I think today, and I’m a little biased, but I think that the digital measures that we hope to become digital biomarkers that show a lot of promise are digital voice. And the reason is because, one, they’re very easy to collect; we have lots of recording devices, so there’s a lot of data that’s readily available. The other thing is that speaking is a very complex, cognitively complex task; it also has emotionality in it, so that digital voice signal actually has a lot of dimensions to it...
I think today, and I’m a little biased, but I think that the digital measures that we hope to become digital biomarkers that show a lot of promise are digital voice. And the reason is because, one, they’re very easy to collect; we have lots of recording devices, so there’s a lot of data that’s readily available. The other thing is that speaking is a very complex, cognitively complex task; it also has emotionality in it, so that digital voice signal actually has a lot of dimensions to it. And from that, you can extract both cognitive-related, probably, you know, speech-related measures that are much more motoric-related, and then, of course, emotional, you know, mental health-related. So from that one voice recording, likely multiple voice recordings, you’ll be able to extract from it multiple types of indicators. So I think that that’s pretty exciting. The other thing is that most people speak. So, you know, one of the problems that we always have in thinking about how to measure different kinds of cognitively related behaviors is how do we do that regardless of someone’s education, their language, and their culture. So people who may not have had formal education or perhaps even have, you know, some learning disabilities, for instance, may not be able to read and write, but they usually can speak. So it’s something that’s ubiquitous to most people and it’s easily collected and it’s very rich in information.
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