Cosms

Maeda, Naoki; Hirabe, Yuko; Arakawa, Yutaka; Yasumoto, Keiichi

doi:10.1145/2968219.2971397

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…The most commonly used physiological signals to detect stress state are cardiac activity, respiratory activity, brain activity [18], body temperature [19], sweating, eye movements, facial expressions and gestures. Previous studies about human health monitoring systems and the measurement of vital parameters have been considered for general purposes such as driving assistance and fatigue recognition [20] [21], office worker stress monitoring [22], coal mine worker safety [23], and remote videomediated assistance [24]. In human-robot interaction, human emotions, especially negative ones, influence the performance of robotic interventions.…”

Section: B Vital Parameters Monitoring Of An Operatormentioning

confidence: 99%

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

et al. 2023

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Remote robotic interventions and maintenance tasks are frequently required in hazardous environments. Particularly, missions with a redundant mobile manipulator in the world's most complex machine, the CERN Large Hadron Collider, are performed in a sensitive underground environment with radioactive or electromagnetic hazards, bringing further challenges in safety and reliability. The mission's success depends on the robot's hardware and software, and when the tasks become too unpredictable to execute autonomously, the operators need to make critical decisions. However, in most current humanmachine systems, the state of the human is neglected. In this context, a novel 3D Mixed Reality (MR) human-robot interface with the Operator Monitoring System (OMS) was developed to advance safety and task efficiency with improved spatial awareness, advanced manipulator control, and collision avoidance. However, new techniques could increase the system's sophistication and add to the operator's workload and stress. Therefore, for operational validation, the 3D MR interface had to be compared with an operational 2D interface, which has been used in hundreds of interventions. With the 3D MR interface, the execution of precise approach tasks was faster, with no increased workload or physiological response. The new 3D MR techniques improved the teleoperation quality and safety while maintaining similar effects on the operator. The OMS worked jointly with the interface and performed well with operators with varied teleoperation backgrounds facing a stressful real telerobotic scenario in the LHC. The paper contributes to the methodology for human-centred interface evaluation incorporating the user's physiological state: heart rate, respiration rate and skin electrodermal activity, and combines it with the NASA TLX assessment method, questionnaires, and task execution time. It provides novel approaches to operator state identification, the GUI-OMS software architecture, and the evaluation of the 3D MR techniques. The solutions can be practically applied in mission-critical applications, such as telesurgery, space robotics, uncrewed transport vehicles and semi-autonomous machinery.

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Section: B Vital Parameters Monitoring Of An Operatormentioning

confidence: 99%

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

et al. 2023

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CareCam: An Intelligent, Camera-Based Health Companion at the Workplace

Kraft

Schmidt

Oschinsky

et al. 2022

Lecture Notes in Information Systems and Organisation

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Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

Wang

Aung

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Continuously monitoring schizophrenia patients’ psychiatric symptoms is crucial for in-time intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey administered by clinicians to evaluate symptom severity in schizophrenia. The CrossCheck symptom prediction system is capable of tracking schizophrenia symptoms based on BPRS using passive sensing from mobile phones. We present results from an ongoing randomized control trial, where passive sensing data, self-reports, and clinician administered 7-item BPRS surveys are collected from 36 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-12 months. We show that our system can predict a symptom scale score based on a 7-item BPRS within ±1.45 error on average using automatically tracked behavioral features from phones (e.g., mobility, conversation, activity, smartphone usage, the ambient acoustic environment) and user supplied self-reports. Importantly, we show our system is also capable of predicting an individual BPRS score within ±1.59 error purely based on passive sensing from phones without any self-reported information from outpatients. Finally, we discuss how well our predictive system reflects symptoms experienced by patients by reviewing a number of case studies.

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Cosms

Cited by 4 publications

References 2 publications

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

CareCam: An Intelligent, Camera-Based Health Companion at the Workplace

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

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