I’ve spoken in previous blogs about how the ArcticLink 3 S1 enables always-on context awareness through it’s ultra-low power consumption. Let’s talk about specific use cases and applications it can enable:
Smartphones with environmental sensors (temperature, humidity, pressure) can be used to provide well-baby monitoring. Many parts of the world are very dry at certain parts of the year, and humidity (or lack of) can often be a key factor in respiratory illnesses in children. A smartphone could be deployed as monitor while the child sleeps, alerting parents and caregivers to conditions and allowing them to make changes as necessary.
As we age, other health factors come into play. One of the leading causes of injury in the elderly is falling. While there are existing monitoring technologies for this, these technologies require that your monitor be within range of a transmitting station. A smartphone, with a 9DOF sensor subsystem and sensor algorithms, is fully capable of detecting the user falling (versus the phone being dropped), and with the right application, notifications could be sent to emergency medical personnel, friends, family, and even neighbors. Future smartphones may be capable of measuring the users heartbeat, respiration, blood pressure — even perhaps things like blood sugar. Always-on context awareness allows real-time monitoring of this without imposition to the user.
Many people today use dedicated fitness monitoring products like FitBit, and many also take advantage of smartphone-based fitness applications like those preloaded on newer devices. However, fitness monitoring products often fail in that they fail to recognize the user’s context — our CEO, Andy Pease, rides his bicycle to work almost every day, and his dedicated fitness monitoring device fails to recognize the fact he is biking, and doesn’t include the distance (and associated calorie burn) associated with it. With many manually-activated smartphone-based fitness programs, the sensor sub-system is quite adept at counting steps — as long as the device is in your pocket or hand. What is the user places the phone onto a baby stroller or cart they push in front of them? In this case, the steps aren’t counted. An always-on context aware sensor hub, coupled with intelligent sensor algorithms, solves these user experience issues. The sensor hub + sensor algorithms will recognize the user is biking, and automatically provide that information to device-based applications that will provide correct, activity-based fitness data. If a pressure sensor is present in the phone, the sensor hub could use that information to determine is the user is walking up and down hills to even more accurately judge caloric burn. No pressure sensor? Well, the smartphone could take advantage of data fusion by mapping the users activity versus terrain information provided by a mapping program, and map elevation changes in the users path to increased or decreased caloric burn.
- Driver Texting
Texting while driving is illegal in many places. Always-on context awareness enables phones to determine whether the user is (A) in a car and (b) in the drivers seat, and could disable various features in the phone, including texting. Further, parents could activate this feature remotely on their children’s devices, removing the possibility of texting and hopefully making that driver (and those around him/her) safer.
- Indoor Navigation and Pedestrian Dead Reckoning
How often have you been in a shopping mall and lost your way? Been in a airport and can’t easily locate your gate or a restroom? As GPS antennas are extremely power hungry, most smartphone users don’t leave them on all the time. Further, they become useless once you enter a building, because the GPS signal doesn’t penetrate walls. Once a user enters a building, always-on context awareness can be used to automatically count the users steps, step distance and direction, providing real-time location that can be used in data fusion to plot exactly where in the building the user is. This is useful for personal mapping, and even potential useful in search and rescue situations. Further, the inclusion of pressure sensor data would allow the device to not only know GPS coordinates (X and Y), but also height (Z), allowing the device (and applications) to know what floor the user is on as well. GPS also can be inaccurate when walking in cities with tall buildings, as these buildings can obscure signals, leading to situations where after a day of traveling by foot, your phone could think you a block or more aware from where you actually are. Pedestrian Dead Reckoning refers to the ability of the sensor subsystem to accommodate for this and provide a true location.
- Environmental Monitoring
Every year hundreds, if not thousands, of people die in natural disasters. While no sensor hub can prevent these disasters, they can be used as early warning systems. Lets use tornado’s as an example — while places like ‘tornado alley’ in the United States have a network of early warning systems, always-on context awareness would allow an smartphone with a pressure monitor (and other environmental sensors) to become essentially a weather station. Think about it — millions of weather stations constantly moving and providing data in real-time. While tornado’s do take some time to manifest and people are able to be warned, earthquakes strike suddenly and without warning, and effect larger areas. However, smartphones can be used to detect the immediate shaking of an earthquakes (versus a persons hand, for instance), and earthquake alerts could be sent out as the shaking propagates. While likely not being able to provide enough warning for people to get to safe places, it could perhaps allow enough warning time for a surgeon to not make a cut into a patient. This data can also be used to target search and rescue efforts, as well as to locally measure aftershock data.
- Targeting Advertising
We’ve likely all been in situations where we are looking for something to eat, and have browsed the menu’s in front of multiple restaurants before deciding where to dine. Using a combination of always-on and data fusion, our phone’s could identify from that context (walking to the location of a restaurant, stopping, then walking to another restaurant, stopping, and so forth) that we are hungry, and then could engage in targeted advertising for a local restaurant. Obviously most of us would prefer this type of advertising be on an opt-in basis only, but in this situation most people likely would appreciate a “10% off” coupon appearing on our device from the pizza place just around the corner, along with a confirmation of an open table for you, a link to consumer reviews of the site, a menu, and even step-by-step directions on how to get there. All of this is possible — today — but it requires always-on context to enable this.
- Consumer Behavior Studies
Finally, let’s look at a problem advertisers have always faced — how to measure the true effectiveness of what their campaigns. Certainly some people will judge effectiveness by product sales — but what is the campaign stunk but the product was just that good? Further complicating this is the Observer-expectancy effect that states that when we know we are being watched, we alter our natural behavior.
What if advertisers and marketers could post two advertisements near each other, and measure how many people stop and look at the one versus the other, and for how long? And do this without the test subjects knowing they were part of a study? Always-on context enables mobile operating systems to be used to create macro data of consumer behavior — that is, how many users stopped and turned towards advertisement A (and for how long)? and the same for advertisement B? With the billions spent yearly on print and location-based advertising, this data could prove invaluable to companies looking to refine their messaging.
These are just a few of the ideas I’ve heard — always-on context awareness enables a whole host of applications, from the health to advertising, and the list will just keep on growing!