Karen’s blog from MEMS Executive Congress: Part 2

I last left you hanging, waiting to hear more about the heated conversations between the panelists and the audience – and I have to tell you, it really started heating up in the audience during the energy panel. Ooo baby it was jumping.

MEMS in energy can mean a lot of things – and our panelists diverse perspectives discussed a great deal, but the majority of the audience wanted to focus on the topic of MEMS in energy harvesting. Though not necessarily experts in this field, thankfully our panelists were up to the challenge. Our moderator was Bert Gyselinckx, General Manager, Holst Centre, imec; Wim C. Sinke, Program Development Manager, Solar Energy, Energy Research Centre of the Netherlands; Eric Yeatman, Professor of Microengineering, Deputy Head of Department, Imperial College London; and Harry Zervos, Senior Technology Analyst, IDTechEx. I actually should probably add Rob Andosca of MicroGen Systems as a fifth panelist as he was eager to ask and answer any question from the audience with his BOLT energy harvester in hand.

I loved the diversity of perspective on this panel –Wim for example does not have an entirely MEMS-centric background. His expertise is in solar and photovoltaic energy and he spoke of how multiple technologies will work together to make reliable and sustainable energy system, as well as the importance of portfolio management – combining different energies in an active way to make it work. We in MEMS could learn a lot from guys like Wim (I hope everyone picked up his business card; I know I did).

The panel also spoke about wireless sensor networks and Harry gave a great overview of the three technologies that are converging: 1. Microgenerators and energy storage (vibration, solar, heat, tree resin, etc.); 2. Ultra low-power electronics (currently being developed) – helping power sensors; and 3. Transmission protocols that don’t need a lot of power to send data. Eric followed up with the poignant view that until things become truly wireless, you can’t really have wireless sensor networks. And once they are wireless how will they be powered – by energy harvesting or battery? This opened the floodgates and I, with microphone in hand had to jog all over the audience to capture the comments and follow-up questions from the audience.

Let me be diplomatic and say that there is no clear consensus out there on MEMS energy harvesting. And out came the very clever quotes including some of my favorites including this one from Wim: “Don’t look at MEMS as the energy harvesters, MEMS are the enablers to help realize energy savings.” And this one from someone (maybe you’ll remember and leave a comment here)  “I’m happy to hear everyone in MEMS talking about energy, but I can assure you that not everyone in energy is talking about MEMS…yet.” And Bert’s: “MEMS will probably not be main source of energy replacing nuclear power plants soon; but MEMS will enable increased intelligence in energy applications.” As great as these sound bytes were, the show stealer came when Rob Andosca stood up and talked about how cows are being used for energy harvesting and gave us the best quote: “You power the Moo-mometer with MEMS because cows get dirty.” Tech-Eye reporter Tamlin Magee loved that one too and plans to write a story on – perhaps cow-power is the next big thing!

The last panel of the day before the closing keynote was MEMS in medical with a focus on aging moderated by Frank Bartels, Founder (Bartels Mikrotechnik), President (IVAM). Panelists were:  Heribert Baldus, Principal Scientist – Personal Health Solutions, Philips Research; Jérémie Bouchaud, Senior Principal Analyst, MEMS and Sensors, IHS iSuppli; Kimmo Saarela, CEO, TreLab Oy; and Axel Sigmund, National Contact Point MTI/DW and Ambient Assisted Living Joint Programme, VDI/VDE Innovation + Technik GmbH. This was another diverse panel with varying views on how to address the medical and healthcare issues of the world’s aging population.

 When asked how MEMS is enabling a better quality of life with regard to prevention, monitoring, management, replacement and rehab I think Kimmo summed it up best when he said that with MEMS we can put so many things into a small form factor, which entices people to use our products. MEMS sensors allow us to collect raw data from so many sources. Data analysis is the key benefit and is their “value add” to the customer. But the key thing here is that power consumption and size really matter. Heribert added that MEMS is enabling an aging population to detect issues in their daily lives and manage their lives. I like to say it gives them their dignity back – and that is no trivial thing.

Jérémie spoke of some of the mass markets already present for MEMS in aging including sleep apnea disorders and oxygen therapy. There are also mass markets for MEMS medical applications that are in the hospital (not yet in the home) including disposable blood pressure monitors as well as dialysis and drug infusion applications. This kicked off a discussion about an aging population living at home which is becoming more of a critical issue in Europe, and a main focus of what Axel is addressing at VDI/VDE Innovation + Technik.

At the close, the panelists were asked what they saw as the future of medical – Heribert said he’d like to see more sensor integration, more intelligence and far less power. Jérémie said he sees a future for gas sensors analyzing the breath (and will not require FDA approval). Axel sees non-invasive diabetes monitoring as having the biggest impact; while Kimmo echoed Heribert and sees a future of more integrated solutions where biometric sensors will give more data and aid early detection and intervention. Frank agreed with Jérémie that gas sensors will be next once the pump issue is solved and that the time for microfluidics is near.

This final panel set things up perfectly for our closing keynote, Renzo dal Molin, Advanced Research Director, Cardiac Rhythm Management business unit, SORIN GROUP. Renzo gave the presentation “Vision for Implanted Medical Devices Healthcare Solutions and Technical Challenges,” which outlined the opportunity for implantable medical devices. He described in detail how

the next generation of medical devices will come from miniaturization of devices, reduction of power consumption, and wireless capability and yes, even spoke of energy harvesting (you can guess whose ears perked at that statement). Renzo then highlighted how the BioMEMS market is expected to grow from $1.9 B in 2012 to $6.6 B in 2018 thanks to the inclusion of accelerometers in pacemakers and homecare monitors; MEMS sensors for glucose meter connected to smartphones; MEMS microphones for hearing aids as well as MEMS insulin pumps.

The audience was excited to discuss where Renzo saw the future of BioMEMS going, and where he felt the industry should focus moving forward. Renzo agreed that in the near future (once regulatory hurdles were overcome) patients will be able to monitor their implantable devices on their mobile devices. And he felt the next big thing will be biomarkers, as well as MEMS-enabled devices that could give an ECG will be revolutionary to the medical field.

And with that it was time to break and enjoy a fantastic evening at the Heineken Experience. We took some photographs throughout the day but by far my favorites are the ones we took at the brewery – you should definitely check them out. I would like to close this mega-long blog by thanking everyone who made this second-year MEMS Executive Congress Europe a great success from my fabulous MIG Team, to the MIG Governing Council, to the Congress EU Steering Committee, to the AMAZING sponsors (especially those top tier ones who are sponsoring all year long – we love you), the keynotes, the speakers, the attendees (especially the press who attended and those who have posted great stories – hooray!), our fantastic conference organizers at PMMI, and our sister conference folks at Smart Systems Integration. THANK YOU ALL.

Karen’s blog from MEMS Executive Congress: Part 1

There were many things that impressed me from hosting the second MEMS Executive Congress Europe – and it wasn’t the cold and snow (though it was chilly!). What struck me the most was how lively, engaged and intelligent the conversations were, not amongst the panelists but between the audience and the panelists. Often, Europeans can be conservative and reserved in conferences, but not this year In fact my favorite quote from one of the panelists was: “when I agreed to this join this panel I didn’t know I would be joining a religious war.”

The morning definitely didn’t start off with an aggressive tone as the elegant Ralf Schnupp, Vice President Segment Occupant Safety & Inertial Sensors, Continental served as our keynote. He focused his discussion on future trends in automotive with an overview of the megatrends affecting: safe mobility, clean power, intelligent driving, global mobility and most importantly, safety, with a goal of zero fatalities and accidents (WOW). He spoke of the challenges of complex sensor systems as well as the requirements of such systems. What stuck with me was his statement that “we don’t need more sensors, we need more robust, secure and safe MEMS/sensors.” For sensors I think he’s onto something (because it’s about the smart sensor integration and the software); although when I tried out that theory later that week at our sister-conference, Smart Systems Integration, I was completely shot down (ha!).

After Schnupp’s keynote came the consumer panel moderated very capably by Dave Thomas, Marketing Director, Etch Products, SPTS Technologies. Panelists included: Paul Buijs, General Manager, Bruco Integrated Circuits bv; Robin Heydon, Global Standards – Research and Innovation Group, CSR; and Joel Huloux, Director – Standardization and Industry Alliances, STMicroelectronics. You can probably tell from two of the four titles that the panel talked A LOT about standardization. And yes that was by design, as it’s an important topic that the MEMS industry has been working on and partnering with groups like MIPI Alliance (which Joel chairs).

Joel brought a good perspective to the panel because he’s not a MEMS guy; he’s really an OEM/end-user that having spent over a decade with handset company Erikson (I want to say 20 years but don’t quote me) and is now with ST, because of the ST/Erickson joint venture. He said that MIPI aims to create specifications for mobile interfaces and recently became interested in MEMS (and joined an important partnership with MEMS Industry Group) because mobile devices add at least two new MEMS each year. True, but the question remains, what are you going to standardize? And with that question, thus opened a little bit of the holy war amongst the panel and the audience. Clearly it’s an important hot button issue.

When asked about the future of consumer electronics, the panelists all felt that its market strength would continue. Robin felt the most important impact on the world would be the Internet of things as well antenna switching (he does work for CSR after all). He also felt that the next move would be towards peripherals such as the smart watch – while Paul envisioned a future where we’d all have a “doctor in a watch” as the next killer app, enabled by MEMS.

Next up was the automotive panel moderated ably by Marc Osajda, Director, Pressure Sensor Business Unit, Freescale Semiconductor – Germany. With panelists: Frédéric Breussin, Business Unit Manager, MEMS & Sensors, Yole Développement; Pietro Perlo, Vice President Torino E-District, Interactive Fully Electrical Vehicles; and Jan Peter Stadler, Senior Vice President of Engineering Sensors, Automotive Electronics Division, Robert Bosch GmbH. What surprised me about this panel is how quickly the panelists started talking about electric bicycles (e-bikes). I actually had to check with Ralph Schnupp, who was sitting next to me, to confirm that was indeed what Pietro had started the panelists discussing.

Marc quickly moved them back to automotive and it was actually quite comical to watch – Pietro and Jan Peter were sort of like the odd couple – both representing opposite sides of the spectrum of automotive. While Pietro focused on totally electric vehicles (including bikes!),Jan Peter averred that the automobile would evolve, but even by 2020 the majority of cars will still be run by combustible engines. Frédéric was well placed as a market analyst to give perspective on current uses of MEMS and sensors in applications such as night vision, heads up displays as well as efforts to reduce emissions, increase comfort and increase safety. What was also clear from all the panelists was that the consumer world is driving more and more of the automotive world; which is good for technology, but bad for pricing.

The best part of the panel was when Marc asked each panelist to describe what his car would look like in 2025. Frédéric said he’d finally give in and buy a hybrid, Jan-Peter said he wasn’t sure what kind of engine but he’d definitely want a car big enough to hold the wine he’d drive back from Romania and carry his e-bike to all the places he likes to use them (in the mountains). Lastly, Pietro stole the show when he said he’d be using a flying an electro-mobility flying car: “this is a possibility because we are MEMS!”

I’ll leave you hanging there, wanting to hear more of the excitement and challenging conversations at MEMS Executive Congress Europe 2013. A teaser: The next panel was MEMS in Energy, which discussed energy harvesting MEMS in depth, and as you can imagine, the opinions varied widely, to put it mildly. Soon, I’ll also describe to you the MEMS in Medical, focused on Aging panel which challenged us all to think more about quality of life issues and what more we can do with MEMS to enable a better world. So stay tuned, I’ll post my next blog soon.

Building a virtual gyro

Originally posted by Michael E Stanley of Freescale Semiconductor in The Embedded Beat on Mar 12, 2013

In Orientation Representations Part 1 and Part 2, we explore some of the mathematical ways to represent the orientation of an object. Now we’re going to apply that knowledge to build a virtual gyroscope using data from a 3-axis accelerometer and 3-axis magnetometer. Reasons you might want to do this include “cost” and “cost”. Cost #1 is financial. Gyros tend to be more expensive than the other two sensors. Eliminating them from the BOM is attractive for that reason.  Cost #2 is power. The power consumed by a typical accel/mag pair is significantly less than that consumed by a MEMS gyro. The downside of a virtual gyro is that it is sensitive to linear acceleration and uncorrected magnetic interference. If either of those is present, you probably still want a physical gyro.

So how do we go from orientation to angular rates? It’s conceptually easy if you step back and consider the problem from a high level. Angular rate can be defined as change in orientation per unit time. We already know lots of ways to model orientation. Figure out how to take the derivative of the orientation and we’re there!

In our prior postings, we’ve discussed a number of ways to represent orientation. For this discussion, we will use the basic rotation matrix. Jack B. Kuipers has a nice derivation of the derivative of direction cosine matrices in his “Quaternions and Rotation Sequences” text – one of my most used textbooks.  It makes a good starting point.  Paraphrasing his math:

Let:

1. v_f = some vector v measured in a fixed reference frame
2. v_b = same vector measured in a moving body frame
3. RM_t = rotation matrix which takes v_f into v_b
4. ω = angular rate through the rotation

Then at any time t:

5. v_b= RM_t v_f

Differentiate both sides (use the chain rule on the RHS):

6. dv_b/dt  = (dRM_t/dt) v_f + RM_t(dv_f /dt)

Our restrictions on no linear acceleration or magnetic interference imply that:

7. dv_f/dt = 0

Then:

8. dv_b/dt  = (dRM_t/dt) v_f

We know that:

9. v_f = RM_t^-1 v_b

Plugging this into (8) yields

10. dv_b/dt  = (dRM_t/dt) RM_t^-1 v_b

In a previous posting (Accelerometer placement – where and why) , we learned about the transport theorem, which describes the rate of change of a vector in a moving frame:

dv_f/dt = dv_b/dt – ω X v_b

Those who take the time to check will note that we have inverted the polarity of the ω in Equation 11 from that shown in the prior posting.  In that case ω was the angular velocity of the body frame in the fixed reference frame.  Here we want it from the opposite perspective (which would match gyro outputs).

And again,

12. dv_f/dt = 0 so
13. dv_b/dt = ω X v_b

Equating equations 10 and 13:

14. ω X v_b = (dRM_t/dt) RM_t^-1v_b
15. ω X = (dRM_t/dt) RM_t^-1

where:

16. 	0	-ωz	ωy
    ω X =	ωz	0	-ωx
    	-ωy	ωx	0

Going back to the fundamentals in our first calculus course and using a one-sided approximation to the derivative:

17. dRM_t/dt = (1/Δt)(RM_t+1 – RM_t)

where Δt = the time between orientation samples

18. ω_b X = (1/Δt)(RM_t+1 – RM_t) RM_t^-1

Recall that for rotation matrices, the transpose is the same as the inverse:

19. RM_t^T = RM_t^-1
20. ω_b X = (1/Δt)(RM_t+1 – RM_t) RM_t^T

Equation 15 is a truly elegant equation.  It shows that you can calculate angular rates based upon knowledge of only the last two orientations.  That makes perfect intuitive sense, and I’m ashamed when I think how long it took me to arrive at it the first time.

An alternate form that is even more attractive can be had by carrying out the multiplications on the RHS:

21. ω_b X = (1/Δt)(RM_t+1 RM_t^T – RM_t RM_t^T)
22. ω_b X = (1/Δt)(RM_t+1 RM_t^T – I_3×3)

For the sake of being explicit, let’s expand the terms.  A rotation matrix has dimensions 3×3.  So both left and right hand sides of Eqn. 22 have dimensions 3×3.

23. (1/Δt)(RM_t+1 RM_t^T – I_3×3)  = (1/Δt) W

24. 	0	W1,2	W1,3
    W = RMt+1 RMtT – I3X3 =	W2,1	0	W2,3
    	W3,1	W3,2	0

The zero value diagonal elements in W result from small angle approximations since the diagonal terms on RM_t+1 RM_t^T will be close to one, which will be canceled by the subtraction of the identity matrix.  Then:

25. 	0	-ωz	+ωy		0	W1,2	W1,3
    ω X =	+ωz	0	-ωx	=  (1/Δt)	W2,1	0	W2,3
    	-ωy	+ωx	0		W3,1	W3,2	0

and we have:

26. ω_x= (1/2Δt) (W_3,2 – W_2,3)
27. ω_y= (1/2Δt) (W_1,3 – W_3,1)
28. ω_z= (1/2Δt) (W_2,1 – W_1,2)

Once we have orientations, we’re in a position to compute corresponding angular rates with

• One 3×3 matrix multiply operation
• 3 scalar subtractions
• 3 scalar multiplications

at time each point.  Sweet!

Some time ago, I ran a Matlab simulation to look at outputs of a gyro versus outputs from a “virtual gyro” based upon accelerometer/magnetometer readings.  After adjusting for gyro offset and scale factors, I got pretty good correlation, as can be seen in the figure below.

You will notice that we started with an assumption that we already know how to calculate orientation given accelerometer/magnetometer readings.  There are many ways to do this.  I can think of three off the top of my head:

• Compute roll, pitch and yaw as described in Freescale AN4248.  Use those values to compute rotation matrices as described in Orientation Representations: Part 1.  This approach uses Euler angles, which I like to stay away from, but you could give it a go.
• Use the Android getRotationMatrix [4] to compute rotation matrices directly.  This method uses a sequence of cross-products to arrive at the current orientation.
• Use a solution to Wahba’s problem to compute the optimal rotation for each time point.  This is my personal favorite, but I think I’ll save further explanation for a future posting.

Whichever technique you use to compute orientations, you need to pay attention to a few details:

• Remember that non-zero linear acceleration and/or uncorrected magnetic interference violate the physical assumptions behind the theory.
• The expressions shown generally rely on a small angle assumption.  That is, the change in orientation from one time step to the next is relatively small.  You can encourage this by using a short sampling interval.  You should soon see an app note that my colleague Mark Pedley is working on that discards that assumption and deals with large angles directly.   I like the form I’ve shown here because it is more intuitive.
• Noise in the accelerometer and magnetometer outputs will result in very visible noise in the virtual gyro output.  You will want to low pass filter your outputs prior to using them.  Mark will be providing an example implementation in his app note.

This is one of my favorite fusion problems.  There’s a certain beauty in the way that nature provides different perspectives of angular motion.  I hope you enjoy it also.

References

1. Freescale Application Note Number AN4248: Implementing a Tilt-Compensated eCompass using Accelerometer and Magnetometer Sensors
2. Orientation Representations: Part 1 blog posting on the Embedded Beat
3. Orientation Representations: Part 2 blog posting on the Embedded Beat
4. getRotationMatrix() function defined at http://developer.android.com/reference/android/hardware/SensorManager.htmlWikipedia entry for “Wahba’s problem”
5. U.S. Patent Application 13/748381, SYSTEMS AND METHOD FOR GYROSCOPE CALIBRATION, Michael Stanley, Freescale Semiconductor