Extending Assisted Audio Capabilities of TIAGo Service Robot

Grama, Lacrimioara; Rusu, Corneliu

doi:10.1109/sped.2019.8906635

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…We have focused our attention on the audio capabilities of the TIAGo and we have obtained good outcomes in [2,3,4,5,6]. In [2] three types of features have been used (Linear Predictive Coding -LPC, Linear Predictive Cepstral Coefficients -LPCC and Mel-Frequency Cepstral coefficients -MFCC) and eight classifiers were tested (Bayes Network -BN, Quadratic Discriminant Analysis -QDA, Support Vector Machines -SVM, Multilayer Perceptron -MP, k-Nearest Neighbor -kNN, kStar, Fuzzy Lattice Reasoning -FLR, and Random Forests -RF).…”

Section: Previous Results and Motivation For Updating The Audio Databasementioning

confidence: 99%

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Audio Database for TIAGo Service Robot

Grama

Muscar

Rusu

et al. 2021

Carpathian Journal of Electronic and Computer Engineering

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In this paper we present an updated version of the audio database acquired by the TIAGo service robot and by the simulated TIAGo service robot. To the initial database which consists in 1380 audio signals we have added 1920 more acoustic signals. The audio database consists now in 3300 isolated audio events corresponding to 110 classes. All the recorded sound events correspond to the indoor environment, and they are spread into five different scenarios: kitchen, room, appliances, voice and non-verbal. The audio database is intended to be used in order to identify indoor events based on audio signature, especially when elderly or chronically ill people live alone.

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Section: Previous Results and Motivation For Updating The Audio Databasementioning

confidence: 99%

“…Using the log of the total frame energy and 33 liftering MFCC features together with the 1-kNN, in [4], we have obtained a testing accuracy of 98.55%.…”

Section: Previous Results and Motivation For Updating The Audio Databasementioning

confidence: 99%

Audio Database for TIAGo Service Robot

Grama

Muscar

Rusu

et al. 2021

Carpathian Journal of Electronic and Computer Engineering

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“…Some of these robots are humanoid with wheeled bases and move semiautonomously in the hospital controlled by the medical staff. TIAGo [ 21 , 22 ], a robot operating system (ROS)-based robot platform, can perform both grasping tasks and disinfection tasks automatically. Users can choose the operating model for two scenarios through web graphical user interfaces (GUIs).…”

Section: Introductionmentioning

confidence: 99%

A Medical Assistive Robot for Telehealth Care During the COVID-19 Pandemic: Development and Usability Study in an Isolation Ward

Wang¹,

Lv²,

Lu³

et al. 2023

JMIR Hum Factors

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Background The COVID-19 pandemic is affecting the mental and emotional well-being of patients, family members, and health care workers. Patients in the isolation ward may have psychological problems due to long-term hospitalization, the development of the epidemic, and the inability to see their families. A medical assistive robot (MAR), acting as an intermediary of communication, can be deployed to address these mental pressures. Objective CareDo, a MAR with telepresence and teleoperation functions, was developed in this work for remote health care. The aim of this study was to investigate its practical performance in the isolation ward during the pandemic. Methods Two systems were integrated into the CareDo robot. For the telepresence system, a web real-time communications solution is used for the multiuser chat system and a convolutional neural network is used for expression recognition. For the teleoperation system, an incremental motion mapping method is used for operating the robot remotely. A clinical trial of this system was conducted at First Affiliated Hospital, Zhejiang University. Results During the clinical trials, tasks such as video chatting, emotion detection, and medical supplies delivery were performed via the CareDo robot. Seven voice commands were set for performing system wakeup, video chatting, and system exiting. Durations from 1 to 3 seconds of common commands were set to improve voice command detection. The facial expression was recorded 152 times for a patient in 1 day for the psychological intervention. The recognition accuracy reached 95% and 92.8% for happy and neutral expressions, respectively. Conclusions Patients and health care workers can use this MAR in the isolation ward for telehealth care during the COVID-19 pandemic. This can be a useful approach to break the chains of virus transmission and can also be an effective way to conduct remote psychological intervention.

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“…An introduction of these features may be found in [5]. In particular, the MFCCs are successfully used in many acoustic event recognitions (e.g., see [6], [7]). The authors of [4] used three MFCC variants as a neural network input for detecting two sounds -gunshot and glass breaking.…”

Section: Introductionmentioning

confidence: 99%

Efficient Feature Set Developed for Acoustic Gunshot Detection in Open Space

Sigmund

Hrabina

2021

ELEKTRON ELEKTROTECH

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This paper presents an efficient approach to automatic gunshot detection based on a combination of two feature sets: adapted standard sound features and hand-crafted novel features. The standard features are mel-frequency cepstral coefficients adapted for gunshot recognition in terms of uniform gamma-tone filters linearly spaced over the whole frequency range from 0 kHz to 16 kHz. The first 18 coefficients calculated from the 41 filters represent the best set of the optimized cepstral coefficients. The novel features were derived in the time domain from individual significant points of the raw waveform after amplitude normalization. Experiments were performed using single and ensemble neural networks to verify the effectiveness of the novel features for supplementing the standard features. The novelty of the work is the proposed feature combination, which allows to achieve very effective detection of gunshots from hunting weapons using 23 features and a simple neural network. In binary classification, the developed approach achieved an accuracy of 95.02 % in gunshot detection and 98.16 % in disregarding other sounds (i.e., non-gunshot).

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Extending Assisted Audio Capabilities of TIAGo Service Robot

Cited by 13 publications

References 9 publications

Audio Database for TIAGo Service Robot

Audio Database for TIAGo Service Robot

A Medical Assistive Robot for Telehealth Care During the COVID-19 Pandemic: Development and Usability Study in an Isolation Ward

Efficient Feature Set Developed for Acoustic Gunshot Detection in Open Space

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