Tag Archive for: human-centric design

Santos Lite Software Updates

In August 2021, SantosHuman, Inc. completed several updates to Santos Lite, our foundational digital human modeling software suite. The modifications include new avatars for Asian males, elderly females, and other human body types as well as trade-off analysis fixes and improvements and motion capture visualizer updates.

To learn more about the Santos Lite software updates, improvements, and bug fixes, download the complete change log dated August 31, 2021.

SantosLite Software ChangeLog-Ver0_0_2021_0831

If you have other questions about Santos Lite or Santos Pro, see the Santos FAQ page.


Q&A with the CEO: Why was Santos® created?

In this month’s Q&A, Steve Beck, CEO of SantosHuman Inc., addresses the question of why Santos was created.

Q: Why was Santos® created?

Santos® exists because Trial & Error is a terrible approach to design.

To unpack this a bit and make sure we’re all thinking about the same thing, let’s look at how Trial & Error is used in a cottage industry. Our example begins with an artisan, alone in a workshop, diligently giving physical form to some radical new wooden chair design. It’s complex enough to require several weeks to complete but as soon as the glue dries, our artisan sits down to reflect on a job well done. This is the Trial part in the scenario and while the chair’s aesthetics are impressive, it falls apart almost immediately. And that’s the Error part. Having completed the 1st Trial & Error iteration, the artisan must now work to address the flaws in the design through a series of subsequent iterations.

For products where materials are cheap, production is simple, and function is not critical, design by Trial & Error is often the only viable option. But imagine using that approach to design a high-rise apartment building where structural integrity could not be determined until full occupancy on a windy day. Or, to design an aircraft where aerodynamic properties could not be determined until after take-off. Or, to design a high-speed passenger train where nobody knows if the suspension systems will withstand the forces involved in a fast, twisty trip through a mountain range.

Trial & Error in these cases would be unthinkable. Further, it would be impossible to have ever developed today’s exceptionally high-quality products with their amazing capabilities using Trial & Error. Yet, products with amazing capabilities do exist, and they are of exceptionally high quality, so how is this accomplished?

Predictive Models for Virtual Testing

The quality and capabilities available in products today are achieved through the use of mathematical models that can virtually test properties critical to the design including structural integrity, aerodynamics, and how well mechanical systems perform during operation.

How does that work?

Let’s continue with our chair design example, but instead of expending the time and materials required to test and refine a series of complete chairs, a mathematical model is used to predict structural integrity for a virtual version of the chair, relative to a range of occupant weights. Upon completion, the virtual tests provide insight as to where the chair’s structure is sound versus where it may fail. Armed with this insight, our artisan modifies the virtual chair design to address the weak areas, runs additional virtual tests to confirm the solution, and then moves quickly into production without ever having to create a single physical prototype.

While obviously a superior approach to guessing and hoping for the best (a.k.a. Trial & Error), the above scenario is not likely to fit within a cottage industry business model. The cost of the technology required to use predictive models as described would be far beyond the means of a typical artisan. But for products where materials are expensive, production is complicated, and function is critical, Trial & Error is avoided wherever predictive models exist. Why? Because predictive models provide critical insight early in a product’s design cycle. And why is that important?


The ability to gain insight early in a design cycle is important because the earlier the need for change is detected, the less costly those changes are to make. And it’s not just a little bit cheaper. This is a well-studied relationship where change becomes significantly more expensive the closer a product gets to production. Post-production, the cost of change can be staggering.



This is why predictive models are so valuable and have been so widely adopted. Predictive models provide critical insight at the earliest stages of design, leading to better designs that can be brought into production sooner, which significantly benefits the bottom line of nearly every company, in every industry.

What does this have to do with Santos?

As valuable as these widely adopted models are, none of them have the ability to predict whether humans can interact with products or processes effectively and safely. And, because there has never been a way to predict the Human-in-the-Loop, all industries have had to rely on Trial & Error for human-centric design. One of the many unfortunate results of this is that the United States Department of Defense (US DoD) is often required to address usability issues after systems have already been delivered; when options to address design issues are most limited and also most expensive to implement.


Santos: The Alternative to Trial & Error

With all of that in mind, let’s return to the question. Why was Santos® created?

Santos was created to eliminate Trial & Error from human-centric design. The genesis story begins in the early 2000’s when the Tank Automotive Command Center (TACOM) was considering the development of the next generation tank. History suggested it could take as long as 13 years for a new tank design to evolve from whitepaper to production. Over those 13 years, 9 physical prototypes could be required at an estimated cost of $1B (USD) each. One of the primary reasons $9B worth of physical prototypes could be required would be to ensure critical Warfighter-in-the-Loop issues are understood and addressed because failure to do so will cost lives.

TACOM understood it was relying on the same Trial & Error approach to design that an artisan chair maker uses. But instead of evaluating a wooden chair, Trial & Error would be used to develop and refine an entire tank in order to make sure warfighters are able to operate its sophisticated systems safely and effectively. Each iteration was going to cost US Taxpayers $1B and TACOM wanted a more objective and cost-effective approach. They wanted to virtually test for Warfighter-centric issues in the same way that Finite Element Analysis (FEA) is used to virtually test for structural integrity issues, or the way Computational Fluid Dynamics (CFD) is used to virtually test for aerodynamic issues, or the way Multi-Body Dynamics (MBD) is used to virtually test for mechanical system operation issues.  Those predictive models significantly reduce the need for physical prototypes. TACOM wanted to do the same thing for Warfighter-centric design.

That’s the problem we were asked to solve in 2003.

And we did it. We solved the problem. The research conducted in pursuit of this solution resulted in what is now commonly referred to as Santos®. Santos technologies include the ability to predict human physical behavior and performance so that Warfighter-in-the-Loop issues can be identified and addressed at the earliest stages of design. Now, instead of expending massive resources at the end of a design cycle to test system usability in a reactive role when options for change are most limited and most expensive, Santos® allows design teams to perform Warfighter-centric system evaluations in a proactive role, at the earliest stages of design. This can only be done with a predictive human model.

Santos® Means No More Trial & Error in Human-Centric Design

If you’re interested in seeing a quick example deployment of this technology, click on the image below to go directly to a previously posted blog that includes a video demonstration. Or, feel free to contact us directly at Sales@SantosHumanInc.com.

Thanks for tuning in and, as always, let us know what you think.  We’d love to hear from you.


– S

Video Demo: Task-Focused Trade-Off Analysis

Watch and learn how task-focused trade-off analysis can inform and support human-centric product development decisions in the very earliest stages of product design. You can save on your timeline and budget while also increasing your confidence in the quality of the human-centric design.

This example shows a cab space design process. However, the capabilities of Santos technologies can be applied to design and analysis of any product involving humans. The list of applicable industries includes everything from aerospace to medical equipment and powersports to consumer products.

Santos® Pro offers comprehensive trade-off analysis for human physical behaviors and interactions with products. Learn more about Santos products.

Video Demo: Integrating Santos into Other Applications

Could a Santos model be added to your product? This video demo shows how the Santos Basic Predictive Model for Physical Behavior, or BPMPB, Software Development Kit (SDK) integrates into existing applications. The Santos BPMPB SDK offers flexibility when integrating into other software environments.

So, if you’re wondering if Santos technology is portable, please watch this video to learn more. It could save your design team even more time during the early stages of product design.


Learn more about the Santos BPMPB SDK on our products page.

The Expense of Not Optimizing the HITL: Predictive Models for Precision Grasps

In a previous blog series (post #1 of which can be found here), I had said that traditional design processes do not include human-in-the-loop (HITL) evaluations until so late in a product’s development cycle that change is no longer a realistic option.  While that series focused on just four examples, each from a different industry, SantosHuman Inc. (SHI) works with clients from many industries and the anecdotal evidence for that statement is overwhelming.

Blog series posted.  Next topic.  Moving on.

Or at least, I thought so until a recent client engagement provided an example so perfectly suited to that series that I found myself agitated that it wasn’t included.  This post alleviates that agitation and, going forward, similar posts could serve as addendums to that series.  However, these addendum posts would be used sparingly and only for the most relevant examples because our experience at SHI indicates examples will continue to be plentiful.

Santos precision graspIn this first addendum entry, a global manufacturer in a highly competitive industry redesigned the thumb-operated switchgear on a new product scheduled to be in production soon. They told us their usual approach to evaluating something like this requires a working physical prototype and can take between two and three months to complete. Early on they were notified that a prototype would not be available prior to production so their plan had been to perform the study as soon as the vehicle was in production.  The problem was that the company expected to be producing thousands of these vehicles every day when launched.

Let’s review that last paragraph just in case one of your eyebrows isn’t now several centimeters higher than the other. If the evaluations of the newly designed switchgear were to proceed as originally planned, tens of thousands of their products would be in dealerships and in the hands of new owners by the time the evaluations were complete. As you can imagine, the thought of having to address usability issues for tens of thousands of vehicles, coupled with the loss of market share that accompanies poor operator feedback in a highly competitive market, was causing considerable concern.

Santos thumb switchThey contacted us hoping there might be some way to use just the CAD geometry to perform the switchgear usability evaluations prior to launch.

Once again, we see that traditional and pervasive approaches to product design not only value capability over usability, existing processes make it impossible to even consider usability until it’s too late to do anything about it.

The good news?  As a result of development efforts SHI undertook in 2014 to respond to the needs of one of the world’s largest consumer goods manufacturers (which you can read more about here*), Santos® capabilities include the ability to predict precision and power grasps.  As with all Santos® predictive models, this is a 1st principles approach to predicting grasps that not only affects – and is affected by – the entire body, it can simultaneously take into consideration other competing operator task requirements.

Our clients aren’t interested in replicating what they can already do with the virtual mannequins that have been around for decades.  SantosHuman Inc.’s clients are looking for solutions to problems that would be impossible to solve without us.

Take a look at this unique capability demonstrated in an example use case and then let us know what you think.  We’d love to hear from you.

– S

Video Demo: What’s the Difference Between a Predictive Human Model and a Traditional Digital Human?

As you might expect, I spend a lot of my time talking to people about Santos® technologies.  When they are familiar with digital human modeling at all, they’ll often say things like, “Digital human models have been around for decades.  Our teams have tried them but feel they’re difficult to use and ultimately not that much of a value-add.  Why should we be interested in yours?

For those of us who have been involved from the very beginning in what is now commonly referred to as Santos®, the answer is obvious.  But, simply saying, “Santos provides the ability to predict human physical behavior and performance“, isn’t meaningful before also providing a great deal of additional background information.  This blog post attempts to make one of the many significant values of this unique capability a bit more obvious.

The video linked to below provides a side-by-side comparison highlighting the difference between using a truly predictive human model (on the left) versus the way in which a more traditional digital mannequin is used (on the right).  While Santos® predictive models provide significant advantages for human-centric design and evaluation in any industry, this video focuses on a contrived cab space development application.

How to Watch the Video
Both the left and right sides of the video were created using a single digital human character within our flagship product, Santos Pro.  The right side of the video mimics the traditional way in which digital human models were designed to be used.  The left side demonstrates the use of Santos predictive models.

The right side of the video only needs to be watched once through the first iteration.  There’s a lot happening on the right side of the video at first so it’s not only initially more interesting, it’s almost impossible not to watch.  In comparison, the activities on the left side are rather boring at first as the user is just setting up the constraints required to define an operator task.  So go ahead and focus on the right side through the first iteration.  The activities shown on the left will complete at about the same point in the video as the 1st iteration of the activities on the right so you’re not going to miss anything.  Note, however, that the two sides only complete at about the same time because the right side has been sped up by about 5x and that’s an important point.  It takes less than a minute to set up the predictive model task on the left but takes about 5 minutes for a highly experienced, expert user to manually rotate individual joints into position on the right.

After the first iteration of activities on the right is complete, that clip just repeats over and over until the end.  But you’ll find you don’t have to watch the right side very long to see that manually rotating digital mannequin joints is non-intuitive, time-consuming, and tedious.   In addition, it is clearly a highly subjective process where compromised, even non-human-looking, results are a frequent option.  And as if that wouldn’t be frustrating enough, consider there are no economies of scale to using a digital mannequin.  Every design option explored requires another round of subjective and tedious manual joint rotations.  And then, after all that effort, when you’re all done, what is it you actually know?  It’s no surprise that many design teams consider the use of digital human mannequins an obstacle as opposed to a solution that can be used to bring better, customer-focused designs into production sooner.

In contrast, use of a truly predictive human model (the left side of the video) allows multiple and even competing task objectives to be evaluated in a system-of-systems approach that, in this example, includes seat location, steering wheel and pedal use, and even a vision requirement. The advanced predictive nature of Santos enables your teams to identify Human-in-the-Loop requirements at the earliest stages of product development while change is still a cost-effective option.

SantosHuman Inc.  When getting it wrong is not in the budget.

Take a look and let us know what you think.  We’d love to hear from you.

Santos® Pro provides a foundational platform for truly human-centric design through a full range of predictive human modeling capabilities. Learn more about our complete product line.

Q&A with the CEO: What prompted the development of SantosHuman’s predictive human modeling software?

In this Q&A, Steve Beck, President & CEO, discusses the genesis of what is now commonly referred to as Santos®. Watch for additional posts like this one in the future.

Q: What prompted the development of Santos®?

SantosHuman digital human modelA: The research that led to the human simulation capabilities now commonly referred to as Santos® was originally funded by the United States Army Tank-Automotive and Armaments Command (TACOM) in 2003. These initial funds were provided to advance research in digital human modeling that could help reduce the time and cost to bring new systems to market.

TACOM’s assessment of the digital human modeling tools available at that time was that the capabilities were not sufficient for their future requirements. TACOM’s goal was to “kick start” the development of virtual warfighter-in-the-loop system testing technologies that could decrease the need for physical prototypes and thereby decrease the time and cost to bring new systems into production.

TACOM program managers provided the following motivation for the initiative:

  • The next generation tank was expected to take 13 years from white paper to production.
  • In that time, it was expected that nine working physical prototypes would be created.
  • Each prototype was estimated to cost over $1 billion.
  • Ninety percent of the cost (U.S. taxpayer funds) of each prototype would be committed the minute the DoD said, “cut metal”—in other words, as soon as they committed to having the prototype built.

SantosHuman modeling softwareTACOM program managers also relayed that one of the primary reasons for creating these prototypes was to ensure that warfighter-in-the-loop issues were identified and addressed prior to production because failure to do so will cost lives.  The ability to assess these types of issues virtually and reduce the number of physical prototypes required represented a significant opportunity to reduce expenditures.

Over $50 million (and counting) in external funding has been invested in Santos®-related research and development by the U.S. Department of Defense and private industry since 2003. SantosHuman Inc. is productizing this research to create client-driven Santos® capabilities which now encompass a broad spectrum of Virtual Human-in-the-Loop Solutions for both defense and private industry.

– S

Video Demo: Vision Trade-Off Analysis

The true value of Santos® predictive human models is the ability to provide the trade-off analysis your design teams can actually use at the earliest stages of product development. In this brief video demo, we show you how Santos® technologies can take the guesswork out of human-centric design through a contrived example focusing on seated operator sight lines. Task-focused prediction of human physical behavior and performance makes this possible in minutes instead of hours, weeks, or even months of trial and error and physical prototypes.

Let us know what you think.  We’d love to hear from you.

Learn more about the SantosHuman difference.

Blog Series: Failing to Optimize the Human-in-the-Loop at the Earliest Stages of Design is More Expensive than You Think – 3 of 3

For those who have just arrived, welcome. Post #1 can be found here and post #2 can be found here. For everyone else, thank you for following this series and for coming back for the third and final post.

The opening statement in this series was that most design processes, whether intentional or not, effectively prioritize product capability over usability. The considerable cost of failing to prioritize usability was then shown through client engagement examples from four different industries.

All examples presented in the first two posts would have benefitted significantly if design teams had the ability to evaluate the human-in-the-loop in ways that could inform and support product development decisions. That has been the promise of digital human modeling for decades. Yet, examples just like those presented continue and they are not only common but pervasive throughout most industries.

This final post focuses on why and, of course, provides a solution.

The Problem
Most commercially available digital human models are really just virtual mannequins. Like the mannequins found in department stores, virtual mannequin joints must be individually rotated into place until some recognizable human activity is achieved. Manipulating mannequin joints within a computer environment is tedious, non-intuitive, time-consuming, and subjective. It can also be quite frustrating, not only because non-human-looking results are a frequent option, but also because every design change requires the entire process to be repeated.

Companies that provide virtual mannequins have worked hard to mitigate this frustration by including the ability to leverage pre-recorded snapshots of human activity, primarily in the form of motion capture data. Use of motion capture data to drive virtual mannequin postures does circumvent the need to interactively manipulate their joints but that data is also expensive to acquire and time-consuming to process. In fact, recent estimates from one of our automotive clients indicated an internal motion capture budget of over $30,000 per subject, per motion capture study.

But the real problem with using pre-recorded data of any kind in design is that it’s inflexible. It cannot respond to change. It can only be used as acquired. Any design change that potentially affects human interactivity requires the acquisition of more data. This is great news for companies in motion capture-related businesses, but it’s a nightmare for design teams and their budgets and deadlines. Unfortunately, this contributes to an even bigger problem.

Because virtual mannequin joints must either be manually manipulated or driven by pre-recorded data, they can really only react to an existing design.  This means significant resources must first be expended to bring a design to a relatively high level of maturity before a virtual mannequin can be deployed. In other words, the use of a virtual mannequin requires a rather long list of traditional engineering efforts to be completed first. Consequently, at the point when human-centric evaluations can finally occur, any indicated need for change will be in direct conflict with all the resources already expended.

This is almost the same situation design teams were in before virtual mannequins existed; when product evaluations could only be accomplished through trial and error, physical prototypes, and focus groups. While the need for physical prototypes may be reduced, human-centric evaluations still occur too late to be effective.  What is most ironic is that the usability of your products by your customers—those who ultimately determine your product’s success in the market—is effectively being treated as if it is among the least important of your product’s design criteria.

Why are outcomes like those presented in this series so common?  Because traditional design processes do not allow human-in-the-loop evaluations to occur until late in a product’s development cycle when change is no longer a realistic option.

The Solution
To be clear, Santos® technologies offer significant advantages in these traditional workflows which appear to be pervasive throughout most industries. Santos® predictive models are fast, flexible, objective and of course, predictive. Because they’re predictive, they provide a fair amount of autonomy which makes them easier to use and easier to use correctly.

However, the real value of Santos® virtual human-in-the-loop solutions lies in the unique ability to predict human physical behavior and performance while taking into consideration the human-centric challenges we must all deal with every day in the physical world. These challenges include:

  • Simultaneously achieving multiple and competing task goals
  • Mitigating limitations in strength, flexibility, and fatigue
  • Optimizing grasp strategies
  • Ensuring we can see what we’re doing
  • Remaining in balance and avoiding collisions in spite of external forces that may be acting upon us
  • Trying not to get hurt

A truly predictive model makes trade-off analysis (the evaluation of what-if scenarios) possible. Trade-off analysis is why predictive models are created and why they are so valuable. A truly predictive human model can provide the task-focused trade-off analyses your teams need to optimize the human-in-the-loop at the earliest stages of design—where change is not only most effective but still an option.

Watch this video for one example of how this is done.

Like many of the companies we work with, your company has probably been in business for a very long time. Your teams probably have 100’s if not 1000’s of employee-years’ worth of experience using your existing design processes. And your revenues are likely in the millions if not billions of dollars per year.  By all objective measures, your company is exceptionally good at what it does.

However, consider that your design teams Avoid the Cost and Uncertainty of Trial & Error in meeting:

  • Structural Performance Requirements through the use of Finite Element Analysis
  • Aerodynamic and Thermal Performance Requirements through the use of Computational Fluid Mechanics
  • Mechanical System Performance Requirements through the use of Multi-Body Dynamics

So, why continue to incur the cost and uncertainty in meeting human-in-the-loop requirements through trial and error? Those humans-in-the-loop are your customers. Their positive feedback is that next level of competitive advantage.

SantosHuman Inc.  When you need to get it right the first time.

Thank you for staying with the series, keep an eye on this space for new blog topics, and let us know what you think.  We’d love to hear from you.

– S

Blog Series: Failing to Optimize the Human-in-the-Loop at the Earliest Stages of Design is More Expensive than You Think – 2 of 3

Welcome to the second post in this blog series which continues with two additional examples on how traditional approaches to product design negatively affect your company’s bottom line and market share. The first post, which can be found here, provided examples from the Consumer Products and the Medical Device industries.  This post focuses on examples from the Industrial Lawn Care and the Powersports & Small Utility Vehicles industries.

Example #3: Industrial Lawn Care
A  manufacturer of industrial lawn care equipment had received enough complaints regarding the usability of one of its products to become concerned. As with most industries, poor customer feedback has a direct impact on market share so they needed to address this quickly. SantosHuman was contacted to help identify the operator-centric issues.

Small Woman on Lawn MowerOur predictive human models quickly indicated that smaller women would have to sit much further forward on the seat to effectively operate the primary controls than had been anticipated by the design. After demonstrating our initial findings, the client revealed that many of the complaints were actually from smaller women who said they could not keep the lawnmowers running. Prior to our demonstration, the manufacturer had assumed this was due to operator error. After seeing our presentation, they remembered that the vehicle seat assembly included a kill switch designed to turn the engine off if sufficient pressure is not applied while the vehicle is moving.

In this example, the product was already in production and receiving poor customer feedback. Focus groups could have evaluated fully functional physical prototypes prior to production to gain similar insight, but how would that have helped?

When evaluating human-in-the-loop issues for a mature design, there are basically two options available when a need for change is indicated that far downstream in the design process.  You can go back and ask for the budget to redesign the product based on the insight gained.  Or, you can go forward with the intention of leveraging that insight in some future iteration.  Regardless of which of those options are chosen, we begin to see why traditional design processes make it almost impossible for product usability insights to be incorporated effectively. But, we’ll dive deeper into that with the third and final post in this series.

Powersport VehicleExample #4: Powersports & Small Utility Vehicles
In this last example, a startup company outsourced two years’ worth of aesthetic and mechanical design towards the development of a new electric utility scooter.  After all design efforts were complete, the company also outsourced the creation of a production-ready prototype to present to their investors. We were told that while the prototype scooter “looked fantastic”, they soon realized it was an “ergonomic mess” and SantosHuman was asked to help identify the issues.  Over the course of a single weekend, our predictive human models provided a variety of operator-in-the-loop trade-off analyses that could be used to inform and support redesign efforts.

Again, while the ability to quickly and objectively evaluate mature product designs for human-centric issues has significant value, that is not the moral of the story. Rather, the examples presented in this and the previous post in this series are symptomatic of much larger problems which will be discussed in the final post on March 4th, 2019.

In the meantime, think back on the examples presented and imagine that these are your teams.  Imagine the cost of developing an electric utility scooter over two years. Imagine the cost of creating a production-ready physical prototype of a vehicle like that. And just in case you’re not aware of how expensive physical prototypes can be to create, consider that there is a rumor that the first working iPhone cost $1.5M.  With that in mind, imagine discovering that while your go-to-market product met the aesthetic and capability goals, it wasn’t fit for human use. Now imagine having to choose between entering a market where your first sale may be your last – or – asking your investors for additional funds for significant redesign efforts.  Does this seem like the career-enhancing moment that was likely envisioned when the project began two years earlier?  What would you have done differently?  Why do you think that would have helped?

Our experience with many companies, over many years, indicates the scenarios presented in these first two posts are not only common, they are pervasive throughout most industries.  Budget and deadline overruns, market rejection based on poor customer feedback, costly and time-consuming product redesigns, and amplified risk of injury liabilities are not the kinds of things you want to leave to chance.  So, tune in on March 4th, 2019 for the final post in this series to find out why traditional design processes are at odds with truly human-centric design and to discover a solution.

Until then, let us know what you think.  We’d love to hear from you.

– S