Abstract

Introduction
Social isolation has been found to be a significant risk factor for health outcomes, on par with traditional risk factors. This isolation is characterised by reduced social interactions, which can be detected acoustically. To accomplish this, we created a machine learning algorithm called SocialBit. SocialBit runs on a smartwatch and detects minutes of social interaction based on vocal features from ambient audio samples without natural language processing.
Methods and analysis
In this study, we aim to validate the accuracy of SocialBit in stroke survivors with varying speech, cognitive and physical deficits. Training and testing on persons with diverse neurological abilities allows SocialBit to be a universally accessible social sensor. We are recruiting 200 patients and following them for up to 8 days during hospitalisation and rehabilitation, while they wear a SocialBit-equipped smartwatch and engage in …

Metadata

publication

BMJ open 13 (8), e076297, 2023

year

2023

publication date

2023/8/1

authors

Kelly White, Samuel Tate, Ross Zafonte, Shrikanth Narayanan, Matthias R Mehl, Min Shin, Amar Dhand

link

https://bmjopen.bmj.com/content/13/8/e076297.abstract

resource_link

https://bmjopen.bmj.com/content/bmjopen/13/8/e076297.full.pdf

journal

BMJ open

volume

13

issue

8

pages

e076297

publisher

British Medical Journal Publishing Group