No Pain, No Gain, Right?
By Jim Warrren (with input from Erich Stein) – first posted on Jabil’s Insight Blog
I was playing basketball over the weekend and noticed across the court a guy was doing some drills by watching his phone. And being pretty winded and looking for an excuse to cut my efforts short, I wandered over to ask what he was doing. As it turned out, he was returning to form after an ACL strain from a couple of months ago. He was doing physical therapy but also was using some exercises he’d found on YouTube to hopefully help him along. I asked if he was worried about overtraining as he pushed to return to form, his response, “no pain no gain, right?” And that got me thinking, what capabilities do we have in our space that can allow for rehab with just the right balance of pain and gain…?
In Jabil, and Nypro, we have loads of capabilities in sensing technology and a whole lot, of IoT and the manufacturing know-how to pull together great ideas and create real world products. So if Jabil was going to engage in helping with rehab, how might we proceed? And being that this is a pretty intense subject, I wanted to bring in one of our experts to chat about what could be done. Erich Stein holds a Doctorate in Biomedical Engineering and is one of Nypro’s Technical Business Unit Managers, in short that means he can help me temper my wild ideas into actionable efforts. But even better, he sees what I do as we apply our capabilities to areas such as patient recovery and he shares my interest in future technology being considered for today’s challenges. He is one of many like-minded folk at Jabil and always brings new insights to existing challenges.
As we do, we wanted to look at this challenge from a start, middle and completed state with establishing our initial base line. Erich likes the approach of regaining motion, correcting issues with correct form and monitoring correct performance as a means of tracking your path back to full form. We both agree that positional sensors embedded in clothing would be an ideal way of tracking our alignment. And once we have our sensors aligned specifically for our use, we can move to tracking improvement versus over exertion. Erich takes this a step further in how a patient can monitor their own care.
Erich likes to take the holistic concept and apply it to a care eco-system, allowing the patient’s effort to build a state of care it self moving towards a final correction. Erich suggests, “If we look at this idea as a care eco-system, the positional data collected, plus additional health monitors that capture respiratory rate (blood saturation) and heart rate, we could provide some great input to an app that, when paired with virtual Physical Therapy inputs, could not only create a recovery plan, but could help the patient adhere to the plan.” Another cool output could be a “tracker” that lets the patient know how close they are to “full recovery”, as well as a video library of rehab movements, and a “dial a Physical Therapist” feature for instant access to a professional.
These measurements need to be made accurately. Erich has some thoughts there as well, suggesting that, a couple of questions come to mind. How to measure range of motion, how to measure alignment, and how to measure over-exertion? The first two are a given –embedded positional sensors in fabric. These sensors could actively provide their location to an app or they can be passive, where the app “sees” them like the old-school biomechanics models, where ping pong balls were glued to the subject, or more high-tech like the app that can design a custom fitted dress shirt by taking several images of the subject. As we are leveraging technology, active sensors make more sense.
Erich and I discussed data management and how predictive analytics could play a role in “returning to form”. So then what about over-exertion? This one is tricky, but Erich would say it is plausible to use the positional sensors to determine a few parameters that could be correlated to over-exertions: repetitions and consistency in form. Erich argues that if someone is lifting too heavy, the joint would likely deform from normal. Positional sensors could quantify joint strain and provide a warning that an injury could occur. I would argue that if someone is doing too many repetitions, losing form/tone could predict a soon-to-be fatigue injury. Positional sensors could determine the number of reps performed, as well as monitor joint tone. The data could be used modulate effort to keep the patient from “re-injuries”.
Jabil and Nypro are the one stop solution to connect these technologies, create products, ecosystems and an achievable manufacturing plan. Bringing in technical experts, like Erich, can helpyou can move beyond the concept and into reality.
