Ottobock is digital. Ottobock is human.
We invest in solutions that build bridges between humans and technology. Around the world, our digital start-up teams drive innovation.
How 3D scanners and printers are revolutionising fitting for patients
To this day, plaster casts are made in order to fit prostheses as effectively as possible. However, 3D scanners are a faster option that is more comfortable for the patient. Our iFab – short for “individual fabrication” – enables us to produce custom orthoses and prostheses quickly. O&P professionals scan a residual limb and process the data directly on a computer. Time that was once spent on manual work on the plaster cast – often a complex task – can now be channeled into the fitting process. The processed data are tested in a computer simulation and transferred directly to the milling machine and 3D printer. This minimises error sources. iFab digitalises the entire fitting and manufacturing process.
Uli Maier, O&P professional
As an O&P professional, I was initially somewhat sceptical about digitalisation. However, I was won over when my first user reported that their 3D-printed liner actually fit better.
The five steps of digital fabrication
A scanner is used to record images of the relevant body part from all sides (360°). This method is quicker and more comfortable than a plaster cast.
Digitising a craft
The digital ecosystem in iFab not only places a stronger focus on patients’ needs and interests during fitting. It also makes the related administrative processes easier for medical supply companies and orthopaedic technology businesses. Instead of making plaster models, they now transmit their data digitally via an online platform (the iFab Customer Centre). We support them as they make the transition to a plaster-free workshop and give them the digital tools they need to use our global Ottobock iFab fabrication sites as their extended workbench.
More time for people
Digitalisation eases the manual workload in orthopaedic technology. In return, there will be an even stronger focus on caring for patients. The iFab platform provides a crucial new intermediate step in patient fitting – namely, simulation. Using patients’ biometric data, a computer can now be used to check, even before it’s fabricated, whether the fitting solution will work as intended. This makes fabrication more precise, minimises potential errors and saves materials and time.
Advancing the digitalisation of orthopaedic technology
A digital treatment process that is precisely tailored to the specific needs of orthopaedic technology, that further improves personalised patient care and that optimises the 3D printing process chain with intelligent algorithms. This is the goal of iFab 4.0, an innovation project funded by the European Union and the Federal State of Lower Saxony.
Higher quality fitting
At its site in Duderstadt, Ottobock is adding more intelligent innovations to its own process chain (scanning, modifying, 3D printing), making it more seamless. For example, the project team is developing special software solutions to scan and model human anatomy. It's also powering ahead to automate additive manufacturing and network the iFab hub in southern Lower Saxony with international digital fabrication sites.
The plan is to store data from individual digital fittings and also from the entire digital production chain in a central database in the future. Here, AI and algorithms filter out successful models and methods that can then be used to self-optimise devices and processes. Our vision is to create a seamless, digital fitting chain and 3D printing production chain that grows more intelligent over time, thus achieving even higher quality fittings.
An inquisitive mind and a playful approach, coupled with a clear vision – these are the best companions for implementing innovations successfully
Nadja Singer, Head of Digital Market Acceleration
Artificial intelligence (AI) for intuitive movements
How does a hand prosthesis know when to flex a finger and type on a keyboard? In the past, people with an amputation had to spend considerable time learning to give their prosthesis complex signals via muscle contractions. Today, prostheses can learn from their users. Thanks to electrodes that capture biosignals in the residual forearm and artificial intelligence, Ottobock prostheses are able to identify how the user wants to move and automatically assign these signals to the correct hand movement.
Control via smartphone and app
Right from the start, O&P professionals use a special app when fitting and adjusting this type of prosthesis. After this, users can manage and practise controlling the prosthesis themselves on their smartphone.
And if they give their consent, devices can even be serviced via the cloud in future. The prostheses will then be able to send direct feedback to Ottobock so we can optimise the technology and avoid potential errors before they occur.
Smart sensors and microprocessors
Ottobock introduced the C-Leg – the world’s first leg prosthesis to be controlled by microprocessors – back in 1997. The experiences we gained in the process led to the introduction of the Genium in 2011. This solution simulates a natural, physiological gait almost perfectly with the help of microprocessors, microsensors and micromotors. This enables users to move with maximum safety, even on difficult surfaces.
Combined advances in computer, sensor and motor technology mean that users can now use the prostheses for running, cycling and swimming. Users can simply select the various modes; an app on their smartphone is one way of doing so. This demonstrates how digital transformation is opening up new opportunities. At the same time, it also creates new requirements – so a special coating on Ottobock’s bebionic hand prosthesis now makes it easy to interact with touchscreens on mobile phones or tablets.
More quality of life thanks to digitalisation
Lina and the AI in her arm
Lina’s prosthesis learns from her: the Myo Plus control device with pattern recognition uses eight electrodes to measure movement patterns in her residual forearm. Based on artificial intelligence, these are assigned to certain hand movements and grips. Tying shoelaces or turning a doorknob are just a couple of examples.