Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning

Alonso-Martín, Fernándo; Gamboa-Montero, Juan José; Castillo, José Carlos; Castro‐González, Álvaro; Salichs, Miguel Á.

doi:10.3390/s17051138

Cited by 28 publications

(11 citation statements)

References 46 publications

(50 reference statements)

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“…Generally, a complete fuzzy controller consists of four modules: fuzzy interface, knowledge base, reasoning mechanism, and de-fuzzy. The precision of the fuzzy controller completely determines the performance of the entire fuzzy control system to a certain extent (Alonso-Martín et al, 2017 ). First of all, the precise amount of input can only be utilized by the fuzzy controller after the fuzzy transformation of the variable.…”

Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Tang

2021

Front. Neurorobot.

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In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

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Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Tang

2021

Front. Neurorobot.

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“…Hence, an appropriate threshold voltage is set for a typically processed signal, as described in the introduction. It is common to analyze the aforementioned AE parameters based on the threshold voltage [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Sheet Metal Forming Experimentsmentioning

confidence: 99%

“…When a metallic material exceeds the maximum load and is close to fracturing, AE occurs frequently. Hence, AE is widely applied for evaluating the mechanical properties of materials [ 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

Jeong

Hong

2021

Sensors

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In this study, an acoustic emission (AE) sensor was utilized to predict fractures that occur in a product during the sheet metal forming process. An AE activity was analyzed, presuming that AE occurs when plastic deformation and fracturing of metallic materials occur. For the analysis, a threshold voltage is set to distinguish the AE signal from the ripple voltage signal and noise. If the amplitude of the AE signal is small, it is difficult to distinguish the AE signal from the ripple voltage signal and the noise signal. Hence, there is a limitation in predicting fractures using the AE sensor. To overcome this limitation, the Kalman filter was used in this study to remove the ripple voltage signal and noise signal and then analyze the activity. However, it was difficult to filter out the ripple voltage signal using a conventional low-pass filter or Kalman filter because the ripple voltage signal is a high-frequency component governed by the switch-mode of the power supply. Therefore, a Kalman filter that has a low Kalman gain was designed to extract only the ripple voltage signal. Based on the KF-RV algorithm, the measured ripple voltage and noise signal were reduced by 97.3% on average. Subsequently, the AE signal was extracted appropriately using the difference between the measured value and the extracted ripple voltage signal. The activity of the extracted AE signal was analyzed using the ring-down count among various AE parameters to determine if there was a fracture in the test specimen.

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“…Some of these objects are recording our activities, such as the touch screens of smartphones or tablets. Thus, adding sensory perception to objects and surfaces that surround us daily is an active area of research, and it has generated keen interest within the interactive surface community [ 1 , 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model

Moreira

Perkusich

Luiz

2020

Sensors

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Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB’s toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN.

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Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning

Cited by 28 publications

References 46 publications

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model

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