errors, the raw data acquired from wearable sensors mostly contain unwanted noise, as shown in Figure 5 a. Hence, it is essential to preprocess the raw data to obtain a format that is considered representative of physical activities and suitable for predictive modeling [ 27 , 28 ]. Essentially, all measured body movements are expressed within frequency components below 20 Hz.…”

Section: Methodsmentioning

confidence: 99%

Recognizing Physical Activities for Spinal Cord Injury Rehabilitation Using Wearable Sensors

Alhammad¹,

Al-Dossari²

2021

Sensors

View full text Add to dashboard Cite

The research area of activity recognition is fast growing with diverse applications. However, advances in this field have not yet been used to monitor the rehabilitation of individuals with spinal cord injury. Noteworthily, relying on patient surveys to assess adherence can undermine the outcomes of rehabilitation. Therefore, this paper presents and implements a systematic activity recognition method to recognize physical activities applied by subjects during rehabilitation for spinal cord injury. In the method, raw sensor data are divided into fragments using a dynamic segmentation technique, providing higher recognition performance compared to the sliding window, which is a commonly used approach. To develop the method and build a predictive model, a machine learning approach was adopted. The proposed method was evaluated on a dataset obtained from a single wrist-worn accelerometer. The results demonstrated the effectiveness of the proposed method in recognizing all of the activities that were examined, and it achieved an overall accuracy of 96.86%.

show abstract

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Cited by 58 publications

References 109 publications

Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors

Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

Recognizing Physical Activities for Spinal Cord Injury Rehabilitation Using Wearable Sensors

Contact Info

Product

Resources

About