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Start-ups lack competence in health, fitness wearables

24 Oct 2014  | Rick Merritt

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Crowd-funded start-ups promoting wearable health and fitness products are unfit, according to Gregory Kovacs, a serial entrepreneur with degrees in electrical engineering and medicine. The Stanford professor said that the big opportunities belong to major players who can create broad ecosystems around what ultimately will become FDA-approved devices.

"There are many opportunities in consumer health, but there is no start-up model that has the scope and scale it will take—this won't be a Kickstarter project," said Kovacs who has co-founded two medtech start-ups. "The major medtech companies are all looking at this area carefully, but the people who build mobile devices have the most to lose, so I am hoping for joint ventures between medtech and consumer companies."

Kovacs poked fun at consumer health start-ups in a talk at an IEEE symposium. "Strap your Fitbit to your dog or cat for a few days, and it will report something incredible like 15,000 calories burned," Kovacs said.

The Endomondo app, which works with several devices, recently reported he ran 50.9mph, but credited him zero calories for the effort. "It could not figure out I have a new car—it's absolutely ridiculous," he said

Today's products "create awful data without validation" in part because many measure from the wrist, which is a relatively poor area to choose.

He lampooned the health start-up market as "a ridiculous space." For example, he noted Intel bought watchmaker Basis for an estimated $150 million, and patch maker Quanttus raised $22 million earlier this year although it's not clear either company has any significant patents.

"These start-ups are built to sell their companies not products," Kovacs said. "They think a Google or Apple will buy them" whatever they do, he said, predicting "financial carnage" in the sector.

Prescription for better ventures

The good news is, "[t]here is a huge opportunity to make money here" for larger companies with resources, which take a smart approach to products.

Kovacs provided a wealth of tips to design teams pursing this space, including:

  • Measure meaningful physiological parameters
  • Make accurate measurements
  • Suggest beneficial behaviour changes
  • Fit into the flow of a user's life
  • Be designed to look good
  • Monetise products through data analysis, not hardware

Although it's possible to find or invent proprietary sensors that provide product differentiation, general algorithms, not sensors, are the key to success.

  • There are a thousand new articles on sensors every year and companies are combing through journals looking for them, but there are very few meaningful opportunities for new sensors. However, existing sensors are a gold mine for building algorithms, and sensor fusion is very valuable. Intelligent algorithms can turn a swamp of crappy data into valuable insights for consumer health.

Specifically, he said, smartphones can make intelligent inferences by combining information about a user's movements, location and calendar, such as a meeting with a boss. "If you have a heart rate of 180 beats/minute while running that's great, but if you have it at 3am while in bed that's horrible," he said.

Still there are plenty of challenges such as keeping health data private. "We need an anti-Google, a secure data harbour, because if it's my data on Google I have no rights to it," he said.

Medical challenges are also huge. Two of the most common measurements—blood pressure and blood glucose levels—have routine spikes and troughs that are rarely measured.

"We have no idea what appropriate sampling rates are even for most common metrics—we should be embarrassed about this, and engineers should fix it," he said.

He pointed to rich, untapped sources of consumer health such as toilets, but "like most medical data we flush it," he said. Toothbrushes are another missed opportunity. "If you are sticking something it in an orifice, why waste that data," he quipped.

Companies in this space need to get over their tendency to avoid regulatory approvals which, he said, can take as little as six months. "The closer you get to meaningful data, the closer you get to the FDA," he said.




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