In this article, we will introduce the meaning of human-machine data interfaces. Plant owners can use this article to learn more about the concept of human-computer data interfaces and the role of human-computer data interfaces in the plant of the future.
This article covers:
- What does human-machine data interface mean?
- Key benefits of human-machine data interface
- Which industries are using it?
- What is required for implementation?
What does human-computer data interface mean?
Internet of Things and Big Data Environment
As more and more IoT platforms are used in manufacturing, large amounts of data are being generated. The "smart" factory environment is about generating and delivering large amounts of data, machine learning, artificial intelligence, augmented and virtual reality solutions, and connected machines with integrated IoT platforms. The actual data can come from external sources such as sensors on machines, connected devices, logistics, embedded HMIs, internal SCADA systems, and customer buying patterns. Today, there are countless data sources that need to be integrated into the decision-making process.
Big Data and Business Intelligence solutions are increasingly being evaluated to gain insights from data. In most cases, this data is sent to the cloud for further analysis and processing. There is a need for a robust industrial cloud solution to store and process data from multiple generated data sources.
Having said that, however, there are times when instant decisions need to be made, in which case the data needs to be processed at the edge, rather than being transmitted to the cloud first. Edge computing involves processing data from IoT platforms closer to where the data is actually generated. In the case of factories, this involves processing data on the factory floor.
Consider a situation where a critical machine on an important assembly line overheats. If all the data has to be sent to the cloud first, this can be very time-consuming because immediate action is required. Issues involving latency and network connectivity can also have an impact. In this case, edge computing is preferable to a cloud-based process because sensors from the machines can send only the data they need to the HMI on the factory floor, so factory personnel can immediately adjust the temperature of the machines accordingly.
Human Data Interface
The Human-Machine Data Interface concept is about humans interacting directly with machine-generated data. It also describes the connection between the brain and the machine's thought patterns. In other words, data flows between the human brain and the machine.
Many plant owners are familiar with machine-to-machine involvement because machines on the plant floor require input from other machines and are part of an internal SCADA system. Most are also familiar with the concept of facilitating human interaction through the use of language.
Human-machine data interfaces are about human-machine communication. Human data interfaces require machines that are able to pick up and understand not only neural patterns and communications, but also recognize and understand other sensory indicators. This could include the use of facial recognition systems so that retailers can gauge a customer's reaction to a certain product or provide promotional information in real time about a product in which they have shown positive interest. Machines should also be able to process and understand voice commands, visual cues, biofeedback, and other sensory data in order to effectively engage in this communication.
Human data interfaces are in line with the Industry 4.0 goal of machine learning, as machines will learn and be able to process the data they receive from the human brain/direct human feedback. Thus, if this concept is applied to a factory environment, in the case of an overheated machine, the temperature could be adjusted by visual cues or direct voice commands from factory workers.
Key Benefits of Human-Machine Data Interfaces
Improved decision-making capabilities
Data-driven plant optimization has facilitated the development of predictive maintenance solutions and other big data insights such as machine learning algorithms. Once IoT platforms are in place, human data interfaces may allow the human brain to access data and insights directly from these platforms without having to transfer data to the cloud first.
Simplifying the data engagement process
Data analytics is a fairly complex field. While the development of back-end systems still requires advanced technological capabilities, human-machine interfaces have the potential to reduce the complexity of traditional front-end systems.
Real-time data analysis and processing
Human data interfaces are well suited to edge processing environments and allow for critical decision making and data analysis in real time. There is no time delay due to latency and only the data that needs to be processed is sent, therefore smaller packets of data are involved.
Is the industry using it?
Currently, there are not many industries using the Human Data Interface. The healthcare sector has been an early adopter of Human Data Interface technology and has been used to help paraplegic patients. It is expected that more and more industries will adopt the Human Data Interface model in the near future.
What does implementation require?
Attitudinal change
The first obstacle that needs to be overcome to implement a human data interface is attitude. Traditionally, data analytics and big data management have only really been delved into by data analysts and other IT/business professionals. In order to query data, one also needs to understand databases and multiple programming languages. The human data interface concept relies on the ability of the human brain to issue direct commands to machines, and the ability of machines to pick up on human cues and sensory indicators. This means that data flow can be facilitated regardless of the skill level or expertise of the end user, which requires a dramatic shift in current attitudes toward data querying and management.
Understanding data
The human brain must be able to understand the data transmitted by the machine and vice versa. Virtual reality training and other AI tools can be used to provide people with machine engagement sessions for more complex data sets. Machines must also have the necessary sensors and algorithms to process direct human feedback.