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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.

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.

Conclusions
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.

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.

Blog Series: Failing to Optimize the Human-in-the-Loop at the Earliest Stages of Design is More Expensive than You Think – 1 of 3

Whether intentional or not, most design processes effectively prioritize product capability over usability. Starting today, this three-part blog series provides real-world examples that show how traditional approaches to design adversely affect your company’s confidence, budget, and competitive advantage.

The series begins with real-world examples of all too common usability issues. While SantosHuman has worked with clients from many industries, the following examples were selected from four different industries:

In the final post (Post #3, Mar-4-2019), we’ll explore why Traditional Design Processes Make Truly Human-Centric Design Impossible, but also provide a solution.

To learn more, bookmark this topic and watch for new posts in the coming weeks.

Example#1: Consumer Products
Consumer Goods ExampleOne of the world’s largest consumer goods manufacturers indicated that customers who have bad experiences with product bottles may never purchase those products again. As a result, the company created a department to evaluate new bottle designs through traditional means including physical prototypes and focus group testing.

This department’s reliance on physical prototypes and focus group evaluations turned out to be significantly more expensive than expected. They contacted SantosHuman, looking for opportunities to reduce these costs and Avoid Trial & Error in Design Processes.

Example#2: Medical Devices
A medical equipment manufacturer contacted SantosHuman to evaluate a new ultrasound wand design. The new design was complete and the manufacturer wanted to verify the human-in-the-loop advantages for marketing purposes. They told us this new design was important because medical devices like ultrasound wands are not just used occasionally by physicians. Often, this type of equipment is used by technicians who sit at workstations all day, testing one patient after another. They said that health service companies who employ these technicians experience a significant number of work-related injury reports involving the lower back, shoulders, wrists, and neck. This is important not only because it affects the lives of the technicians, but also because work-related injury directly affects the bottom line of every company in every industry. Estimates indicate that companies in the United States spend between $50B and $70B per year on work-related injury. The client believed that if designed correctly, medical devices could assist in use behaviors that reduce exposure to risk of injury.

Medical Device ExampleAfter receiving the CAD geometry for the new and old ultrasound wand designs, Santos® predictive models were used to simulate a variety of medical technician-in-the-loop use activities. Within two days, our evaluations indicated that, relative to the old design, use of the new design appeared to increase exposure to risk of injury.

As a result, the company delayed production and performed their own evaluations using physical prototypes and human focus groups. Three months later, they indicated their results were similar to ours.

While the ability to quickly evaluate the usability of mature product designs has obvious advantages, that’s not the point of this blog series, so stay tuned.

On Monday, February 25th, we’ll provide two additional examples from clients within the Industrial Lawn Care and the Powersports & Small Utility Vehicle industries.  In the meantime, let us know what you think.  We’d love to hear from you.