cc snips
1.One of these engagements is for a wearable product that will be introduced by a top ten smartphone supplier during Q1. This is a particularly exciting design win. This customer has high brand recognition and all of the algorithms used in this design were developed by QuickLogic.
2.This migration strategy is also enabling a Japanese OEM to use our sensor hub design in an innovative, wearable product. They initially started the design using our S1 and are scheduled to enter production during Q4 using our S2 platform.
Roth had
A smartwatch with IRDA
A wearable with LED status.
Who besides Samsung has IRDA on their platforms currently that makes watches?
LG for one and HTC
LG already has smartwatches on the market.They are top 10
HTC is NOT.
The Taiwanese smartphone maker is still working on a smartwatch and plans to release it in early 2015, according to people familiar with the matter. There had been reports that HTC had scrapped its plans because it felt it couldn't compete in the burgeoning category.
Put up the blog item
The S2′S Sensor Data Buffer Memory In The Real World
One of the generational enhancements we made on the ArcticLink 3 S2 was to increase the sensor data buffer memory by 800%. To wit, the logical question of the lay person might be “so what does that do for me?” That’s a great question: the Sensor Data Buffer Memory allows the ArcticLink 3 S2 ultra-low power sensor hub to store aggregated data. The data can be a variety of things; in the example I’ll use in the rest of this blog, I’ll reference the storage of step count data.
Before we go too far, let’s talk step counts:
A lot of us have fitness bands. While these devices are useful not only for motivation (“gotta get that step count to 10,000 to meet my goal” is a common user refrain), they also provide some basic fitness data. Basic, though, is the key word. Simply counting the number of steps provides a basic assessment of someone’s on-going fitness level; i.e., person A takes 1.5X the number of steps as person B, which leads you to believe that person A probably has a better fitness level. Now, that’s all well and good, but what if person A’s steps consist of leisurely strolling, whereas person B is running everywhere? Perhaps the fitness assessment might change?
More Accurate Fitness…
The trick to a more accurate fitness assessment is differentiating those steps, and classifying them more accurately. The AL3 S2 can be enabled with a QuickLogic-developed Enhanced Pedometer algorithm, which will differentiate between walking, jogging, and running steps taken, and report out the number for each. Obviously this can provide a much more accurate fitness assessment to the wearer.
+ cc snip
all of the algorithms used in this design were developed by QuickLogic.
my vote is that they use the enhanced pedometer algo in a fitness band, it one of the top 10 names in smartphones, so its not a fitbit etc.
I went through this list..quickly
Samsung na
Nok na
Apple na
Chinese ZTE, Hauwei, Lenovo, Coolpad na
that leaves 2 names...LG ans Sony.
This is just for fun, but I go with Sony. 10 ten Brand well known, they have been into bands a LOOONG time.
And, if you are wondering, we do have QuickLogic-developed Standard Pedometer which does count steps, with no differentiation between contexts.
Getting back to the Sensor Data Buffer Memory…
I mentioned the 800% increase in memory. In keeping with the step count example, the memory allows us to store a certain number of counts of steps based on time. That time scale is adjustable; most wearables today have either a 15 minute or 1 hour time scale, meaning the number of steps taken is reported out in 15 minutes or 1 hour intervals.
Lets tie that memory to real-world examples…
Lets say an OEM is developing a fitness band where they want to implement our Enhanced Pedometer. They are interested in long battery life, as is everyone. Rather than the band constantly pinging a smartphone over Bluetooth to report activity, they want the sensor hub to be able to store the step counts, differentiated by walking/jogging/running. For the AL3 S2, it’s no problem. If the time scale is every 15 minutes (the most advanced devices today provide this), the AL3 S2 is going to be able to store more than 7 days of step count data — and that includes separate counts for the different walk/jog/run contexts.
Same use case, but with standard pedometer? More than 21 days of memory. If we want to store data in a shorter time scale; lets say, every 10 minutes with Enhanced Pedometer? Still more than 4.5 days of memory.
Bottom line: the increase in sensor data buffer memory leads directly to an ability to store days (even weeks!) worth of fitness data, and can lead to longer battery life of a device because data is able to be stored locally instead of sent via battery-draining Bluetooth. And, when the Enhanced Pedometer is used, the ArcticLink 3 S2 can enable devices that provide a much more accurate fitness report, coupled with longer battery life.
No comments:
Post a Comment