A Real-Time Robot-Based Auxiliary System for Risk Evaluation of COVID-19 Infection

Wei, Wenqi; Wang, Jianzong; Ma, Jiteng; Cheng, Ning; Xiao, Jing

doi:10.21437/interspeech.2020-2105

Cited by 20 publications

(23 citation statements)

References 16 publications

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“…Wei et al [63] propose a diagnostic robot for COVID-19. The robot carries out a consultation with a patient whereby the robot can detect coughing events.…”

Section: Patient Monitoringmentioning

confidence: 99%

“…Have you been to the public areas of high risk in the last 14 days?" [63]. During the consultation, the robot records the dialogue.…”

Section: Patient Monitoringmentioning

confidence: 99%

“…This proposed model achieved an accuracy of 98.8% when detecting coughs. Then, if a cough is detected, a classification network is trained to identify COVID-19 among other respiratory diseases; this model achieved an accuracy of 76% [63]. Although this work is preliminary, future work will focus on tracing people with high-risks of COVID-19, potentially reducing the strain on public resources.…”

Section: Patient Monitoringmentioning

confidence: 99%

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Service Robots in the Healthcare Sector

et al. 2021

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Traditionally, advances in robotic technology have been in the manufacturing industry due to the need for collaborative robots. However, this is not the case in the service sectors, especially in the healthcare sector. The lack of emphasis put on the healthcare sector has led to new opportunities in developing service robots that aid patients with illnesses, cognition challenges and disabilities. Furthermore, the COVID-19 pandemic has acted as a catalyst for the development of service robots in the healthcare sector in an attempt to overcome the difficulties and hardships caused by this virus. The use of service robots are advantageous as they not only prevent the spread of infection, and reduce human error but they also allow front-line staff to reduce direct contact, focusing their attention on higher priority tasks and creating separation from direct exposure to infection. This paper presents a review of various types of robotic technologies and their uses in the healthcare sector. The reviewed technologies are a collaboration between academia and the healthcare industry, demonstrating the research and testing needed in the creation of service robots before they can be deployed in real-world applications and use cases. We focus on how robots can provide benefits to patients, healthcare workers, customers, and organisations during the COVID-19 pandemic. Furthermore, we investigate the emerging focal issues of effective cleaning, logistics of patients and supplies, reduction of human errors, and remote monitoring of patients to increase system capacity, efficiency, resource equality in hospitals, and related healthcare environments.

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“…Wei et al [63] propose a diagnostic robot for COVID-19. The robot carries out a consultation with a patient whereby the robot can detect coughing events.…”

Section: Patient Monitoringmentioning

confidence: 99%

“…Have you been to the public areas of high risk in the last 14 days?" [63]. During the consultation, the robot records the dialogue.…”

Section: Patient Monitoringmentioning

confidence: 99%

Section: Patient Monitoringmentioning

confidence: 99%

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Service Robots in the Healthcare Sector

et al. 2021

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“…Despite these benefits, only a few robot arms and teleoperated robots have been tested [26]. Other approaches in COVID-19 robotics response include temperature screening [42], [43] and a cough detection algorithm [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Deployment of a Socially Assistive Robot for Assessment of COVID-19 Symptoms and Exposure at an Elder Care Setting

Mucchiani

Cacchione

Johnson

et al. 2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

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This work investigates the deployment of an affordable socially assistive robot (SAR) at an older adult day care setting for the screening of COVID-19 symptoms and exposure. Despite the focus on older adults, other stakeholders (clinicians and caregivers) were included in the study due to the need for daily COVID-19 screening. The investigation considered which aspects of human-robot-interaction (HRI) are relevant when designing social agents for patient screening. The implementation was based upon the current screening procedure adopted by the deployment facility, and translated into robot dialogues and gesturing motion. Post-interaction surveys with participants informed their preferences for the type of interaction and system usability. Observer surveys evaluated users' reaction, verbal and physical engagement. Results indicated general acceptance of the social agent and possible improvements to the current version of the robot to encourage a broader adoption by the stakeholders.

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“…Such audio-based strategy has profound implication when examining symptomatic cough sounds associated with COVID-19 whereby cough is a primary symptom, alongside fever and fatigue. Convolution Neural Network (CNN)-based systems were trained to detect cough and screen for COVID-19, and reported accuracy exceeding 90% in [ 14 , 15 , 16 ] and while another study had reported 75% accuracy [ 17 ]. Features were extracted (both handcrafted and transfer learned) from a crowd-sourced database containing breathing and cough sounds [ 18 ] and were used to train a support vector machine and ensemble classifiers to screen COVID-19 individuals from healthy controls.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds

Balamurali

Hee

Kapoor

et al. 2021

Sensors

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Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). To train a deep neural network model, we collected a new dataset of cough sounds, labelled with a clinician’s diagnosis. The chosen model is a bidirectional long–short-term memory network (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs—healthy or pathology (in general or belonging to a specific respiratory pathology)—reaches accuracy exceeding 84% when classifying the cough to the label provided by the physicians’ diagnosis. To classify the subject’s respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among four classes of coughs, overall accuracy dropped: one class of pathological coughs is often misclassified as the other. However, if one considers the healthy cough classified as healthy and pathological cough classified to have some kind of pathology, then the overall accuracy of the four-class model is above 84%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological coughs, irrespective of the underlying conditions, occupy the same feature space making it harder to differentiate only using MFCC features.

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A Real-Time Robot-Based Auxiliary System for Risk Evaluation of COVID-19 Infection

Cited by 20 publications

References 16 publications

Service Robots in the Healthcare Sector

Service Robots in the Healthcare Sector

Deployment of a Socially Assistive Robot for Assessment of COVID-19 Symptoms and Exposure at an Elder Care Setting

Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds

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