Embedded sensor fusion and moving-average filter for Inertial Measurement Unit (IMU) on the microcontroller-based stabilized platform

Redhyka, Grace Gita; Setiawan, Dika; Soetraprawata, Demi

doi:10.1109/icacomit.2015.7440178

Cited by 26 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The torso flexion and extension rotation angles are calculated by employing the complementary filter which takes information from the IMU's accelerometers and gyroscopes that are recorded and sent to a virtual scenario. 60 To visualize and reproduce the motion capture, a virtual scenario including a virtual avatar was developed using the Unity 3D game engine. The virtual avatar mirrors the spinal movements of the wooden manikin by reading the rotational data calculated by the Arduino platform and sent to the Unity 3D engine using Bluetooth communication protocols.…”

Section: Tangible Manikin Uimentioning

confidence: 99%

Development and evaluation of two posture-tracking user interfaces for occupational health care

Valdivia

Blanco

Uribe-Quevedo

et al. 2018

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The spinal column requires special care through exercises focused on muscle strengthening, flexibility, and mobility to minimize the risk of developing musculoskeletal disorders that may affect the quality of life. Guidelines for spinal column exercises are commonly presented through printed and multimedia guides accompanied with demonstrations performed by a physiotherapist, occupational health expert, or physical fitness trainer. However, existing guides lack interaction and oral explanations may not always be clear to the user, leading to decreased engagement and motivation to start, continue, or complete an exercise program. In this article, we present two interactive and engaging posture-tracking user interfaces intended to promote proper spinal column exercise form. One user interface employs a wooden manikin with an integrated inertial measurement unit to provide a tangible user interaction. The other user interface presents a mobile application that provides instructions and explanations about the exercises. Both user interfaces allow recording key postures during the exercise for reference and feedback. We compared the usability of the interfaces through a series of flexion and extension exercises, monitored with an inertial measuring unit worn around the torso, and a Microsoft Kinect V2 vision-based sensor. Although no significant differences between the manikin user interface and the mobile application were found in terms of usability, the inertial measurement unit provided more accurate and reliable data in comparison to the Microsoft Kinect V2 as a result of body occlusions in front of the sensor caused during the torso flexion. Although both user interfaces provide different experiences and performed well, we believe that a combination of both will improve user engagement and motivation, while providing a more accurate motion profile.

show abstract

Section: Tangible Manikin Uimentioning

confidence: 99%

Development and evaluation of two posture-tracking user interfaces for occupational health care

Valdivia

Blanco

Uribe-Quevedo

et al. 2018

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…The result is a data sparring signal processing technique that does not sacrifice on noise reduction potential. This interdisciplinary technique has been used in some biomedical (Chen et al, 2006; Manikandan & Soman, 2012) and robotic (Redhyka et al, 2015) applications, but is primarily utilized to predict stock market trends (de Souza et al, 2018; Ellis & Parbery, 2005; Metghalchi et al, 2012). Many moving average iterations exist (Vandewalle et al, 1999), but we focus on simple moving averaging (SMA) in this study, which averages each data point with equal weight and has the highest retention of original data set properties.…”

Section: Introductionmentioning

confidence: 99%

Time‐domain signal averaging to improve microparticles detection and enumeration accuracy in a microfluidic impedance cytometer

Ashley

Hassan

2021

Biotech & Bioengineering

View full text Add to dashboard Cite

Microfluidic impedance cytometry is a powerful system to measure micro and nano-sized particles and is routinely used in point-of-care disease diagnostics and other biomedical applications. However, small objects near a sensor's detection limit are plagued with relatively significant background noise and are difficult to identify for every case. While many data processing techniques can be utilized to reduce noise and improve signal quality, frequently they are still inadequate to push sensor detection limits. Here, we report the first demonstration of a novel signal averaging algorithm effective in noise reduction of microfluidic impedance cytometry data, improving enumeration accuracy, and reducing detection limits. Our device uses a 22 µm tall × 100 µm wide (with 30 µm wide focused aperture) microchannel and gold coplanar microelectrodes that generate an electric field, recording bipolar pulses from polystyrene microparticles flowing through the channel. In addition to outlining a modified moving signal averaging technique theoretically and with a model data set, we also performed a compendium of characterization experiments including variations in flow rate, input voltage, and particle size. Multivariate metrics from each experiment are compared including signal amplitude, pulse width, background noise, and signal-to-noise ratio (SNR). Incorporating our technique resulted in improved SNR and counting accuracy across all experiments conducted, and the limit of detection improved from 5 to 1 µm particles without modifying microchannel dimensions.Succeeding this, we envision implementing our modified moving average technique to develop next-generation microfluidic impedance cytometry devices with an expanded dynamic range and improved enumeration accuracy. This can be exceedingly useful for many biomedical applications, such as infectious disease diagnostics where devices may enumerate larger-scale immune cells alongside sub-micron bacterium in the same sample.

show abstract

“…To obtain fast and accurate attitude states, sensor fusion techniques have been applied to IMU measurements, including wide ranges of complementary filters [5,17,18,19,20,21,22,23,24] and Kalman filters [23,24,25,26,27,28,29,30,31,32,33,34,35]. A complementary filter typically combines accelerometer output for low-frequency attitude estimation with integrated gyroscope output for high-frequency estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Kalman filter is an optimal recursive estimation scheme that uses a system’s dynamic model, known control inputs, and multiple sequential measurements from sensors to form an estimate of the system states fusing prediction and measurement online [25,26,27,28]. The extended Kalman filter (EKF) is developed for nonlinear system state estimation and has been widely used for real-time UAV systems for Euler angle based attitude estimation [23,24,29,30] as well as quaternion based attitude estimation [31,32,33,34,35].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Koksal

Jalalmaab

Fi̇dan

2018

Sensors

View full text Add to dashboard Cite

In this paper, an infinite-horizon adaptive linear quadratic tracking (ALQT) control scheme is designed for optimal attitude tracking of a quadrotor unmanned aerial vehicle (UAV). The proposed control scheme is experimentally validated in the presence of real-world uncertainties in quadrotor system parameters and sensor measurement. The designed control scheme guarantees asymptotic stability of the close-loop system with the help of complete controllability of the attitude dynamics in applying optimal control signals. To achieve robustness against parametric uncertainties, the optimal tracking solution is combined with an online least squares based parameter identification scheme to estimate the instantaneous inertia of the quadrotor. Sensor measurement noises are also taken into account for the on-board Inertia Measurement Unit (IMU) sensors. To improve controller performance in the presence of sensor measurement noises, two sensor fusion techniques are employed, one based on Kalman filtering and the other based on complementary filtering. The ALQT controller performance is compared for the use of these two sensor fusion techniques, and it is concluded that the Kalman filter based approach provides less mean-square estimation error, better attitude estimation, and better attitude control performance.

show abstract

Embedded sensor fusion and moving-average filter for Inertial Measurement Unit (IMU) on the microcontroller-based stabilized platform

Cited by 26 publications

References 7 publications

Development and evaluation of two posture-tracking user interfaces for occupational health care

Development and evaluation of two posture-tracking user interfaces for occupational health care

Time‐domain signal averaging to improve microparticles detection and enumeration accuracy in a microfluidic impedance cytometer

Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

Contact Info

Product

Resources

About