A new Orientation Method for Inclinometer based on MEMS Accelerometer used in Industry 4.0

Hoang, Minh Long; Carratù, Marco; Paciello, Vincenzo; Pietrosanto, Antonio

doi:10.1109/indin45582.2020.9442189

Cited by 7 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Afterwards, such files have been used to train and test the ML algorithms developed by Python language [54], taking advantage of its specific AI libraries: Scikit-learn [55], Keras from TensorFlow platform [56]. The micro-electromechanical system (MEMS) accelerometer [57][58][59] is encapsulated to be appropriately mounted under the bed frame as shown in Figure 2, and it is connected to the microcontroller through an SPI connection. As demonstrated in Figure 3, the ADXL355 sensor provides acceleration data in a 32-bit digital format from X, Y, Z axes.…”

Section: Setup and Devicesmentioning

confidence: 99%

“…All the acquired data (X, Y, Z, and finger pulse) are transmitted to the workstation by a serial connection at the baud rate of 921,600. The micro-electromechanical system (MEMS) accelerometer [57][58][59] is encapsulated to be appropriately mounted under the bed frame as shown in Figure 2, and it is connected to the microcontroller through an SPI connection. As demonstrated in Figure 3, the ADXL355 sensor provides acceleration data in a 32-bit digital format from X, Y, Z axes.…”

Section: Setup and Devicesmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

Hoang,

Matrella,

Ciampolini

2024

Sensors

Self Cite

View full text Add to dashboard Cite

This work aims to compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in detecting users’ heartbeats on a smart bed. Targeting non-intrusive, continuous heart monitoring during sleep time, the smart bed is equipped with a 3D solid-state accelerometer. Acceleration signals are processed through an STM 32-bit microcontroller board and transmitted to a PC for recording. A photoplethysmographic sensor is simultaneously checked for ground truth reference. A dataset has been built, by acquiring measures in a real-world set-up: 10 participants were involved, resulting in 120 min of acceleration traces which were utilized to train and evaluate various Artificial Intelligence (AI) algorithms. The experimental analysis utilizes K-fold cross-validation to ensure robust model testing across different subsets of the dataset. Various ML and DL algorithms are compared, each being trained and tested using the collected data. The Random Forest algorithm exhibited the highest accuracy among all compared models. While it requires longer training time compared to some ML models such as Naïve Bayes, Linear Discrimination Analysis, and K-Nearest Neighbour Classification, it keeps substantially faster than Support Vector Machine and Deep Learning models. The Random Forest model demonstrated robust performance metrics, including recall, precision, F1-scores, macro average, weighted average, and overall accuracy well above 90%. The study highlights the better performance of the Random Forest algorithm for the specific use case, achieving superior accuracy and performance metrics in detecting user heartbeats in comparison to other ML and DL models tested. The drawback of longer training times is not too relevant in the long-term monitoring target scenario, so the Random Forest model stands out as a viable solution for real-time ballistocardiographic heartbeat detection, showcasing potential for healthcare and wellness monitoring applications.

show abstract

Section: Setup and Devicesmentioning

confidence: 99%

Section: Setup and Devicesmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

Hoang,

Matrella,

Ciampolini

2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Robots serve as an indispensable part of industrial environments, increasing productivity and reducing design-to-market time through their ability to efficiently improve production in highly repetitive tasks [5]. Precision of the factory elements is a primary issue that must be improved to increase manufacturing performance [6,7]. Industrial tasks on a factory floor, such as object handling [8] and manipulation [9], require ahigh level of precision while maintaining safe operation.…”

Section: Introductionmentioning

confidence: 99%

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

Khanesar

Branson

2022

Energies

View full text Add to dashboard Cite

This paper presents a sliding mode fuzzy control approach for industrial robots at their static and near static speed (linear velocities less than 5 cm/s). The extended Kalman filter with its covariance resetting is used to translate the coordinates from Cartesian to joint angle space. The translated joint angles are then used as a reference signal to control the industrial robot dynamics using a sliding mode fuzzy controller. The stability and robustness of the proposed controller is proven using an appropriate Lyapunov function in the presence of parameter uncertainty and unknown dynamic friction. The proposed controller is simulated on a 6-DOF industrial robot, namely the Universal Robot-UR5, considering the maximum allowable joint torques. It is observed that the proposed controller can successfully control UR5 under uncertainties in terms of unknown dynamic friction and parameter uncertainties. The tracking performance of the proposed controller is compared with that of the sliding mode control approach. The simulation results demonstrate superior performance of the proposed approach over the sliding mode control method in the presence of uncertainties.

show abstract

“…Commercial inclinometers typically use a triaxial accelerometer or triaxial gyro sensor [8]- [10], [22]- [27]. Inclinometers using an accelerometer and gyro sensor must be calibrated as accurately as possible with changes in temperature, gain factors, and biases [24]- [27]. In addition, a complex algorithm and calibration are essential for measuring the inclination angle with such inclinometers.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, in an environment with many intense and repetitive movements (e.g., rolling, yawing, and pitching), such as the ocean, the inclinometer is subjected to mechanically harsh conditions and can exhibit abnormal measured values owing to sensor drift and offset [25]- [27]. Therefore, it is necessary to rapidly reset the device with an initial value once conditions permit [24], [25]. The measurement methods used in accelerometer-based inclinometers utilize piezoresistive [8], [10], [28], fiber optical [29]- [32], capacitive [8], [21], [23], [33], [34], resonant [35]- [38], and thermal properties [39].…”

Section: Introductionmentioning

confidence: 99%

3D Printing-Assisted Soft Capacitive Inclinometers for Simultaneous Monitoring of Tilt Angles and Directions

et al. 2022

View full text Add to dashboard Cite

This study presents capacitive-type inclinometers composed of flexible polymer pillars and dome-shaped roof frames that were manufactured using the three-dimensional (3D) printing method. Polylactic acid (PLA) filaments and acrylonitrile butadiene styrene (ABS) filaments were printed by the fused deposition modeling type 3D printer to fabricate the dome-shaped roof frames and the polymer curing molds, respectively. The operating principle of the inclinometer was to detect the change in capacitance between the helix-shaped electrode coiled around the polymer pillar and the built-in electrode in the roof frame. When the inclinometer was tilted, the polymer pillar was bent, and the physical distance between each electrode was changed with respect to the tilt angle and direction. Therefore, the tilt angles and directions were simultaneously estimated by distinguishing the capacitance and peak capacitance, respectively. The results of the experiments revealed that the inclinometer using the polymer pillar that was electrically connected to a standard weight and the roof frame with a roof angle of 45 • exhibited a higher sensitivity (1.391 pF at a tilt angle of 40 • ) compared to those using roof angles of 90 • and 135 • . This study supports the use of 3D printing technology for the facile manufacturing of inclinometers that can detect tilt angles and directions simultaneously, which is not achievable with conventional inclinometers.INDEX TERMS 3D printing, inclinometer, capacitive, polymer, rapid prototyping.

show abstract

A new Orientation Method for Inclinometer based on MEMS Accelerometer used in Industry 4.0

Cited by 7 publications

References 15 publications

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

3D Printing-Assisted Soft Capacitive Inclinometers for Simultaneous Monitoring of Tilt Angles and Directions

Contact Info

Product

Resources

About