AI Fall Detection Keeps Grandma Safe, if She’s OK With Being Watched


In the Dutch municipality of Waalre, 10 older adults are now living under the quiet watch of artificial intelligence. Ceiling-mounted sensors from Kepler Vision Technologies scan their homes continuously, feeding an AI trained to distinguish a fall from a sit-down and automatically push a notification to family members or emergency contacts when the algorithm flags an incident. Depending on how you feel about surveillance tech, that either sounds like a great way to protect independent older people who live alone or like a dystopian nightmare. The pitch, at least on paper and given the alternative, leans toward the former.

According to Statistics Netherlands, just over a quarter of the Dutch population will be over 65 by 2040, yet the country’s care infrastructure is not growing at nearly the same rate. This isn’t a problem unique to the Netherlands. In the US, we’ll reach similar numbers by 2050. Japan’s over 60 population is already around 30% today and the World Health Organization predicts that the global population over 60 is expected to nearly double by 2050. That means there’s more pressure for older adults to manage independently at home, for longer, with less institutional support every year. Falling — more specifically, lying undiscovered after a fall — is one of the more dangerous consequences of this unfortunate calculus, but the faster someone is found after a fall, the better their chances of recovery are.

Screenshot of Leefsamen app with a fall alert, Dutch text

Leefsamen’s app automatically sends a notification to family members and emergency contacts when a fall is detected.

Leefsamen

This Dutch pilot, run through a collaboration between connectivity provider WeConnect, care network Leefsamen, and Brainport region partners, is designed for people already at elevated fall risk who want to stay in their own homes. The hardware and software are similar to the AI fall-detection systems Kepler has been running in nursing facilities for some time. So, this first application in private residences is a logical extension, not necessarily a conceptual leap.

And yet, the idea of an all-seeing eye inside a home seems, well, weird.

A sensor that can reliably detect the movement pattern of a fall can, by definition, detect a great deal else about how someone moves through their home — when they get up at night, how often they visit the bathroom, whether their gait is changing. Even if the system is designed to suppress that data, the infrastructure for collecting it exists. If the pilot scales, what happens when the commercial incentives of the companies involved diverge from the privacy interests of a 78-year-old who signed a consent form she may not have fully understood? What happens in the event of a data breach?

These aren’t hypothetical concerns — heck, they aren’t even limited to this pilot program, since the tech is already monitoring more than “15,000 elderly people around the clock” in care facilities, according to Kepler’s release. The partner companies have made the familiar pledges to protect privacy with Kepler specifying compliance with international information security standards which is a little reassuring, but data breaches happen.

None of this makes the technology bad; it’s just complicated. For someone who’s living alone, the choice may not be between AI monitoring and unmonitored freedom; it may be a choice between AI monitoring and a fall that goes undiscovered for two days. Framed that way, the sensor in the hallway starts to look less like surveillance and more like a smoke detector with better software.





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Python Partial Function – Table of Content

Python partial function

A partial function is one of the tools in the Functools module. ‘Functools’ is a module provided by python for higher-order functions, i.e., act on or return other functions. ‘Functools’ contains tools for functional-style programming. 

Partial functions help in calling another function with the positional arguments (args) and keyword arguments (keywords). We can fix some values in certain arguments.We can create new functions without having to replicate the body of the original function. 

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Creating a partial function

Let us create a partial function that calculates the cube roots of the given numbers.

from functools import partial

#Create a function

def exponent(a,b):        

return a ** b

#Create a partial function that calculates cube roots

result = partial(exponent,3)

print(result(4))

The first function is for defining exponent arguments. The second function ‘result’ is created for fixing the value ‘3’ in the arguments to send to the main function – exponent. The output for the above program will be 81. If you want to generate square roots of given numbers, you can just replace the value ‘3’ with ‘2’ in the result partial function.


Here is one more program which performs multiplication of different numbers. 

from functools import partial

def expression(a,b,c,d):

return a*2 + b*3 + c*4 + d

result = partial(expression,5,6,7)

print(result(10))

The output for this program will be 66.

 

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Benefits of partial functions

Here are some benefits of using partial functions in your code.

  • We can construct variants of existing functions.
  • Partial functions are used to derive values from normal functions.
  • A partial function is similar to bind functionality in c++.
  • We can call the partial functions in multiple places of our code.It helps in code reusability.
  • Reduce the number of lines in your code.

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Conclusion

Partial functions help in removing redundancy.It also improves your coding capability.It is especially used in applying a range of different inputs to a single object (or) set somethings as constant in arguments of a function.So what are you waiting for? Try out creating different kinds of partial functions and have fun.Do check out our other python tutorials and blogs on python to increase your skill level.

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