In today's fast-paced digital age, technology has become an integral part of our lives. While it offers numerous benefits and conveniences, excessive use can lead to digital overload, affecting mental health, productivity, and overall well-being. Adopting a technology diet involves conscious efforts to reduce screen time and strike a healthier balance in our tech consumption. Here are five effective strategies to kickstart your technology diet and achieve a more balanced lifestyle: 1. Set Clear Boundaries and Goals Establishing clear boundaries is the cornerstone of a successful technology diet. Start by identifying the areas where excessive technology use affects your life negatively. It could be spending excessive time on social media, binge-watching shows, or continuously checking emails. Set realistic goals to reduce screen time gradually, such as limiting social media use to a specific timeframe or designating tech-free zones and hours at home. Consider using apps or devi...
How AI Overcomes the Challenges of Indoor Asset Tracking in Hospitals
In IoT applications, AI is most usually employed at the “top
cease” of the data stack – operating on massive datasets, regularly from
multiple assets. In a medical institution putting, for instance, AI and RTLS
might be used for predictive analytics: can you predict the rate of ER
admissions based at the climate? Can you higher estimate while gadget requires
protection based on usage?
At the “backside cease” of every IoT stack, but, AI is
starting to be carried out to the sensors themselves with a completely vital
effect: AI permits low-high-quality sensors to gain very extraordinary
performance, delivering a return on funding that’s been absent in lots of IoT
answers till now.
AI and RTLS
One software of AI in sensors is in actual-time region
structures (RTLS). AI and RTLS are hired in lots of industries to hold song of
transferring assets to better monitor, optimize and automate essential
processes.
A easy example in a clinic is the control of clean system
rooms – garage rooms unfold throughout a health facility where clean system is
staged for use. A nurse requiring a chunk of gadget should be able to discover
precisely what they need in a smooth room.
However, if the easy room stock level is not maintained
successfully then device won't be to be had, forcing a lengthy search that
impacts affected person safety and workforce productiveness, ultimately forcing
hospitals to over-buy highly-priced gadget (regularly double) to make certain
there's an extra of availability.
If you can determine the location of device robotically, you
could effortlessly keep song of the wide variety of to be had devices in every
smooth room and automatically cause replenishment when inventory runs low. This
is one use of RTLS wherein the requirement is to decide which room a device is
in. Is it in a patient room? Then it’s not to be had. Is it in a easy room?
Then it contributes to the rely of to be had gadgets.
Determining which room a device is located in with very high
self belief is therefore paramount: a area blunders that makes you watched that
the three IV pumps you are looking for are in affected person room 12 while in
truth they're in the smooth room round the corner might cause a breakdown of
the method through over-estimating to be had pumps.
With RTLS, a mobile tag is connected to the asset, and glued
infrastructure (frequently inside the ceiling or at the walls) determines the
region of the tag. Various wi-fi technologies are used to achieve this, and
this is where AI is making a sizable tremendous effect. The technologies used
fall into one in all camps:
Common Issues
The problems with camp #1—the non-wall penetrating
technology—are manifold. What happens whilst someone leaves the door open? (A
commonplace policy in maximum hospitals). How do you determine the location of
a device while there are not any walls? (Equipment is often saved in open
areas).
The solution is to add an increasing number of
infrastructure devices to the already very pricey requirement to location a
tool in each room, meaning that these answers quickly come to be cost
prohibitive, and really cumbersome to set up.
Camp #2 calls for loads less infrastructure and is more
appealing from a fee perspective, but there are boundaries. Measuring the
signal strength received from a single tag at multiple constant receivers helps
a deterministic calculation of tag vicinity. By the use of usual models for the
way sign strength drops over distance, a tough range estimate may be made, and
3 variety estimates yield a 2D location estimate. Geofences in software
translate those 2D coordinates into room occupancy.
The hassle is that the manner indicators drop over the
variety is complicated and chaotic, stimulated now not most effective by way of
sign blockage (walls, gadget, people), however also by way of the interactions
of multiple signal reflections (“multipath fading”). The internet end result is
that place is determined with an accuracy of 8 to 10 meters or worse—no longer
almost sufficient to decide which room an item is in.
Machine Learning
Those with a system-gaining knowledge of background can also
have spotted an opportunity: figuring out which room an item is in is not a
tracking problem, but a classification problem. As with all epiphanies, it took
a new generation of RTLS corporations to step again from their algorithms to
look the problem in a brand new light. It’s here that AI is reworking RTLS.
What if you may leverage the low-fee technology of Camp #2
to attain the same degree of overall performance as Camp #1? What if you may
deliver all the value with out the cost? By leveraging BLE sensors and applying
gadget-getting to know that is exactly what AI brings to the celebration.
Rather than leaping through hoops to make very negative
variety estimates based totally on sign electricity, why now not leverage sign
energy as a characteristic to educate a category set of rules? Since the alerts
penetrate multiple partitions, a unmarried tag can listen indicators from
numerous fixed infrastructure gadgets presenting masses of capabilities to
result in a totally excessive self assurance inference approximately room
occupancy. The AI is skilled once all through installation, getting to know the
functions sufficient to distinguish Room 1 from Room 2, and so forth.
This is a essential shift in thinking with a totally
profound outcome. For traditional Wi-Fi and BLE structures, the chaotic signal
propagation in homes creates huge variations in signal strength, confounding
range-estimation algorithms.
The end result is very poor accuracy, but conversely, that
identical version in signal electricity from one region to some other is
exactly the function version that makes ML such a powerful tool. The signal
propagation features that weigh down conventional processes are the precise fodder
you need to feed an AI.
RTLS has entered a new era in which sophisticated device
mastering algorithms running on cloud-sized brains can take a classification
approach to object region. The result of AI and RTLS is high-appearing,
low-cost sensors which can be enhancing important processes and allowing
hospitals to provide better carrier and obtain higher consequences—all at a
decrease price.