An Effective Method on Vibration Immunity for Inclinometer based on MEMS Accelerometer

Hoang, Minh Long; Pietrosanto, Antonio

doi:10.1109/cas50358.2020.9267997

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Our second objective was to validate the performance of the LittleBeats' IMU (Study 3), which integrates an off-the-shelf IMU sensor that has been used in prior studies with excellent performance [55][56][57]. Including an IMU on the LittleBeats™ platform provides information on posture and movements that can be used to assess such constructs as infant physical activity, sedentary behavior, and sleep.…”

Section: Validation Of Ecg (Study 1 2) Imu (Study 3) and Audio (Study...mentioning

confidence: 99%

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Islam,

McElwain,

et al. 2024

Sensors

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Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.

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Section: Validation Of Ecg (Study 1 2) Imu (Study 3) and Audio (Study...mentioning

confidence: 99%

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Islam,

McElwain,

et al. 2024

Sensors

View full text Add to dashboard Cite

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“…A microelectromechanical systems (MEMS) accelerometer [ 3 , 4 ] and a gyroscope [ 5 , 6 ] are two main sensors used to provide the spatial angles for the drone system. Moreover, the IMU filters are required to optimize the angle measurements due to significant noise from the engine vibration and external interference [ 7 , 8 ]. As described in [ 9 ], roll, pitch, and yaw are the Euler angles used for orientation tracking: rotation around the front-to-back axis is called roll, rotation around the side-to-side axis is called pitch, and rotation around the vertical axis is called yaw ( Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking

Hoang

Carratù

Paciello

et al. 2023

Sensors

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The paper works on the new combination between the No Motion No Integration filter (NMNI) and the Kalman Filter (KF) to optimize the conducted vibration for orientation angles during drone operation. The drone’s roll, pitch, and yaw with just accelerometer and gyroscope were analyzed under the noise impact. A 6 Degree of Freedom (DoF) Parrot Mambo drone with Matlab/Simulink package was used to validate the advancements before and after fusing NMNI with KF. The drone propeller motors were controlled at a suitable speed level to keep the drone on the zero-inclination ground for angle error validation. The experiments show that KF alone successfully minimizes the variation for the inclination, but it still needs the NMNI support to enhance the performance in noise deduction, with the error only about 0.02°. In addition, the NMNI algorithm successfully prevents the yaw/heading from gyroscope drifting due to the zero-value integration during no rotation with the maximum error of 0.03°.

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Yaw/Heading optimization by drift elimination on MEMS gyroscope

Hoang

Pietrosanto

2021

Sensors and Actuators A: Physical

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An Effective Method on Vibration Immunity for Inclinometer based on MEMS Accelerometer

Cited by 9 publications

References 11 publications

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking

Yaw/Heading optimization by drift elimination on MEMS gyroscope

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