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Google to offer heart and respiratory rate measurements using just your smartphone’s camera

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Google is introducing features that will allow users to take vital health measurements using just the camera they already have on their smartphone, expanding health and fitness features typically only available on dedicated wearables to a whole new group of people. Beginning next month, and available initially on Google Pixel phones exclusively (but with plans to offer it for other Android devices in future), users will be able to measure both their heart rate and their respiratory rate using just their device’s camera.

Typically, taking these measurements has required specialized hardware, including red or green light-based heart rate monitors like those found on the Apple Watch or on fitness trackers like those made by Google-acquired Fitbit. Google’s hardware and software teams, including the Google Health unit led by Director of Health Technologies Shwetak Patel, have managed to develop computer vision-based methods for taking these measurements using only smartphone cameras, which it says can produce results that are comparable to clinical-grade measurement hardware (it has produced a study to validate these results, which it’s making available in pre-print format while it seeks peer review through an academic journal).

For respiratory rate, the technology relies on a technique known as ‘optical flow,’ which monitors movements in a person’s chest as they breathe and uses that to determine their breathing rate. In its clinical validation study, which covered both typical individuals in good health, and people with existing respiratory conditions, Google’s data indicates that it’s accurate to within 1 breath per minute across all participants.

For heart rate, Google is initially using the camera to detect “subtle color changes” in a user’s finger tip, which provide an indicator about when oxygenated blood flows from your heart through to the rest of your body. The company’s validation data (again, still subject to external review) has shown accuracy within 2% margin of error, on average, across people with a range of different skin types. Google is also working on making this same technology work using color changes in a person’s face, it says, though that work is still in the exploratory phase.

Google is going to make these measurement features available to users within the next month, it says, via the Google Fit app, and initially on currently available Pixel devices made by the company itself. The plan is then to expand the features to different Android devices running Android 6 or later, sometime “in the coming months.”

Image Credits: Google

“My team has been working on ways that we can unlock the potential of everyday smart devices,” Patel said in a press briefing regarding the new features. This would include smart devices in the home, or a mobile phone, and how we leverage the sensors that are starting to become more and more ubiquitous within those devices, to support health and wellness.”

Patel, who is also a computer science professor at the University of Washington and who has been recognized with an ACM Prize in Computing Award for his work in digital health, said that the availability of powerful sensors in ubiquitous consumer devices, combined with advances in AI, have meant that daily health monitoring can be much more accessible than ever before.

“I really think that’s going to be a really important area moving forward given that if you think about health care, the journey just doesn’t end at the hospital, the four walls of the hospital,” he said. “It’s really this continuous journey, as you’re living your daily life, and being able to give you feedback and be able to measure your general wellness is an important thing.”

It’s worth noting that Google is explicit about these features being intended for use in a person’s own tracking of their general wellbeing – meaning it’s not meant as a diagnostic or medical tool. That’s pretty standard for these kinds of features, since few of these companies want to take of the task of getting full FDA medical-grade device certification for tools that are meant for general consumer use. To that end, Google Fit also doesn’t provide any guidance or advise based on the results of these measurements; instead, the app provides a general disclaimer that the results aren’t intended for medical use, and also offers up some very high-level description of why you’d even want to track these stats at all.

Many of the existing dedicated wellness and health tracking products on the market, like the Oura ring, for instance, provide more guidance and actionable insight based on the measurements it takes. Google seems intent on steering well clear of that line with these features, instead leaving the use of this information fully within the hands of users. That said, it could be a valuable resource to share with your physician, particularly if you’re concerned about potential health issues already, in place of other less convenient and available continuous health monitoring.

Patel said that Google is interested in potentially exploring how sensor fusion could further enhance tracking capabilities on existing devices, and in response to a question about potentially offering this on iPhones, he said that while the focus is currently on Android, they ultimate goal is indeed to get it “to as many people as possible.”

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MealMe raises $900,000 for its food search engine

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This morning MealMe.ai, a food search engine, announced that it has closed a $900,000 pre-seed round. Palm Drive Capital led the round, with participation from Slow Ventures and CP Ventures.

TechCrunch first became familiar with MealMe when it presented as part of the Techstars Atlanta demo day last October, mentioning it in a roundup of favorite startups from a group of the accelerator’s startup cohorts.

The company’s product allows users to search for food, or a restaurant. It then displays price points from various food-delivery apps for what the user wants to eat and have delivered. And, notably, MealMe allows for in-app checkout, regardless of the selected provider.

The service could boost pricing and delivery-speed transparency amongst the different apps that help folks eat, like DoorDash and Uber Eats. But Mealme didn’t start out looking to build a search engine. Instead it took a few changes in direction to get there.

From social network to search engine

MealMe is an example of a startup whose first idea proved only directionally correct. The company began life as a food-focused social network, co-founder Matthew Bouchner told TechCrunch. That iteration of the service allowed users to view posted food pictures, and then find ordering options for what they saw.

While still operating as a social network, MealMe applied to both Y Combinator and Techstars, but wasn’t accepted at either.

The startup discovered that some of its users were posting food pics simply to get the service to tell them which delivery services would be able to bring them what they wanted. From that learning the company focused on building a food search engine, allowing users to search for restaurants, and then vet various delivery options and prices. That iteration of the product got the company into Techstars Atlanta, eventually leading to the demo day that TechCrunch reviewed.

During its time in Techstars, the company adjusted its model to not merely link to DoorDash and others, but to handle checkout inside of its own application. This captures more gross merchandize value (GMV) inside of MealMe, Bouchner explained in an interview. The capability was rolled out in September of 2020.

Since then the company has seen rapid growth, which it measures at around 20% week-on-week. During TechCrunch’s interview with MealMe, the company said that it had reached a GMV run rate of more than $500,000, and was scaling toward the $1 million mark. In the intervening weeks the company passed the $1 million GMV run-rate threshold.

MealMe was slightly coy on its business model, but it appears to make margin between what it charges users for orders and the total revenue it passes along to food delivery apps.

TechCrunch was curious about platform risk at MealMe; could the company get away with offering price comparison and ordering across multiple third-party delivery services without raising the ire of the companies behind those apps? At the time of our interview, Bouchner said that his company had not seen pushback from the services it sends users to. His company’s goal is to grow quickly, become a useful revenue source for the DoorDashes of the world, and then reach out for some of formal agreement, he explained.

“We continue to be a powerful revenue generator and drive thousands of orders to food delivery services per week,” the co-founder said in a written statement. Certainly MealMe found investors more excited by its growth than concerned about Uber Eats or other apps cutting the startup off from their service.

What first caught my eye about MealMe was the realization of how much I would have used it in my early 20s. Perhaps the company can find enough users like my younger self to help it scale to sufficient size that it can go to the major food ordering companies and demand a cut, not merely avoid being cut off.

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Apple supplier Foxconn reaches tentative agreement to build Fisker’s next electric car

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Apple supplier Foxconn Technology Group has reached a tentative agreement with electric vehicle startup-turned-SPAC Fisker to develop and eventually manufacture an EV that will be sold in North America, Europe, China and India.

Fisker and Foxconn said Wednesday that a memorandum of understanding agreement has been signed. Discussions between the two companies will continue with the expectation that a formal partnership agreement will be reached during the second quarter of this year. 

Under the agreement, Foxconn will begin production in the fourth quarter of 2023 with a projected annual volume of more than 250,000 vehicles. The electric vehicle will carry the Fisker brand.

Foxconn Technology Group Chairman Young-way Liu touted the company’s vertically integrated global supply chain and accumulated engineering capabilities, noting that it gives the company two major advantages in the development and manufacturing of the key elements of an EV, which includes the electric motor, electric control module and battery.

That supply chain and ability to scale engineering quickly will be critical for Foxconn if it hopes to meet its production target.

“The collaboration between our firms means that it will only take 24 months to produce the next Fisker vehicle — from research and development to production, reducing half of the traditional time required to bring a new vehicle to market,” Young-way Liu said in a statement.

Fisker said production of the Ocean SUV — its first EV and one that is supposed to be built by contract manufacturer Magna — will begin in the fourth quarter of 2022. The company said it plans to unveil a production-intent prototype of the Ocean later this year.

This is not Foxconn’s first foray into electric vehicle manufacturing.

Foxconn announced in January 2020 that it had formed a joint venture with Fiat Chrysler Automobiles to build electric vehicles in China. Under that agreement, each party will own 50% of the venture to develop and manufacture electric vehicles and engage in an IOV, what Foxconn parent company Hon Hai calls the “internet of vehicles” business.

Last month, Foxconn and Chinese automaker Zhejiang Geely Holding Group agreed to form a joint venture focused on contract manufacturing for automakers, with a specific focus on electrification, connectivity and autonomous driving technology as well as vehicles designed for sharing.

The joint venture between Foxconn and Geely will provide consulting services on whole vehicles, parts, intelligent drive systems and other automotive ecosystem platforms to automakers as well as ridesharing companies. Geely said it will bring its experience in the automotive fields of design, engineering, R&D, intelligent manufacturing, supply chain management and quality control while Foxconn will bring its manufacturing and Information and Communication Technology (ICT) know-how.

 

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Select Star raises seed to automatically document datasets for data scientists

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Back when I was a wee lad with a very security-compromised MySQL installation, I used to answer every web request with multiple “SELECT *” database requests — give me all the data and I’ll figure out what to do with it myself.

Today in a modern, data-intensive org, “SELECT *” will kill you. With petabytes of information, tens of thousands of tables (on the small side!), and millions and perhaps billions of calls flung at the database server, data science teams can no longer just ask for all the data and start working with it immediately.

Big data has led to the rise of data warehouses and data lakes (and apparently data lake houses), infrastructure to make accessing data more robust and easy. There is still a cataloguing and discovery problem though — just because you have all of your data in one place doesn’t mean a data scientist knows what the data represents, who owns it, or what that data might affect in the myriad of web and corporate reporting apps built on top of it.

That’s where Select Star comes in. The startup, which was founded about a year ago in March 2020, is designed to automatically build out metadata within the context of a data warehouse. From there, it offers a full-text search that allows users to quickly find data as well as “heat map” signals in its search results which can quickly pinpoint which columns of a dataset are most used by applications within a company and have the most queries that reference them.

The product is SaaS, and it is designed to allow for quick onboarding by connecting to a customer’s data warehouse or business intelligence (BI) tool.

Select Star’s interface allows data scientists to understand what data they are looking at. Photo via Select Star.

Shinji Kim, the sole founder and CEO, explained that the tool is a solution to a problem she has seen directly in corporate data science teams. She formerly founded Concord Systems, a real-time data processing startup that was acquired by Akamai in 2016. “The part that I noticed is that we now have all the data and we have the ability to compute, but now the next challenge is to know what the data is and how to use it,” she explained.

She said that “tribal knowledge is starting to become more wasteful [in] time and pain in growing companies” and pointed out that large companies like Facebook, Airbnb, Uber, Lyft, Spotify and others have built out their own homebrewed data discovery tools. Her mission for Select Star is to allow any corporation to quickly tap into an easy-to-use platform to solve this problem.

The company raised a $2.5 million seed round led by Bowery Capital with participation from Background Capital and a number of prominent angels including Spencer Kimball, Scott Belsky, Nick Caldwell, Michael Li, Ryan Denehy and TLC Collective.

Data discovery tools have been around in some form for years, with popular companies like Alation having raised tens of millions of VC dollars over the years. Kim sees an opportunity to compete by offering a better onboarding experience and also automating large parts of the workflow that remain manual for many alternative data discovery tools. With many of these tools, “they don’t do the work of connecting and building the relationship,” between data she said, adding that “documentation is still important, but being able to automatically generate [metadata] allows data teams to get value right away.”

Select Star’s team, with CEO and founder Shinji Kim in top row, middle. Photo via Select Star.

In addition to just understanding data, Select Star can help data engineers begin to figure out how to change their databases without leading to cascading errors. The platform can identify how columns are used and how a change to one may affect other applications or even other datasets.

Select Star is coming out of private beta today. The company’s team currently has seven people, and Kim says they are focused on growing the team and making it even easier to onboard users by the end of the year.

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