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Best Fitness Trackers for Tech Lovers

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January is here and it’s time to make those New Year’s resolutions a reality. If your goal for the next 12 months is to get back in shape, you might want to treat yourself to a fitness tracker.

These wearable devices are like having a personal health coach: They help monitor your heart rate, count calories, track sleep patterns, and elevate your exercise routine. Even though some fitness trackers don’t come cheap, there are more affordable models available that come with cool tech features and get the job done.

Whether you’re looking to elevate your cardio or count daily steps, there’s a fitness tracker out there for your wellness needs. From the Garmin Vivofit 3 to the Fitbit Charge 2, here are the best fitness trackers for tech lovers.

Garmin Vivofit 3

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Sleek and streamlined, the Garmin Vivofit 3  will give you daily step reminders, exercise activity progress, and sleep monitoring for less than $70.

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LETSCOM Fitness Tracker

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The LETSCOM Fitness Tracker monitors your heart rate, counts calories, and offers smart connectivity, so you won’t miss incoming calls or texts when you’re at the gym or park. It’s also waterproof, so you can wear it during water sports activities, including polo and swimming.

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HuaWise Fitness Tracker

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Long battery life and different sports modes make the HuaWise Fitness Tracker a top health tech pick. Whether you’re performing yoga or running laps on the track, this fitness tracker will have good power for a few hours.

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Motiv Ring

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Unlike other fitness trackers, the Motiv Ring doesn’t go on the wrist. Instead, it fits comfortably on your finger, and it provides all the high-tech perks, including sleep tracking, calorie alerts, and daily distance progress.

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Fitbit Flex 2

Photo Credit: Amazon

If you’re a minimalist when it comes to fitness trackers, the Fitbit Flex 2 is a sleek wearable without all the glitz. SmartTrack automatically recognizes which exercises you’re doing, while a colorful LED light display notifies you about incoming calls, texts, and social media notifications.

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MorePro X-Core Fitness Activity Tracker 

Photo Credit: Amazon

Conveniently manage your heart rate, calories, distance, and steps with the MorePro X-Core Fitness Activity Tracker: it has a display screen that’s easy to read, plus it also tells you the time.

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Apple Watch Series 4

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Take your fitness to new heights with the Apple Watch Series 4: There’s electrical and optical heart sensors, a 64-bit dual-core processor, an improved gyroscope for fall detection, and a GPS, so you’ll always know where to go for your next workout.

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Xiaomi Mi Band 2

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Daily, weekly, and monthly fitness tracking is a perk of the Xiaomi Mi Band 2. You’ll get to see your beats per minute (bpm), exercise activities, and more with the Mi Fit app, which syncs with this fitness tracker.

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Kalinco Fitness Tracker

Photo Credit: Amazon

If you’re looking for an affordable exercise wearable, the Kalinco Fitness Tracker is a top pick: You’ll get to track calories, monitor your heart rate, and easily charge it at a fraction of the price.

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Fitbit Charge 2

Photo Credit: Amazon

For advanced fitness tracking, the Fitbit Charge 2 provides three different heart rate zones, a personalized cardio fitness score, the number of calories burned, and gives you incoming call, text, and social media notifications.

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New Florida Law Nixes Need for Autonomous Vehicle Operators

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Florida Gov. Ron DeSantis last week signed a bill removing “unnecessary obstacles that hinder the development of autonomous vehicle technology”—including backup drivers.

The new law, which takes effect July 1, will allow a self-driving car (meeting all insurance requirements) to run without a human operator.

It also exempts occupants from laws against texting and other distractions.

“Signing this legislation paves the way for Florida to continue as a national leader in transportation innovation and technological advancement,” DeSantis said in a statement.

Flanked by smiling supporters, the governor on Thursday signed House Bill 311 at the SunTrax transportation center, used to test autonomous vehicles.

Also in attendance were Republican bill sponsors Sen. Jeff Brandes and Rep. Jason Fischer.

Florida Gov. Ron DeSantis signed a bill allowing autonomous vehicle tests with no human operator (via Gov. DeSantis Press Office)

“We here in Florida are pioneering the most exciting innovations in transportation,” Fischer said. “This bill on self-driving cars will usher in a new era of smart cities that will not only expand our economy but increase road safety and decrease traffic congestion.”

And permit “active display” of TV or video in the car.

The Sunshine State isn’t exactly breaking new ground: Last year, the California DMV introduced new regulations allowing automakers to test and deploy fully driverless vehicles.

We still have a long way to go, though, before folks can start napping behind the wheel.

High-profile accidents—an autonomous Uber struck and killed a pedestrian in Arizona; Tesla’s Autopilot feature was engaged at the time of a fiery Model X crash in California—have left some mistrustful of self-driving cars.

Others, meanwhile, are ready and willing to move forward with the unpredictable technology.

“Autonomous vehicles are the way of the future and Florida is leading the charge through the research, testing, and development of autonomous vehicles,” according to state Department of Transportation Secretary Kevin Thibault. “And now with this bill signed into law … Florida is ready to lead the nation with this innovative transportation advancement.”

Brandes agreed, adding that “With the signing of this legislation we reaffirm our bold commitment to lead the country as we transition to a shared, electric and driverless future.”

In February 2018, Ford unveiled a self-driving delivery pilot program that would see autonomous vehicles roaming the streets of Miami—notorious for its traffic congestion.

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MIT Robot Learns to ID Objects by Sight, Touch

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Humans’ five senses work together to reveal what we see, hear, smell, taste, and touch.

But robots are still learning to understand different tactile signals.

To move the process along, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) developed a predictive AI that can learn to see by touching, and learn to feel by seeing.

The system creates realistic signals from visual inputs to predict with which object it is making physical contact.

Using a KUKU robot arm and GelSight tactile sensor (designed by another group at MIT), researchers recorded nearly 200 objects—tools, household products, fabrics, etc.—being touched more than 12,000 times.

By breaking down those video clips into static frames, the team compiled a dataset of more than 3 million visual/tactile-paired images, known as “VisGel.”

“By looking at the scene, our model can imagine the feeling of touching a flat surface or a sharp edge,” lead study author Yunzhu Li, a CSAIL PhD student, said in a statement.

“By blindly touching around, our model can predict the interaction with the environment purely from tactile feelings,” he continued. “Bringing these two senses together could empower the robot and reduce the data we might need for tasks involving manipulating and grasping objects.”

During testing, if the model was fed tactile data on a shoe, for instance, it could produce an image of where the shoe was most likely to be touched. The same goes for a computer mouse, box, cup, T-shirt, hammer—whatever its automated heart desires.

This type of ability, CSAIL said, could be useful for tasks in which there is no visual data: like when a light is off, or someone is indiscriminately reaching into an unknown area.

Moving forward, the team plans to increase the size and diversity of its dataset  by collecting input in more unstructured areas (i.e. outside a controlled environment). Or by using a new MIT-designed tactile smart-glove.

Even with the help of a sensor-packed mitt, there are certain details that can be tricky to infer from switching models—details that even humans can’t ascertain without using more than one sense. Like identifying the color of an object by touching it or determining how soft a sofa is without actually pressing on it.

CSAIL invented a similar system earlier this year: The “RoCycle” uses a soft Teflon hand covered in tactile sensors to detect an object’s size and stiffness—no visual cues necessary.

Basically, it squeezes cups, boxes, and cans to determine their makeup, and, ultimately, their recyclability.

A collaboration with Yale University, RoCycle demonstrates the limits of sight-based sorting; it can distinguish between two identical-looking Starbucks cups made of paper and plastic that would give vision systems (and the human eye) trouble.

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Deepfake Tool Makes It Easy to Put Words Into Someone’s Mouth

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Changing what someone says in a video is now as easy as “copy and paste.”

Researchers developed new software that uses machine learning to let users edit the text transcript of a video, altering the very words coming out of a person’s mouth.

The team—from Stanford University, the Max Planck Institute for Informatics, Princeton University, and Adobe Research—envision their technology being used by film and television editors.

“Much like word processing, the editor could easily add new words, delete unwanted ones, or completely rearrange the pieces by dragging and dropping them as needed to assemble a finished video that looks almost flawless to the untrained eye,” according to a Stanford press release.

A new algorithm allows video editors to modify talking-head videos as if they were editing text—copying, pasting, adding and deleting words (via Stanford University)

The algorithm works best with talking-head videos, which show speakers only from the shoulders up; hand gestures and other body movements are a dead giveaway.

“The work could be a boon for video editors and producers but does raise concerns as people increasingly question the validity of images and videos online,” the authors said.

Say, for example, an actor flubs their line: The editor can simply rewrite the transcript, and the application will assemble the right word from various phrases spoken elsewhere in the recording.

It’s a bit like when surgeons transplant skin from one area of the body to another. It’s the skin grafting of video production.

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The machine-learning element then converts those sounds into a final video that appears natural to the viewer. Intelligent smoothing and Neural Rendering also work to create a photorealistic video in perfect lip-synch.

“Visually, it’s seamless. There’s no need to re-record anything,” lead researcher Ohad Fried, a postdoctoral scholar at Stanford, said in a statement.

In a crowd-sourced study with 138 participants, the team’s edits were rated as “real” almost 60 percent of the time. There is, of course, still room for improvement.

The algorithm currently requires at least 40 minutes of original video as input, and won’t yet work with just any sequence.

In an era of fake news, Internet hoaxes, and revenge porn, letting this technology fall into the wrong hands could be disastrous.

“This technology is really about better storytelling,” Fried said, acknowledging concerns about the software being used for illicit purposes.

Editing video is as easy as editing text (via Stanford University)

“Unfortunately, technologies like this will always attract bad actors,” he added. “But the struggle is worth it given the many creative video-editing and content-creation applications this enables.”

In an effort to curb rabble-rousers, researchers have proposed guidelines for using these tools that would alert viewers and performers that a video has been manipulated.

An opt-in watermarking system, perhaps, to identify edited content. Or digital/non-digital fingerprinting techniques.

None of these solutions are comprehensive, though; viewers must remain skeptical and cautious, Fried said.

The most pressing matter, he suggested, is to raise public awareness and education on video manipulation, so people are better equipped to question and assess synthetic content.

The full report—available online—will be published in the journal ACM Transactions on Graphics.

Watch: ‘Minority Report’-Like AI Can Detect Shoplifting

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