MEMS-Based Inertial Sensor Signals and Machine Learning Methods for Classifying Robot Motion

Nevliudov, İgor; Filipenko, A. I.; Волкова, М В; Ponomaryova, Ganna

doi:10.1109/dsmp.2018.8478613

Cited by 9 publications

(9 citation statements)

References 5 publications

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“…MEMS-IMU has attracted much attention in the recent years owing to their low cost, small size, and ease of integration [ 1 ]. This device is ushering in a huge market demand, as it has become an important component of different navigation systems, attitude control devices [ 2 ], unmanned aerial vehicles [ 3 ], robot navigation [ 4 , 5 , 6 ], satellite systems [ 7 ], etc. MEMS-IMU system is often affected by various factors characterized on environmental, electronic, and manufacturing noises all leading to random navigation errors when using MEMS-IMU [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…When it comes to future sensors, artificial intelligence (AI) would unquestionably play a vital role in terms of "decoding" signals from the various sensor system and networks [179][180][181][182][183][184][185][186][187][188]. Furthermore, VR and AR will also rely much on novel sensors to provide better functionality such as immersive experience and feedback control.…”

Section: Mems/nems Vs Ai and Its Vr And Armentioning

confidence: 99%

“…This sensor-related AI system can recognize handwriting and gestures, which successfully demonstrated the application of the next-generation sensors. Nevlydov et al developed a robot using machine-learning methods as shown in Figure 12a [180]. They successfully adopted a three-axis MEMS gyroscope fixed to the robot body using various machine-learning methods for solving classification tasks.…”

Section: Mems Vs Aimentioning

confidence: 99%

“…(a) Illustration of the dynamic neural network (DNN) for the performance of the low-resonant-frequency piezoelectric MEMS energy harvester. Adapted with permission from Nevlydov et al[180]. (b) Visual representations of the gaussian mixture model (GMM)-based machine learning algorithm for the speaker recognition, and the dataset of 90% used for training data, and 10% for testing data by 2800 training data of 40 people.…”

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confidence: 99%

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Development Trends and Perspectives of Future Sensors and MEMS/NEMS

et al. 2019

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With the fast development of the fifth-generation cellular network technology (5G), the future sensors and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS) are presenting a more and more critical role to provide information in our daily life. This review paper introduces the development trends and perspectives of the future sensors and MEMS/NEMS. Starting from the issues of the MEMS fabrication, we introduced typical MEMS sensors for their applications in the Internet of Things (IoTs), such as MEMS physical sensor, MEMS acoustic sensor, and MEMS gas sensor. Toward the trends in intelligence and less power consumption, MEMS components including MEMS/NEMS switch, piezoelectric micromachined ultrasonic transducer (PMUT), and MEMS energy harvesting were investigated to assist the future sensors, such as event-based or almost zero-power. Furthermore, MEMS rigid substrate toward NEMS flexible-based for flexibility and interface was discussed as another important development trend for next-generation wearable or multi-functional sensors. Around the issues about the big data and human-machine realization for human beings’ manipulation, artificial intelligence (AI) and virtual reality (VR) technologies were finally realized using sensor nodes and its wave identification as future trends for various scenarios.

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“…Therefore, MEMS inertial sensors are widely popular in civil and military applications. According to the application scenario, in the civilian field, MEMS inertial sensors are mainly used in consumer portables such as mobile phones, game consoles, digital cameras, robots, and medical devices for health care [ 9 , 10 , 11 , 12 , 13 , 14 ]. In the military field, MEMS inertial sensors are commonly employed in high-end markets such as optoelectronic devices, aerospace, torpedo, missile, rockets, and so on [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

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MEMS-Based Inertial Sensor Signals and Machine Learning Methods for Classifying Robot Motion

Cited by 9 publications

References 5 publications

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

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

Development Trends and Perspectives of Future Sensors and MEMS/NEMS

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

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