So this is an area where I can speak from experience. I was previously employed as the ML research scientist for a startup that designed and implemented an mmwave radar-based solution for the use case of seniors in independent living situations. That company fell apart for reasons unrelated to the technical side, unfortunately.
What we found is that seniors had essentially no desire to wear any sensors, which rules out the wearable inertial sensors mentioned in the paper. Also as others have mentioned, a sensor that captures visual images is a nonstarter due to privacy protections on a regulatory level but also privacy concerns on a personal level.
I'd add one other set of challenges that is unfortunately never covered in the academic literature - non-ideal rooms for monitoring signals. The papers show empty conference rooms with line-of-sight between sensors and people, but real settings are much messier. Not only is there furniture to block or distort signals, but also many sources of noise like fans, metal objects, open windows (which cause breezes to move curtains and other objects), pets, visitors, etc. Not to mention the unique room configurations for every person. We overcame several of these challenges but didn't develop perfect answers for many of them.
It was a fun position that gave me a weirdly specific set of knowledge that isn't always transferable on a technical level but was still great, and I wish it could have lasted longer. I'd be happy to share more info though if you're curious!
What we found is that seniors had essentially no desire to wear any sensors, which rules out the wearable inertial sensors mentioned in the paper. Also as others have mentioned, a sensor that captures visual images is a nonstarter due to privacy protections on a regulatory level but also privacy concerns on a personal level.
I'd add one other set of challenges that is unfortunately never covered in the academic literature - non-ideal rooms for monitoring signals. The papers show empty conference rooms with line-of-sight between sensors and people, but real settings are much messier. Not only is there furniture to block or distort signals, but also many sources of noise like fans, metal objects, open windows (which cause breezes to move curtains and other objects), pets, visitors, etc. Not to mention the unique room configurations for every person. We overcame several of these challenges but didn't develop perfect answers for many of them.
It was a fun position that gave me a weirdly specific set of knowledge that isn't always transferable on a technical level but was still great, and I wish it could have lasted longer. I'd be happy to share more info though if you're curious!