Combining IoT & Machine Learning: Enhancing the quality of life

16

October

2019

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Let’s face it, we have all been there: You woke up too late and are in a rush to be on time for the 09:00 o’clock lecture. You open the fridge to grab some yoghurt for breakfast, and.. It’s gone.. Your roommate probably ate it last night. Again.. How convenient would it be if your fridge would know this and automatically adds the product to your grocery list? Plus, what if it has also learned that you usually add bread to that list on the same day, and does so for you? Well, that is exactly how IoT and machine learning can be used in real life situations. Obviously, those are first world problems, so in this blog I would therefore like to illustrate the power of combining IoT and machine learning and which possibilities this combination can offer to enhance the quality of life in situations where it matters significantly more.

 

There is an abundance of articles and literature dedicated to the growing ageing population and the effect of longer life on the dependency of these elders on homecare (e.g. Lawton, 1991 & Covinsky et al., 2003). The need for homecare for this group is increasing significantly while at the same time there is an increasing scarcity in available nurses to check on them regularly (Breedveld, Meijboom and De Roo 2006). This has resulted in inefficient and impractical services and high costs, which is not in favor of the quality.  It would therefore be interesting to see how technology could solve these problems.

The exact same principles of IoT and machine learning that were used in the introduction can also be applied in this case. Home devices like televisions, coffee machines or sensors that can detect movement, noise and the presence of connected smart devices such as smartphones or a smartwatch can all be connected to each other. This could also be expanded with devices targeted at their physical wellbeing. Think of the aforementioned smartwatch than can track their health conditions or a pacemaker. All these connected devices can map an overall picture of the wellbeing, location and habits of a person. By combining this with the principles of machine learning, an algorithm can be created that notices when something is out of the ordinary.

If, for example, a person generally makes coffee at 08:00 in the morning, leaves home half an hour later, returns at the end of the day and switches on the television around 19:00 in the evening, but for some reason multiple of these actions have not taken place, it could mean that something has happened to them. In that case a nurse or family member could be notified to check on that person. This is, of course, a more advanced implementation of the technologies, but more straightforward implementations can also greatly improve their independency, allowing them to live in their own house for a couple more years.  Think of receiving notifications when they forgot to take their medicine, have left the stove on or forgot to lock the doors when leaving the house.

These simple examples of how IoT and machine learning can be combined to increase everyday quality of life for the elderly can all work passively and can be integrated into their homes without the need for human intervention or monitoring. Intervention will only happen in case something is out of the ordinary. On top of that, it is in most cases not even noticeable that their daily routine is monitored, which enhances their feeling of having an independent life (Park & Jayaraman, 2003).

These simple solutions made me wonder in which other combinations of technologies we could gain more advantages to improve life. Do you see any opportunities in this? Or do you think that there could also be negative effects to these technologies?

 

Sources:

Breedveld, E. J., Meijboom, B. R., & de Roo, A. A. (2006). Labour supply in the home care industry: A case study in a Dutch region. Health Policy, 76(2), 144-155.

Covinsky, K. E., Palmer, R. M., Fortinsky, R. H., Counsell, S. R., Stewart, A. L., Kresevic, D., & Landefeld, C. S. (2003). Loss of independence in activities of daily living in older adults hospitalized with medical illnesses: increased vulnerability with age. Journal of the American Geriatrics Society, 51(4), 451-458.

Lawton, M. P. (1991). A multidimensional view of quality of life in frail elders. In The concept and measurement of quality of life in the frail elderly (pp. 3-27). Academic Press.

Park, S., & Jayaraman, S. (2003). Enhancing the quality of life through wearable technology. IEEE Engineering in medicine and biology magazine, 22(3), 41-48.

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A robotic workforce: fact or fiction?

16

October

2019

4.67/5 (3)

Our current workplace is becoming increasingly digital and automated. Employees fear that robots will eventually overtake their jobs, as was the case with manufacturing and is currently happening with administrative tasks (BCG, 2015). But is this really the case? Are we heading towards a future in which all jobs will be automated and performed by a robotic worker? In this blog I want to share my opinion on the debacle about automation in the future workforce.

 

Fear of losing a job has always been present in the background, but one paper, written by Frey and Osborne (2013) about the future of innovation and employment, caused a lot of fear among the current workforce a couple of years ago. The authors claimed that half of the current jobs will be automated in the near future. For many people this will, of course, be very frightening to hear about. However, is this really the case? According to OECD (2013), who wrote an article in direct response to Frey and Osborne, only 9 percent of all jobs could be fully automated. This difference is explained by the fact that Frey and Osborne included all jobs in their percentages no matter if they would be fully automated in the future or only minor parts would be automated or performed by a robot.

This exact point is, in my opinion, of key importance in the job automation discussion. Naturally, it is unavoidable that certain jobs or parts of it will be automated in the future. A robot is after all cheaper and less prone to errors than a human worker (Romero et al., 2016). The inference should not be made, however, that human workers will not be of value anymore in the future workplace. The majority of the jobs still have to be performed manually. Think of jobs in which cognitive skills are necessary, complex decisions have to be made and where the human touch is a key factor. Jobs in healthcare or strategy-making are very clear examples of where human workers will still be needed in the future. Automation will mostly play a central role in tasks such as processing huge amounts of data, moving information from one place to another or in tasks that are very repetitive.

As a result, it is true that workers will need to learn new skills to be able to interact and collaborate with these robots (BCG, 2015). Nowadays, it is very accessible for employees to teach themselves skills necessary for automating simple tasks. Programs like UiPath and Blue Prism let you build programs that can do the repetitive tasks for you, without knowing anything of programming yourself.  This way employees do not only learn skills that are future proof, but most importantly, can also be part of the evolution of their job in a proactive way. This will, in addition, take away the fear and misconception from employees with which we started the beginning of this blog. Robots and automation will not take over complete jobs, they will only support you with handling certain tasks.

Taking all of the above into account, my opinion is that the future workforce will stay mostly human. It will, however, be optimized and supported by robots and it would be wise for employees to understand the basics of automation to adapt to the changing workplace. How do you see this? Do you think computers and robots will become smart enough to outcompete all human workers?

 

p.s. In case you are interested in automation and would like to experiment with it yourself, have a look at UiPath, which offers easy to understand automation lessons.

 

References:

BCG. (2015). Man and machine in industry 4.0. How Will Technology Transform the Industrial Workforce Through 2025? Retrieved from https://www.bcg.com/publications/2015/technology-business-transformation-engineered-products-infrastructure-man-machine-industry-4.aspx on 15-10-2019.

Frey, C., B., & Osborne, M. (2015). Technology at work. The future of employment and innovation.

OECD (2016). The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis. OECD Social, Employment and Migration Working Paper. Volume 189.

Romero, D., Bernus, P., Noran, O., Stahre, J., & Fast-Berglund, Å. (2016). The operator 4.0: human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems. In IFIP international conference on advances in production management systems (pp. 677-686). Springer, Cham.

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