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Your search keyword '"Francesco Luke Siena"' showing total 2 results
Start Over Author "Francesco Luke Siena" Journal journal of medical systems
2 results on '"Francesco Luke Siena"'

1. Proof-of-Concept Study: a Mobile Application to Derive Clinical Outcome Measures from Expression and Speech for Mental Health Status Evaluation

Author

Michael Vernon, Bill Byrom, Paul Watts, David Crundall, Philip Breedon, and Francesco Luke Siena

Subjects
Mobile & Wireless Health, Autism Spectrum Disorder, Contempt, media_common.quotation_subject, Health Status, Medicine (miscellaneous), Health Informatics, Anger, Clinical outcome assessments, Video analysis, 03 medical and health sciences, 0302 clinical medicine, Health Information Management, Outcome Assessment, Health Care, Humans, Speech, Valence (psychology), International Affective Picture System, 030304 developmental biology, media_common, 0303 health sciences, Facial expression, Mobile Applications, Sadness, Facial Expression, Happiness, Mental health, Selfie, Psychology, 030217 neurology & neurosurgery, Information Systems, Clinical psychology
Abstract

This proof-of-concept study aimed to assess the ability of a mobile application and cloud analytics software solution to extract facial expression information from participant selfie videos. This is one component of a solution aimed at extracting possible health outcome measures based on expression, voice acoustics and speech sentiment from video diary data provided by patients. Forty healthy volunteers viewed 21 validated images from the International Affective Picture System database through a mobile app which simultaneously captured video footage of their face using the selfie camera. Images were intended to be associated with the following emotional responses: anger, disgust, sadness, contempt, fear, surprise and happiness. Both valence and arousal scores estimated from the video footage associated with each image were adequate predictors of the IAPS image scores (p

Published
2020

2. Utilising the Intel RealSense Camera for Measuring Health Outcomes in Clinical Research

Author

Bill Byrom, Paul Watts, Philip Breedon, and Francesco Luke Siena

Subjects
Mobile & Wireless Health, Computer science, Video Recording, Medicine (miscellaneous), Health Informatics, 02 engineering and technology, Rehabilitation Nursing, Health outcomes, Health informatics, Motion capture, 03 medical and health sciences, Motion, 0302 clinical medicine, Health Information Management, Motion Capture, Outcome Assessment, Health Care, 0202 electrical engineering, electronic engineering, information engineering, 3D Depth Camera, Humans, Clinical Trials, Medical systems, Clinical Trials as Topic, Intel® RealSense™, business.industry, Robotics, Facial tracking, Data science, Automation, Health Outcomes, Face, 3d camera, 020201 artificial intelligence & image processing, Artificial intelligence, business, 030217 neurology & neurosurgery, Information Systems
Abstract

Applications utilising 3D Camera technologies for the measurement of health outcomes in the health and wellness sector continues to expand. The Intel® RealSense™ is one of the leading 3D depth sensing cameras currently available on the market and aligns itself for use in many applications, including robotics, automation, and medical systems. One of the most prominent areas is the production of interactive solutions for rehabilitation which includes gait analysis and facial tracking. Advancements in depth camera technology has resulted in a noticeable increase in the integration of these technologies into portable platforms, suggesting significant future potential for pervasive in-clinic and field based health assessment solutions. This paper reviews the Intel RealSense technology’s technical capabilities and discusses its application to clinical research and includes examples where the Intel RealSense camera range has been used for the measurement of health outcomes. This review supports the use of the technology to develop robust, objective movement and mobility-based endpoints to enable accurate tracking of the effects of treatment interventions in clinical trials.

Published
2018

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