HomeStay is a unified technology platform that offers care, health and lifestyle services that allows more independence, convenience, safety and dignity to seniors and elderly and provides peace of mind to their families. Our technology team is digitising the aged care industry by introducing cool technology that is easy to use, integrated with other useful products and systems that will be helpful in everyday life.

At HomeStay, we use the following technology in our platform.  We hope the information is helpful and if you would like to find out more about our technology, please do not hesitate to contact us.

Internet of Things (IoT) For Private Home Caring

The internet of things is a network of physical devices embedded with electronics, software, sensors, actuators and network connectivity which enables these objects to collect and exchange data.

Simply put, this is the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other). This includes everything from mobile phones, coffee makers, washing machines, headphones, lamps, wearable devices and almost anything else you can think of.  This also applies to components of machines, for example a jet engine of an airplane or the drill of an oil rig.

Terms and Basic Definitions:

Below, we’ve provided a glossary defining the Internet of Things:


Internet of Things:  A network of internet-connected objects able to collect and exchange data using embedded sensors.


Remotes: Enable entities that utilise IoT devices to connect with and control them using a dashboard, such as a mobile application. They include smartphones, tablets, PCs, smart watches, connected TVs, and nontraditional remotes.


Internet of Things device: Any stand-alone internet-connected device that can be monitored and/or controlled from a remote location.


Dashboard: Displays information about the IoT ecosystem to users and enables them to control their IoT ecosystem. It is generally housed on a remote


Internet of Things ecosystem: All the components that enable businesses, governments, and consumers to connect to their IoT devices, including remotes, dashboards, networks, gateways, analytics, data storage, and security.


Analytics: Software systems that analyze the data generated by IoT devices. The analysis can be used for a variety of scenarios, such as predictive maintenance.

Big Data Analytics
Big Data is a term for data sets that are so large or complex that traditional data processing applications software is inadequate to deal with them.  Lately, the term “big data” tends to refer to the use of predictive analytics, user behaviour analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.

Analysis of data sets can find new correlations to spot business trends, prevent diseases, combat crime that has not been previously available.

Unstructured data comes from information that is not organized or easily interpreted by traditional databases or data models, and typically, it’s text-heavy. Metadata, Twitter tweets, and other social media posts are good examples of unstructured data.

Multi-structured data refers to a variety of data formats and types and can be derived from interactions between people and machines, such as web applications or social networks. A great example is web log data, which includes a combination of text and visual images along with structured data like form or transactional information. As digital disruption transforms communication and interaction channels—and as marketers enhance the customer experience across devices, web properties, face-to-face interactions and social platforms—multi-structured data will continue to evolve.
Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum.

Because of new computing technologies, machine learning today is not like machine learning of the past. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications that you may be familiar with:


The heavily hyped, self-driving Google car? The essence of machine learning.


Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.


Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.


Fraud detection? One of the more obvious, important uses in our world today.

Why the increased interest in machine learning?


Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.


All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention.


Artificial Intelligence (AI) and Elderly Home Care


Artificial intelligence is transforming the world of work. Advances such as deep learning, new sensor technologies, and subsequent data availability, mean that computers can perform a much wider range of tasks than previously thought possible.

AI is intelligence exhibited by machines, rather than humans. In computer science, the field of AI research defines itself as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds such as reasoning, learning, and problem solving.

AI is science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as

AI is accomplished by studying how the human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

The increased use of AI in aged care, along with the massive amounts of data that will become available means a new kind of health professional will emerge, one that is able to understand data generated by this new technology, create a tailor-made health plans, and carry out any necessary action. There is less emphasis on carrying out manual tasks, care workers of the future will have time to become more engaged with patients on a personal level, improving the mental wellbeing of people under their care.

We are at the dawn of the practical use of artificial intelligence as we become more familiar with this technology, we will find a myriad of ways to incorporate it into the aged care space. With so much potential for change, the sector is sure to offer some real opportunities in the future.