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I can even link the model I am discussing to the concept of clickbait – expectancy and value are top-down (or knowledge-driven) factors which help you decide where to allocate your limited attentional resources. Which links us nicely to the model we are discussing, Christopher Wickens’ Model of Human Information Processing [1].
That’s the beautiful thing about this model, you can use it describe to human behaviour in so many situations. Furthermore, you can use this model to understand the role of the human in your system and make better design decisions. In this article I discuss one how the SYSTRA Human Factors team has used the Model of Human Information Processing to assess selective attention and perception for in-cab signalling solutions. In future articles we will discuss other aspects of the Model of Human Information Processing in a diverse range of rail projects.
For anyone with an interest in human factors I would highly recommend reading ‘An Introduction to Human Factors Engineering’ by Christopher Wickens [1]. It was my go-to textbook during university and has been a permanent feature on my desk ever since. In the following sections I will briefly introduce the model, however if I have piqued your interest, I suggest you refer to the far more comprehensive explanation therein.
The human information processing system is comprised of three distinct stages: perception of information, central processing and transformation of that information, and responding to that information. On the left of the model the senses gather information which can be perceived and turned into meaningful information on which we can take action.
Sometimes perception can lead directly to an action, such as slamming on the car brakes when you perceive a hazard ahead. This form of perception leading directly to action is synonymous with the System 1 thinking described by Daniel Kahneman [2]. You think fast, automatically, and with minimal effort. Other times your response is delayed, as information is manipulated within working memory in order to select the most appropriate response. This System 2, or Thinking Slow, in Kahneman’s parlance, is conscious and effortful. An example is trying to solve a complex math problem.
At the top of the model is attentional resources pool. This illustrates the finite pool of cognitive resources we have available to support each stage of the process. This limited pool of resources has a direct consequence on the selection of sensory channels (left of the model) for further information processing. Whilst not shown on the model it is worth noting that a persons’ mood or emotions may influence the available resources or directly bias one of the stages of information processing.
We could not possibly perceive all the information our senses provide us, as such we must select the sources to pay attention to. Wickens’ identifies four factors which influence our selection of channels, these are discussed within the following project example.
SYSTRA has extensive experience in the design of European Train Control Systems (ETCS) here in Australia and around the world. ETCS is the signalling and control component of the European Rail Traffic Management System (ERTMS). It is a sophisticated train control system that improves rail safety and efficiency. In Europe it is replacing many legacy train protection systems and provides interoperability between jurisdictions. Critically for this example, ETCS most commonly utilises in-cab signalling, rather than traditional lineside signalling.
ETCS is typically implemented in stages, rather than a whole network at once. This results in areas with different levels of supervision and control. For example, the busy inner section of the network may employ ETCS Level 2, whilst the outer regions may retain a legacy system, or a lower level of ETCS. When a train transitions to a region with a lower level of supervision (e.g. ETCS Level 2 to legacy unfitted section) the driver is required to acknowledge this transition on the in cab display, the Driver Machine Interface (DMI). See an example of the DMI indication that must be acknowledged in Figure 2. Note the icon will be flashing.
The SYSTRA Human Factors team sought to better understand human information processing associated with an ETCS level transitions and other features of the ETCS solution, to inform design decisions and to assess any safety and performance risks.
For ETCS level transitions the focus was on the selective attention and perception components of the model. We were interested to know whether drivers would identify the transition alerts and take action and to explore any potentially negative consequences associated with distraction.
A driver is alerted to an ETCS level transitions by two auditory alerts and visual cues on the DMI. One alert occurs in advance of the transition, and the second occurs as the transition is executed. The driver must acknowledge a flashing icon on the DMI within 5 seconds of the second alert (though other configurations are possible). Wickens’ framework is used to assess whether these cues are sufficient to ‘grab’ a drivers’ attention:
Based on the four factors of selective attention, it was concluded that a driver is highly likely to pay attention to the level transition alerts and indications. Though as previously noted, we only have a limited pool of attentional resources. Our attention will be influenced by external factors, and any other demands being placed on us at that time. As such it is imperative that ETCS level transitions are positioned at locations with low driver workload. Placing a transition in a complex section of the network, where a drivers attentional resources are depleted, will reduce the likelihood of the level transition ‘channels’ being selected.
Selective attention does not always guarantee perception, a topic that is explored below.
Perception involves the extraction of meaning from information processed by the senses. Wickens’ defines three simultaneous and concurrent processes that influence ability to perceive. These are discussed below using the example of the ETCS level transition:
This example does not go into detail on the central processing and response stages of the model. Acknowledging a level transition is likely to be an automatic response, based on the factors of selective attention and perception described above. Long term and working memory are fascinating topics where the application of good design and human factors principles can optimise the match between man made products and the human information processing system. We will explore these factors further in future articles.
This article provides an overview of the selective attention and perception components on the Human Information Processing Model. These tools are demonstrated for a relatively simple project example where existing design features and situational factors mean that perception and response are expected. However, for more complex situations, operator perception and action is not always guaranteed. SYSTRA has utilised the same approach to assesses selective attention and perception for a broad range of ETCS features, proving focused recommendations to optimise these processes, and ultimately improve the usability and safety of ETCS solutions.
For more information, please get in contact with Jamie Barton.
In 2018, the UK Department for Transport (DfT) challenged the Great Britain rail industry to remove all diesel-only trains from the network by 2040.
Over the next two decades, the railway will be undertaking large scale electrification programmes in order to achieve this target.
There are currently 13 Electrical Control Rooms (ECRs) monitoring the GB rail network and ensuring these ECRs are sufficiently staffed is essential for meeting the industry’s targets of reducing diesel-only trains from the network.
Any increase in the amount of electrification in turn creates an increased demand on the operational functions who are managing and maintaining this infrastructure. However, the steps required to adapt to this change are more complex than simply increasing the number of people on shift.
Rail organisations are required to consider the impact that changes to operational job roles, processes, and workload levels will have on staff’s ability to do their jobs safely and effectively. Appropriate action can then be taken during the design and development process to ensure that the future processes, equipment and working environment will meet the needs of the end users.
This article summarises the steps that SYSTRA’s UK based Human Factors team have taken on a project over the past 12-18 months to help ensure that one of the largest ECRs in the UK will have sufficient capacity to manage the additional electrification being introduced by the GB rail network up to 2050.
Although this information relates specifically to ECR design, the same steps can be applied to change management projects in other disciplines.
The electrified rail network is monitored by Electrical Control Operators (ECOs). Involvement of ECOs and Union Representatives was essential at every stage of the project to gain buy-in and trust from the staff and to enable collection of subjective data.
Due to COVID-19 restrictions at the time of assessment, discussions were held online to inform the initial data gathering. Fortunately, these restrictions were relaxed in time to allow SYSTRA to organise a direct observation of a 12-hour Saturday nightshift when peak workload demand is experienced to validate the workload models.
For people to access trackside equipment in electrified areas (e.g., to conduct maintenance), the ECOs are required to implement electrical isolations using specific processes to ensure that the isolations are correctly and safely implemented.
It is important that the processes and workflows are carefully mapped out and understood so that consideration can be given to how effective exiting processes will be once the demand for isolations increases. A hierarchical task analysis approach was used to collate this information to the required level of detail, and some simplified workflows were developed as a high-level summary of the steps involved.
A ‘vulnerability assessment’ was carried out using Systematic Human Error Reduction and Prediction Approach (SHERPA) to provide greater understanding of where the York ECR isolation planning, and execution process may be vulnerable to human error.
The findings of this vulnerability assessment have since been used to inform the design of a new ECR in Derby and also the reorganisation of the existing facilities in York. The vulnerability assessment has also been referenced by teams working on the design of new Supervisory Control And Data Acquisition (SCADA) software to understand how the numbers of alarms and alerts can be reduced to help manage workload.
A baseline model was created using a task-occupancy approach to create a profile that accurately reflects current peak workload associated with granting and revoking isolations. The key inputs to the baseline model were the number of isolations, task occurrences, task durations, and task complexity.
This analysis was presented as a model of demand for each hour of the 12-hour shift, and additional modelling carried out to develop models of scenarios that were not observed directly by our Human Factors team. This data was validated and accepted by the ECO representatives before being taken into the next stage of future workload prediction.
A list of future electrification projects and schemes was generated in consultation with the key project sponsors within the organisation. Predictions were made to quantify the additional single-track kilometres (STK) of electrification being introduced for each project. Discussions were also held to understand any potential increase in complexity or time occupancy demand for the infrastructure (e.g. increased number of control points).
The way that isolations are granted and revoked is also likely to change over the next few years, so any known changes to standards and processes were also considered at this stage.
The baseline workload model was remodelled to predict the impact of future electrification projects on ECO workload and inform strategic decisions within the organisation about future requirements for ECRs and ECOs.
The predictive model required key inputs including baseline number of isolations, STK and complexity of infrastructure. Capacity was built into this model to ensure that time is available for rest breaks and for managing any unforeseen issues without becoming overloaded.
The predictive model outlines staffing requirements for the ECR over the next two decades and highlights when additional ECR capacity will be required to manage the workload.
It was identified that a new ECR was required to manage a portion of the infrastructure to provide sufficient capacity for York ECR to manage new projects coming into the region.
Plans are now underway to build a new ECR in Derby to alleviate some of this additional workload demand. The predictive workload model was used to model the projected requirements for staffing arrangements for Derby ECR.
This work is supported with a register of Human Factors requirements that will ensure that the ECR’s of the future are designed and commissioned in an optimal way to meet the operational needs of the ECOs.
The contents of this article were presented by Charlotte Kaul at the 2022 Chartered Institute of Ergonomics and Human Factors conference held in Birmingham.
For further information, please contact Charlotte Kaul or Joanne Smyth.
Digital Human ModellingUsing a hypothetical project as an example, we will show you how you can quickly assess and iterate a design within a virtual environment. Before we go any further, let’s look at what DHM is and its key benefits.
Digital human modelling (DHM) is a tool for simulating human interaction with a product or system within a virtual environment. It enables designers and human factors professionals to evaluate designs using anthropometrically and biomechanically accurate avatars.
DHM largely replaces the traditional methods of building physical models and then developing a design through a trial and error process. This traditional process is incredibly time consuming and expensive. Another limitation is the sizes and shapes of the participants available to us.
In contrast when we are working with DHM in a virtual model, design changes can be made and their impact assessed within a matter of minutes. Human body dimensions can be scaled individually to accurately represent the extreme users in the target population. Nothing is left to chance as we can comprehensively demonstrate the suitability of a design for all its intended users.
One of the key benefits of DHM is this can all be done in the early design stages, which significantly reduces the cost of any design changes. SYSTRA utilises industry leading Santos modelling software to facilitate DHM assessments. One of the key benefits of Santos is the predictive modelling feature which makes Santos incredibly efficient and allows you to explore design variations quickly. We illustrate how this works in the following example.
To integrate a new driver information display within an existing rolling stock cab. The goal of the exercise is to identify a suitable mounting location, that supports the intended use, does not impact on existing controls and operations, and minimises modifications to the existing cab design.
Before we position the display in the cab we identify what it is for and how it will be used, in this case we have confirmed that:
If this were a real project then there is likely to be System and Sub-System requirements that must be considered at this stage. Though more often than not, the HF specialist is required to dig a little deeper and define best practice requirements to help guide the design. It’s this combination of human factors expertise coupled with the DHM software that is so powerful.
For the purpose of this example the following requirements have been defined to help guide the design:
First the Train Cab – It is possible to import 3D models from a wide variety of modelling software. This model is most often provided by the designer in their preferred file format, which is then converted. Working with the original design files means our assessment is completely aligned with the proposed design. However, for this example, we have a created a train cab using a combination of stock CAD files.
Then the digital avatars – For this example, we have included the industry standard, 5th%ile female, and 95th%ile male to represent our user population of Australian train drivers. Due to the normal distribution of key physical attributes across a population, if you demonstrate that the design is suitable for these extreme users, you in turn demonstrate its suitability for the wider population.
It is worth remembering that whilst someone may be 5th%ile in stature, this does not mean they will be 5th%ile in other key dimensions. The stature of each avatar has been scaled using Australian data from Peoplesize 2020. The software then uses a scientifically validated algorithm to scale each body dimension. Though dimensions can be individually scaled if this is critical to the task under investigation.
In the following images our 5th%ile female avatar has been positioned in a normal driving position. The viewing cone allows us to position the tablet within 60 degrees of the central line of sight. With a rough placement defined we can then begin to iterate the design and identify the optimum solution.
The next step is to establish model constraints between the digital human and simulated environment (e.g. left finger touching tablet, and eyes focusing on the tablet). Once these constraints have been established you can adjust the model geometry and the digital human will automatically adopt the resultant posture.
In this example the software automatically predicts the avatar posture as the driver information display position is adjusted. Analysis tools, such as the joint range of motion tool, are automatically updated with each movement to allow real time assessment of design solutions.
The same series of assessment can be completed using the opposite extreme of the target user population, a 95th%ile male.
In our train cab example, the final step is to assess any impacts on driving the train and signal sighting. To achieve this, we have incorporated a 3D model with a selection of signals positioned in accordance with Australian standards. Using the Eye View feature, we can quickly assess how the tablet position will impact on external sightlines.
This brief project example illustrates how you can bring your designs alive in a virtual environment. You can quickly evaluate design solutions and provide robust assurance evidence to support your design decisions.
Learn more about why Santos was created in this Q&A with CEO Steve Beck.
Our Human Factors team can use Digital Human Modelling to support you. Please contact Jamie Barton if you would like to know more.
Human factors and accessibility – Designing beyond protected characteristicsHuman Factors (HF) is often thought of as ’all about the individual’; what they do, how they feel, why they did or didn’t do something.
However, this only matters with the added context of where individuals sit within a wider system. In application HF is much broader. It considers how people interact with their working environment, with other people, with technology and within their organisation. Our Human Factors team are experienced in working with different user groups to identify and capture their wants and needs from the system in which they sit.
This article reports on a ’Design for Accessibility’ piece of work that the UK HF team presented at a Design for Safety event at Heathrow Airport (London). It also discusses how HF can go even further in supporting accessibility and inclusion, by thinking beyond the protected ’known’ characteristics and questioning existing constraints that prevent a wider demographic of people from performing certain tasks or job roles.
When it comes to people, there is no such thing as ’normal.’ Historically, accessibility has been a term used to describe the systems, products and services that are designed to be usable by those with disabilities. In this context, disabilities have been thought of by designers as a personal health condition. With this approach, design solutions can easily be viewed as ’inclusive’ or ’exclusive’ , with inclusive design being where we want to get to.
Taking a forward-thinking approach, accessibility relates to a mismatch between an individual and an interaction. Whether that be in the workplace, using public transport, grocery shopping or changing a fuse at home. This definition allows us to explore how any one person can experience a mismatch at one time in their life and promotes that accessible design is good design for all (Figure 1).
“By designing for someone with a permanent disability, someone with a situational limitation can also benefit. For example, a device designed for a person who has one arm could be used just as effectively by a person with a temporary wrist injury or a new parent holding an infant” – Microsoft Inclusive Design
Using the Chartered Institute of Ergonomics and Human Factors (CIEHF) EDI guidance definitions, equality is concerned with fairness, diversity is concerned with representation, and inclusion is concerned with involvement. The CIEHF guidance describes how taking a HF approach can help to mitigate some of the specific EDI challenges arising from the protected characteristics defined in the UK’s Equality Act (such as Age, Disability, Gender Reassignment, Pregnancy and Maternity). Not only this, but the guidance outlines how more can be done to look beyond protected characteristics (such as socio-economic backgrounds), to recognise ’hidden disabilities’ (such as colour blindness) and to recognise capability loss (such as dexterity, mobility and reach).
As HF specialists it is important that we continue to push the agenda on thinking beyond protected characteristics, to get a truer representation of the requirements for end users.
It can be challenging to empathise with users whose attributes, and the experiences attached to them, are so different from our own; but the biggest step change is to acknowledge that they exist. We need to challenge our assumptions, inform our clients and encourage designers to explore the ’unknown unknowns’ at the earliest stage in the design lifecycle.
For the Design for Safety event at Heathrow we put together an empathy toolkit for stakeholders. The purpose of the toolkit is to enable stakeholders to quickly understand the difficulties faced by people with accessibility mismatches and how early consideration and intervention can mitigate the risk of exclusion.
The toolkit includes:
We asked attendees on the day to perform a task as simple as threading a needle. Volunteers were asked to perform this in three different conditions:
Perhaps unsurprisingly, the time required to perform this dexterous task increased with each layer of complexity. Not only was this approach engaging, but it also provoked discussion and allowed stakeholders to link these mismatches to their own working life and to the working lives of those whom they design for. Stakeholders could relate their frustrations to their own experiences (such as ill-fitting PPE that has not been designed for women) and to invisible impairments (such as a sensory loss of touch.) It also helped to demonstrate how individuals may have more than one impairment and that multiple impairment can exacerbate the challenges that they face.
So often we are constrained by what came before. In other words, designing ’how it has always been done’ and ’for a demographic of people who have always done it’. This affects everything, from the job design itself, to the written processes supporting the job, and the equipment that is used to perform the work. Designs today are all based on the evolution trajectory of what came before. We are missing a trick by not considering how we can get more people doing more things!
Designing for accessibility should not a tick box exercise to meet minimum legal requirements for protected characteristics. It should proactively design for mismatches between individuals and interactions that will benefit all end users, not just those who have a permanent impairment. Empathy is a key driver in demonstrating challenges that individuals face and how our design input can mitigate those challenges. The more we can adopt this approach and educate stakeholders in the design process, the better the end solutions will be for accessibility, equality, diversity and inclusion.
Traffic Management Systems Implementation at Home and AbroadOur human factors team have experience of managing major Traffic Management System programmes in the UK, Norway and here in Australia. From our combined experience we have identified a number of challenges that were common to all the three countries which we have incorporated into a summary of lessons learned. We presented on this topic at the recent HFESA conference in November 2021 and have prepared this summary so we can share these lessons with a wider audience.
This article provides two examples from our work (Kate in the UK, Tom in Norway) including the reason for the methods selected and their benefits, then a summary of key challenges and the lessons learned for Human Factors Integration (HFI). But first, for those that don’t know, we should start by explaining what TMS is and what are some of the operational/business impacts associated with its introduction.
A Traffic Management System or TMS is a number of interconnected systems, that through continuous monitoring of rail traffic can identify potential future conflicts or delays within the train timetables and provide users with suggestions on how to manage the network, also providing the user with any impacts that suggestions may have on train performance once executed to aid them in their decision making. This can increase the performance of the network by allowing the users to resolve issues before they arise, while allowing them to optimise train running throughout the day through replanning activities. Because TMS helps to reduce the time it takes to recover from disruptions it will help to manage the increase in train services, which is the goal of most major TMS implementations.
So now we understand what TMS is, what are the impacts of its implementation?
There are a number of impacted roles and business functions as indicated by the diagram above, three of the key impacts are:
Implementing a TMS system can result in new tasks for a particular role, it can also the result in the transfer of tasks between roles. An integrated digital solution can also mean that certain tasks are no longer required to be performed by the user as they are now performed by the system. For example, the role of the signaller moves away from regulation and controlling signals to planning and monitoring.
In legacy operations, responding to events and incidents is reactive because the issue has to be realised before it can be fixed. Whereas with TMS there is real-time proactive management of the railway where users can see and resolve issues and incidents ahead of time. This not only changes the way that people work but has far reaching implications on business processes.
TMS is a new and complex technology that has far-reaching impacts not just for operations but on many aspects and at several levels of the business. To identify the impacts relevant to each role requires a detailed knowledge of the new solution combined with a detailed knowledge of current tasks and processes. This creates some significant challenges for Human Factors, we will share a couple of examples we have worked on which highlight this.
Extract from the November 2021 HFESA presentation:
“The context of the UK project I worked on was that TMS hadn’t been implemented anywhere in the UK before, so I was working on one of the first TMS projects. As is happening in here Australia, the UK ended up having a number of separate TMS projects running in parallel. This meant there wasn’t the learning in the country, there wasn’t a full understanding of what it would mean for operations and maintenance, the scale and breath of the change was not anticipated or understood.
One of the challenges was that there was no defined operational requirements or future operating concept. Neither was there a documented ‘As Is’ process, which anyone who’s worked in Rail will have experienced, a lack of sufficiently detailed job descriptions or role definitions. I realised that I needed to find a risk-based approach for looking at the totality of the system, to understand the context and focus future work. It was also clear that the approach would need to be efficient as, given the project timescales, there simply wouldn’t be time to do a complete bottom-up analysis of everything that each of the roles currently did.
Given the scale and breadth of the change and number of different roles impacted within this distributed network it was obvious that there would be some key command and control type considerations, but where to start…
I began by mapping the system functions of each HMI to the relevant roles and aligning it as far as possible with their current role functions. Initially keeping the analysis at this highest level, then focusing in on the fact that by the very nature of the dynamic technical system (which would be used for both in advance planning of train services and real-time planning of train services) the actions of 1 role interacts with / impacts other roles. This role boundaries and role interactions analysis was key to identifying the key human factors risks and structuring our work to focus on priority areas.
This HF analysis was then used by operations to understand the system and define the future concept of operations. It was also used by engineering to develop the interfaces between systems to support the data flows. This was an example of how despite there being no inputs a Human Factors Specialists can follow a risk-based approach, really add value and embed their analysis within the activities of other disciplines within the project.”
Extract from the November 2021 HFESA presentation:
“The second example relates to HMI design from a project I worked on in Norway, this project inherited a product that was designed in the 80s. So as part of the human factors work was to lead the HMI redesign of the system to make it more modern and more up to speed with what the client was expecting. It provided us with unique opportunities, like to be involved at the very onset of the project and get involved with multiple different stakeholders across the business. It also provided us with unique challenges since at the beginning of the project there was minimal understanding of the system capabilities, the user’s needs and desires or the business needs.
We decided to do more than take a traditional standards compliance approach and incorporated methods from UX and UI. We chose Google Ventures (GV) a design sprints methodology which requires you to focus on one aspect of the system design at a time, you dedicate one week to each aspect of the design and try to get from idea to minimum viable product by the end of the week. By using this approach, it allowed us to bring together people from all areas of the business from control room operators all the way through to project managers and in some instances even from the finance department, who were doing the budgeting for the project. This enabled us to understand how this system was going to work at every level of the business and what the needs of each individual user were going to be.
We would start very early on with a mind map of how a user would get through certain functions to achieve the end goal and understanding where the pain points would be, through various iterations of design. Including sketching and special programmes for dummy versions of our HMI to perform tests with our users, this was great because it helped not only to get feedback but also to close system knowledge gaps. We brought in people from different areas of the business to try out the prototypes and work through the scenarios. It educated a lot of different people, we were actually bringing our users on a journey with us, helping them to understand not only about the HMI that we were designing but more about TMS and how the future operations of their business were going to work. This was exceptionally valuable because it enabled them to go back to their peers in the control room or in the project management office and talk to them and pass on that knowledge.”
The key challenges have been common to all three countries that we’ve worked in were:
These challenges are magnified if TMS is introduced at the same time as a new System of Safeworking (e.g. moving from lineside to new in-cab signalling). What we think is interesting is that these challenges are common to all the countries and hence why we have incorporated them into a summary of lessons learned. The five key lessons learned are:
Users provide critical insights, involving genuine end users as early as possible in the project is beneficial as is keeping consistency within our user groups. It’s very easy on complex projects to get lost between a variety of different users and not see the same people consistently this can dilute the benefit for design development and validation or delay the process. In contrast, working predominantly with a core group of users they are able to truly understand the system, providing educated insights, and championing the system whilst edifying colleagues. Working with a core group should not however preclude engagement with a wide range of end-users and stakeholders.
At the beginning of a project clients and stakeholders don’t necessarily fully understand the benefits and implications of the new technology so understandably do not have a clearly defined future concept of operations. We’ve learned that you have to bring people on a journey with you on these types of projects. In HF we’re very user and client-focused and our work can help educate people on how these systems work and the benefits they will bring.
Understanding the project and the technical scope in addition to potential business impacts whilst also recognising project barriers and constraints and the fact that different stakeholders can have competing priorities. Understanding these is necessary to identify and plan how best to navigate them, recognising that we can’t necessarily do HF the way we want to instead we have to plan our work in a way that will be most effective.
HF has great tools and the output of our analysis can inform different aspects of a project and ensure systems can be safely used by all users. However, there isn’t a golden bullet and not one approach fits all. We need to work with the inputs available and be flexible and adapt to the environment around us. Careful planning is required to ensure the HF works satisfy the HF requirements whilst providing the maximum value for the project as a whole.
HF is not necessarily recognised for the true integration role that we play. Where we have the flexibility of approach we can build on and integrate with the activities that are being undertaken by other disciplines on the project. As illustrated in both of our case study examples, good integration involves being in the right place to effect change and making sure that users are considered throughout the process.
On a recent project here in Australia we have embedded lessons that we learned from our past project experience, such as engaging with all levels of the business, involving end users from the start, and following a user centred design approach to HMI design. These activities have all reaped the rewards of positive feedback!
We are both informing the design to meet the needs of the business and supporting operational integration so that the business is designed and ready to make the most out of the technology. And within this context we are integrating with the work of other disciplines, identifying and filling the gaps, which is where the real value of Human Factors can be found.
If you would like to know more about the Human Factors services that we offer at SYSTRA, visit the main page.
5 principles to help you think like a human factors professionalIn this article I have identified 5 principles to help you think more like a human factors professional when involved in the design of new equipment. The principles are based on my experience of practicing human factors in high hazard industries.
Whilst I would always advocate the involvement of human factors professionals, particularly for complex and safety critical projects, my hope is that these simple principles can help you ask the right questions, apply some basic principles, and help you to identify when you need to call in an expert.
Throughout this article I will use a made-up project example of a customer help point to illustrate different methods and the benefits of applying each principle. Customer help points, like those typically found at train stations, are used to provide customers with service information and can be used in an emergency. Pressing the buttons will connect a customer with the appropriate person, or provide an automated response. I must stress that I haven’t worked on this project, I have merely picked it as an example.
Start by identifying all the users of your new equipment. In Human Factors we refer to this as “defining the target user population”. The users will vary from project to project, so identifying who they are at the outset will help you make informed decisions to suit their needs. Consumer facing products, such as our customer help point, need to cater for a wide range of potential users (including different ages, shapes and sizes, strengths etc). However, for products in specialist domains your target user population may be different. Perhaps there are job selection criteria that define minimum and maximums. An example of this is train drivers, who have minimum requirements for visual acuity, for obvious reasons. Here are a few tips that will help you to ensure your product or system is fit for purpose and adequately considers the capabilities and limitations of your users.
Consider the physical attributes of users: When considering the physical attributes of a user, human factors specialists most frequently employ a design philosophy, wherein the design is developed to account for the extreme users of the target population. This is most commonly defined as the 5th percentile female and the 95th percentile male (this can be for any piece of anthropometric data of interest, such as stature, reach, of weight). Due to the normal distribution of key physical and cognitive attributes across a sample population, if you demonstrate that the design is suitable for these extreme users, you in turn demonstrate its suitability for the wider population. This concept is illustrated in Figure 2, which provides an illustration of the normal distributions of female and male stature.
Consider cognitive and behavioural attributes: Personas are a powerful tool to consider the capabilities and goals of your target users. Personas are a user-centred design approach most commonly used in user experience design, though increasingly used within the rail industry, particularly in station design. A persona is a fictional character representing a major user from your target audience. The personas will describe the users’ needs, experiences, behaviours and goals. The use of personas help maintain focus on end-user needs throughout the design process. In the case of our customer help point, the personas should include a wide range of potential users, including passengers or reduced ability and mobility. A point I discuss further below.
Consider inclusive design: Wherever possible I would advocate the application of inclusive design principles, this is often called “Design for all” or “Universal Design”. Inclusive design seeks to accommodate the widest possible range of potential users by not unduly excluding users of different abilities.
I’d encourage readers to use the Inclusive Design Toolkit developed by Cambridge University (www.inclusivedesigntoolkit.com) [1]. Figure 3 from the afore mentioned website illustrates the diversity across the population, and the target population for inclusive design.
The Toolkit provides the following categories for capabilities; vision, hearing, thinking, reach and dexterity, and mobility. The demands that your product or system imposes can result in difficulty, frustration, or even exclusion of a much larger group of users than you probably realise. Why not use their free exclusion calculator to see how many potential users you may be excluding (calc.inclusivedesigntoolkit.com).
At this point you may be saying ‘inclusive design sounds great, however we do not have the budget or need on our project’. In many cases the huge increase in potential customers is a compelling argument. Though it should also be noted that application of inclusive design principles will benefit all of your potential users, not just those with reduced ability.
Make sure you identify all user tasks relevant to your new piece of equipment. A Task Analysis (TA) is one of the corner stones of human factors engineering. It is a technique we routinely employ to provide a step-by-step description of the goals, tasks, and sub-tasks associated with a product or system.
A task analysis can take many different forms, it is not always necessary to define each sub-task in detail. As a minimum, designers should define a list of tasks associated with their product. Where a certain task is critical to the success of your product this list can then be used to derive design requirements. It can also be used as a checklist to evaluate design options, or as a more detailed evaluation of your preferred solution. The TA can also be used as the basis for subsequent analyses, such as Human Error Identification (HEI) or Risk Based Training Needs Analysis (RBTNA). Error identification is discussed in more detail in principle 4.
Figure 4 provides an example of a Hierarchical Task Analysis (HTA), for our customer help point. An alternative example is provided in Table 1, which displays the same information in the format of a Tabular Task Analysis (TTA). A key advantage of the Tabular form is the ability to add additional columns to capture information about a specific sub-task. This may include any equipment used, the information needs (i.e. what a user needs to complete this step), or task relevant contextual information (explored further in principle 3).
It is critical that you consider the context in which the tasks will be performed. This is one of the key areas of focus for HF professionals, but why is it so important? The wider workspace and physical environment have a big impact on how users interact with your product, for instance:
Considering this larger system and overall context of use will help you make much better-informed design decisions and avoid costly, or even dangerous mistakes. In principle 2 we explored human interaction with a system, now we must recognise that this interaction does not take place in a vacuum.
This concept is well illustrated in the ‘onion’ model. The model includes the wider context and the various layers of influence. The users, their tasks, and the equipment (your product) form the core of the model. The inner layers of the onion represent tangible impacts on a user or system and tend to be well understood. However, the outer layers are more likely to be overlooked. As you work your way outward there are additional influences (ranging from financial, technical, legal and social) that can have a huge impact of the success or failure of your product.
I recommend trying to identify these external factors – in some instances they may represent latent failures which can have a huge impact. In the case of our customer help point, a simple example could relate to maintenance. External influences could mean that maintenance is limited in time, equipment, and competence, and therefore ease of maintenance should be a key requirement of your design.
In high hazard industries, a detailed analysis of potential human errors is an activity best led by a human factors professional. However, in your role as a project engineer, project manager, subject matter expert, or end-user, you are fundamental to this process.
All engineering projects will include some form of hazard identification. This may include Safety in Design (SiD), Preliminary Hazard Analysis (PHA), Hazard & Operability Analysis (HAZOP), Hazard Identification studies (HAZID), or many other similar techniques. These qualitative assessment techniques each seek to identify hazards, along with existing and proposed controls. In the absence of a dedicated Human Errors Identification (HEI) work package, these forums provide the best opportunity to identify and mitigate potential human errors.
In my experience, consideration of potential human errors within these safety-led techniques is dependent upon the experience of the workshop facilitator, and the guidewords / prompts they use in the session. Furthermore, where users are considered, the hazards typically relate to workplace health and safety – think trips, slips and falls. Whilst inclusion of these is important, focussing on these alone will likely omit much more serious human errors with more severe consequences. As a starting point I would suggest the inclusion of human factors related guidewords in whichever hazard identification technique you choose to use, two examples are provided below.
One option is to use an error taxonomy based on Reason’s model [3] of human error types. The error types are further categorised using Rasmussen’s Skill, Rule, and Knowledge model [4] which provides insights to the human behaviour and decision making leading to the human error.
Referring back to our customer help point example. My preferred HEI technique is the Systematic Human Error Reduction and Prediction Approach (SHERPA). Completing a SHERPA analysis at the design stage would help you identify potential errors that could be expensive to rectify or have adverse consequences on system performance. An example SHERPA analysis is provided below, building on the Task Analysis presented in principle 2.
Of course my top 5 principles had to include something about physical ergonomics. Perhaps the topic people most frequently associated with the human factors discipline. I’ve included this as the final principle as successful application is dependent upon two afore mentioned principles, namely: understanding your users, and thinking about the tasks they will perform.
Physical ergonomics assessments seek to achieve the best possible ‘fit’ between a product and its users. This can have many positive results, including:
In the case of our customer help point, accessibility standards mandate the height of the unit. Compliance with any applicable standards is obviously the starting point, however there are other physical ergonomics considerations that you should be aware of. These considerations will be informed by your task analysis, or task list from principle 2.
In NSW the Assets Standards Authority (ASA) stipulate that “Help points shall be positioned within a height range of 700mm and 1250mm so that the user is able to access the functional components of the help point unit”. This range has been defined to ensure wheelchair users can operate the help point however this is likely to be below the optimum working height for our tallest potential users, here defined as a 95th percentile male.
A 95th percentile Australian male has a stature of 1898mm and a standing elbow height of 1219mm (both including a 25mm footwear adjustment). Therefore, if placed at the lower end of the range, a taller male will be required to bend to view the help point display and to operate the controls, microphone, and speaker. This is where we make use of the considerations from the task analysis, such as task frequency, task length etc. As using a help point is a very infrequent task and the duration of use is short, these non-optimum postures would still be considered acceptable, even for a 95th percentile male.
This example illustrates how anthropometric data can be used in combination with your tasks to assess a design. If a poor fit between your product and user has the potential to cause injury, or inability to use your product, you should create design requirements to address the mismatch and optimise the solution for the widest range of users.
1. Inclusive Design Toolkit developed by Cambridge University (www.inclusivedesigntoolkit.com)
2. Wilson, J & Corlett, N, Evaluation of human work, Taylor & Francis, Second Edition, 1991
3. Reason, J. Human Error, Cambridge University Press, 1990
4. Rasmussen, J. Human errors: a taxonomy for describing human malfunction in industrial situations, J. Occupational Accidents, pp. 311 – 333, Vol. 4, 1982.
5. Human error in plant maintenance, 2018, https://www.maintenanceandengineering.com/2018/04/06/human-error-in-plant-maintenance/
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