In this article, we will cover what is meant by a Human Data Interface. Factory owners can utilize this article, to learn more about what the concept of a Human Data Interface is all about and the role of Human Data Interfaces in factories of the future.
This article covers:
- What Is Meant By The Human Data Interface?
- The Key Benefits of The Human Data Interface
- What Industries Are Using It?
- What Does It Take to Implement?
What Is Meant By The Human Data Interface?
The IoT and Big Data Context
As IoT platforms are increasingly being implemented in the manufacturing industry, a vast amount of data is being generated. The “smart” factory context is about connected machinery generating and delivering vast quantities of data, machine learning, AI, augmented and virtual reality solutions as well as integrated IoT platforms. The actual data can be sourced from sensors on machines, connected devices, logistics, embedded HMIs, the internal SCADA system as well as from external sources such as customer buying patterns. These days there are a myriad of data sources that need to be integrated into decision-making processes.
Big data and business intelligence solutions are increasingly being evaluated in order to garner insights from the data. Mostly this data is sent to the cloud for further analytics and processing. Currently there is a need for a robust industrial cloud solution that can store and process this data from the multiple sources that are generating it.
However with this being said, there are times that instantaneous decisions need to be made and in this case the data needs to be processed at the edge, without transmitting it to the cloud first. Edge computing involves the processing of data from IoT platforms, closer to where the data is actually being generated. In the case of the factory, this involves processing data on the factory floor.
Consider a situation, where a critical machine involved in an important assembly line is overheating. If all the data has to be sent to the cloud first, this can be time consuming since immediate action is needed. Issues involving latency and network connectivity can also have an impact. In this case, edge computing has an advantage over the cloud based process, since the sensor from the machine could send only the needed data to an HMI on the factory floor. Consequently, the machine’s temperature could be adjusted accordingly by factory staff immediately.
The Human Data Interface
The Human Data Interface concept is about humans engaging directly with the data generated from machinery. It also describes the engagement between the thought patterns of the brain and machinery. Data is in other words, flowing between the human brain and the machine.
Many factory owners are familiar with machine-to-machine engagement, since machinery on the factory floor needs input from other machines and are part of the internal SCADA system. Most human beings are also familiar with the concept of human-human engagement which is facilitated through the use of language.
The human data interface is about human-machine communication. The human data interface requires that machines have the ability to not only pick up and understand neural patterns and communication but also other sensory indicators. This can include using facial recognition systems so that retailers for example, can gauge a customer’s reaction to a certain product or deliver promotions in real-time about a product they show positive interest in. Machines should also be able to process and understand voice commands, visual cues, biofeedback and other sensory data in order for this communication to take place effectively.
The Human Data Interface fits in with the Industry 4.0 goal of machine learning, since machines will learn from and be able to process the data they are receiving from the human brain/direct human feedback. Therefore, if one applies this concept to the factory context, in the case of the overheating machine, the temperature could be adjusted through a visual cue from a factory staff member or a direct voice command.
The Key Benefits of The Human Data Interface
Improved Decision-making capabilities
Data driven factory optimization facilitates the development of predictive maintenance solutions and other big data insights such as machine learning algorithms. Once the IoT platform is in place, the human data interface would potentially allow the human brain to access the data and insights from these platforms directly, without the need for the data to go to the cloud first.
Simplification of the data engagement process
Data analysis is quite a complex field. While the development of the back-end systems will still need advanced technical abilities, the human data interface has the potential to reduce the complexity of the traditional front-end systems.
Data analysis and processing in real-time
The human data interface really fits well within the edge processing context and allows 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, so smaller packets of data are involved.
Are Industries Using it?
Currently there are not that many industries using human data interfaces. The health care sector has been an early adopter of human data interface technology and it is being used to assist paraplegics. It’s expected that more and more industries will embrace the human data interface model in the near future.
What Does It Take To implement?
Change in Attitude
The first hurdle, to the implementation of human data interfaces that needs to be overcome is attitude. Traditionally data analysis, and big data management has only really been delved into by data analysts and other IT/business professionals. Also understanding of databases, and multiple programming languages is required, in order to query the data. The Human Data Interface concept relies on the human brain being able to issue commands to machines directly, and machines being able to pick up on human cues and sensory indicators. This means that data flow can be facilitated no matter the skill level, or expertise of the end-user, which requires a monumental shift in the current attitudes towards data querying and management.
Understanding the Data
The human brain has to be able to understand the data that the machine is transmitting and vice versa. Virtual reality training and other AI tools can be used in order to deliver machine engagement courses to people for more complex data sets. Machines also have to have the necessary sensors and algorithms that allow for processing of direct human feedback.
Conclusion
In order to promote the adoption of the human data interface, machine development has to be steered towards incorporating advanced sensors designed for human-machine engagement. Factory owners are advised to consult with technology providers that provide existing human data interfaces that can be integrated with their current edge computing platforms on the factory floor.
