Saturday, February 10, 2018

Technical Analysis of a 1:38:00 RAK Half Marathon

Going sub 1:40:00 in any half marathon is considered a decently competitive time achievement for age groupers.

Consider that this year at the RAK Half, only 12% of the competing runners (both male and female) posted a gross time better than 1:40:00 and the rest 88% were slower. By net time, I assume this percentage will be still lower.

In this year's RAK Half, I bested the previous year's race time by nearly 14 minutes and certainly, I've never run a half this fast since college years, so therefore it is an all-time PB at this distance.

What does it take to run sub 1:40:00? I would like to share some of my own data to shed some technical light on the subject.

Note : Please click all images to zoom in.

Race Day Ambient Conditions

The morning of race saw a starting time temperature of 15 deg C, 88% humidity with 11 kph northerly winds (most likely measured at 10m height off the ground). Barometric pressure was 1014mbar. 

Between 7 and 9am, the temperature rose maybe 2 degree C at most.

These conditions yield a calculated air density of 1.2203 kg per cubic meter and a WBGT (wet bulb globe temperature) of around 16 deg C. 

Race Course

The AIMS certified course is 21.1 km long. The course is mostly very flat with 30-40m of total ascent making it suitable for a flat out race.

Incline data as a function of distance :

Weight Trend 

The trend of weight (without shoes etc) several weeks leading into the race is shown below. The weight trend hovers over 62-63 kg. At the time of the race, adding the mass of shoes and running attire to that figure would put me at a racing weight of approximately 63 kg.

HRV Trend 3 Weeks Before Race

A monitoring of daily heart rate variability 3 weeks from race day revealed that :

1) RMSSD fluctuated with highs reaching the weekend (Friday) of weeks 3 and 2 before the race. Hourly tapering of runs (last 2 weeks) showed a decrease in daily RMSSD.

2) Log transformed RMSSD normalized to R-R intervals, an indicator of fatigue, actually increased during the time 3 weeks before the race and declined during the last taper week.

Not reading too much into this but 1) and 2) might indicate a readiness to perform & heightened parasympathetic activity during tapering period. My weekly hours and average training paces for the last 3-4 weeks to the race are included in the 3rd plot below.

Certainly interesting and deserves more study.

HR Trimp Performance Chart Trend

The following image shows TRIMP performance chart (generated in Golden Cheetah) along a 2.5 month period from Nov 20 2017 to race day on Feb 9 2018  (I ran a 10K race in early November 2017 so I chose to start tracking PMC then). 

While absolute values are not important, the trend says I was more or less in a maintenance phase in the months of Nov and Dec by running an average of 3-4 km every day. In the month of January 2018, I picked that dosage upto >4 km every day. Therefore, I accumulated some residual fatigue indicated by the stress balance line (yellow) following which a taper period relaxed the stress balance to -4 just before race day. Overall fitness (blue line) increased gradually to a peak a week before race day. 

It is interesting to keep the -4 stress balance in context with the race performance achieved at the race. Atleast what the curve shows is that I went into the race slightly fatigued but not at a level that made me dysfunctional.  

Certainly, a performance management chart can be made using the language of external power and RSS, but to me, TRIMP and HR based PMC is much more trust-able when I want to assess heart stress.

Race Data

Aggregated run data is shown below from several devices, namely the Polar V800, Runscribe+ (beta) and Stryd powermeters.

Net Time : 1:38:00 (Data from GPS and 2x accelerometers)
Pace : 4:32 min/km | 7:18 min/mile | 13.23 kph | 8.22 mph | 3.67 m/s

Ave. External Running Power : 234 Watts  (to move center of mass)
Ave. External Power to weight ratio : 3.7 W/kg  
External Power Intensity : 95-98 % of Critical External Power 
Basis of Critical Power : Exponential fit over 90 Day power duration curve
Normalized External Race Power : 233 W

GOVSS Power : 373 W (a proxy for internal + external run power)

Ave. Heart Rate : 190 bpm
Racing HR as %  : 91% (Karvonen method)
HR Zone Distribution (Polar) 

Total Steps : 18,584 
Ave. Step Length : 1.16 m | 3.8 ft
Ave. Stride Length : 2.32 m | 7.6 ft
Ave. Step Rate : 191 steps a minute
Ave.  Stride Rate : 95 strides per minute 

Estimated Vertical Oscillation of Center of Mass : 0.061 m
Ave. Ground Contact Time : 0.215 seconds
Estimated Leg Spring Stiffness : 11 kN/m

Ave. Impact Shock : 12.2 G (correlates with vertical ground impact force)
Ave. Braking Shock : 10.6 G (correlates with horizontal braking forces)

Kinematic Variables & Their Distribution Over Time 

The following series of time series screenshots show box plot distributions of kinematic variables, something I really like. On the Y-axis is dependant variables of interest (such as ground contact time) and on the X-axis is time. 

Step Rate 
Half way point
Median : 189 spm
Overall, high cadence and very even throughout. 

Ground Contact Time
Half way point
Median : 0.218 s
Overall, low GCT and very even which speaks for the evenness in step rate and footstrike type.

Flight Ratio
Half way point 
Median : 30.9%
Overall, a slightly fluctuating proportion of flight time around the 30% mark. In all my past data at these paces, I have not seen numbers substantially higher than 30%.

Stride Length 
Half way point 
Median : 8.4 ft (2.56 m)

Stride Length (Left/Right Distribution)
A bit doubtful on the data but interestingly, it's showing that a decrease in SL in one of the feet is complemented by an increase in the other foot. The dark blue curve must be for the right foot. I'd have to continue to monitor this in past and future data to understand if this is a real variation between left and right sides or just noise.

Footstrike Type
Half way point 
Median : 9 (Between midfoot and forefoot)
This data comes from accelerometers strapped to the heel but eitherway, the indication is not far from what I thought it'd be. 

Impact Force
Half way point
Median : 12.4 G
This is not an indication of actual force but certainly a proxy for negative vertical acceleration. 5-15 Gs is a normal range. 

Braking Force
Half way point 
Median : 10.9 G
This is not an indication of actual force but certainly a proxy for negative horizontal acceleration. 4-13 G's is a normal range.

Run Power (GOVSS)
Half way point 
Median : 421.6 W
GOVSS power involves a computation of internal and external power to run and uses an efficiency correction upon metabolic demand. In other words, this plot gives an indication of total metabolic demand with time.

Pace and Power Splits

Maximum variation in pace = 21 seconds/km.


Readers might recall my post on my dismal performance in the same race in 2017. In that year, I dragged myself across the finish line in 1:52:00 and went back home pissed and determined to get better next year. 

In that post, I revealed pace and power histograms and some other interesting metrics. This season, a self-coached and methodical running streak from September 2017 resulted in a strong performance and a 12th place in my age category. 

I still think the most basic of all tools - a training log and a simple stopwatch - should inform most runners how structured their plans are, if they are making improvements and how much rest they are getting in between. 

The plethora of data metrics from inertial measurement units, heart rate monitors and GPS devices are nice to have and for the added tradeoff in analysis time, you get some marginal improvements in information which may or may not suit everyone.

Yet, we should not lose sight of the forest for the trees. Summing up some greater generalities about achieving sub 1:40:00, I have the following points :

1) Specificity of training : Commit a purpose to most runs, if not all runs.

2) Reverse your gear : Work backwards from shorter distances. You must break barriers in short distance (aerobic) racing to get faster at longer distances. Remember our friend, Riegel? A 44:00 10K will lobby harder for your sub 1:40:00 half campaign than one slower. 

3) Run slow to run fast :  Increase volume of low intensity runs and optimize volume of fast runs. 

4) Be a good guest : Our friend is Mr. Improvement, we'd like him in our house. Complement training sessions with adequate rest in between; an extensive endurance run may take 8-12 hours for supercompensation timing while an intensive anaerobic training session might require 40-60 hours for the same.  Doing stupid things when these bodily changes are taking place will shut the front door on Mr. Improvement. Corny, but this is fact. 

5) The journey counts : Take a year to work towards the half marathon goal of 1:40:00. Run with friends, run often, have fun.

Saturday, January 27, 2018

Ground Contact Variables Affect External Running Power Derived From Accelerometry

In my previous post, I reviewed Stryd's running power model. While looking into Stryd's whitepaper and reading several other journal papers, I suspected that if running powermeter algorithms employed summation of energy changes at center of mass, then could variation in detected ground contact variables explain some of the striking variations in reported power between competing accelerometer platforms in the market?

Ground contact variables are things like ground contact force, ground contact time, stance time, step time, step rate, vertical oscillation and so on. Minimizing errors between detected variables and laboratory equipment may minimize variations in computed power, however we are yet to understand how these accelerometers work in outside running relative to variety of footstrikes and terrain types and how those errors vary relative to actual variables.

To understand what the effect of ground contact variables is on computed running power, I played around with some hard numbers and did a sensitivity analysis using a power model I built.

First, I got in touch with Professor Alberto Minetti to get some raw force plate data for running. Professor Minetti is widely regarded as an expert in the biomechanics of running and is an honorary research professor at Accademia Nazionale dei Lincei in Rome, Italy. He also leads the Laboratory of Physiomechanics of Locomotion at the Department of Pathophysiology and Transplantation at the University of Milan.

Prof. Minetti shared force plate data for a shod front foot striker running at 4 m/s, which is 14.4 kph or 6:42 min/mile. 

I then developed a model to estimate the rate of changes in external work done using the EESA algorithm (see previous post). The model applies the same computation algorithm for external power as described in several sources in literature. 

I. Effect of Variation in Detected Ground Contact Time On Running Power

Stryd's whitepaper showed that the error in ground contact time between force plate and their footpod IMU is 2.83%. The running speed and footstrike type within the data was not discussed. These images from my previous post are reproduced here. 

Figure 1 : Modeled vs actual vertical force-time signatures in a rear-footed runner for an unspecified running speed. Base image courtesy of Stryd. Markups by the me. Observe that the Stryd thinks the footstrike is front footed when the force-time signature from the forceplate shows a rear foot strike.

Figure 2 : Plot showing goodness-of-fit of modeled GCT to force plate measurements. The average error is stated to be 2.83%. The number of runners, running speeds, shoes worn, footstrike mechanics and slope on the treadmill are all unknown which raises the question of how the error varies as a function of each of those factors.  Image courtesy of Stryd.

Now to the data from Prof. Minetti and my model :

Time signatures of the forces in the front foot striking runner are shown in Figure 3, where Fx, Fy and Fz are antero-posterial (A-P, or horizontal), vertical and medio-lateral forces respectively. Mechanical energy changes for all steps in the data and per step are shown in Figures 4 & 5 respectively. 

Figure 3 : Force-time signatures for a 67kg front foot striking shod runner running at 4 m/s. Force plate data of Minetti, Milan (Italy). Force plate acquisition frequency = 1000 Hz.

Figure 4 : Mechanical energy changes for a 67kg front foot striking shod runner running at 4 m/s for 3 steps.

Figure 5 : Mechanical energy changes for a 67kg front foot striking shod runner running at 4 m/s for a single step.

External power (Pext), vertical power (Pv), horizontal power (Px) and lateral power (Pz) are shown in the tabulated data in Figure 6 for a -5 to +5% variation in detected ground contact times. 

Figure 6 : External running power and it's 3D components per step for a rear footed shod runner running at 4 m/s. Values were computed using the EESA algorithm (or Cavagna method) as described in literature for a range of ground contact times from -5% to +5% relative to the highlighted base value. Computer model was developed by Ron George. 

As shown in Figure 6, a variation of -5 to +5% in detected ground contact time has an impact of -5 to +5% in estimated external running power, all other factors kept the same. 

Here, you can see power extending from 365 Watts to 331 Watts because of the error in vertical force-time signatures. 

A computational difference between platforms for ground contact time can arise from 3 
factors :

1. Errors in estimated ground force-time signatures from accelerometry.
2. Inability to differentiate between rear foot strike and front foot strike and suble variations in between.
3. Variations in the minimum force threshold set in the power algorithm while computing ground contact time. In literature, the threshold value from a minimum of 10 N to as large as 50 N, which translates to an appreciable difference in estimated stance times for a given running speed. 

This is just one source of variation possible between different accelerometers in reported power. Another example of variation is from differences in estimated vertical oscillation or vertical translation of the center of mass. 

II. Effect of Variation in Detected Vertical Oscillation On Running Power

The Stryd whitepaper revealed that an error of 3.18% existed between force plate vertical oscillation and that derived from the footpod. This is shown in Figure 7. 

Figure 7 : Estmated vs actual vertical oscillation in the Stryd footpod. Stated average error with respect to force plate = 3.18%. Base image courtesy of Stryd. Markups by me.

What does an error of 3.18% in vertical oscillation mean? It simply means that there is a difference in the vertical landing force that the footpod detects relative to force plate data. Since vertical velocity and vertical oscillation are single and double integrals of detected vertical accelarations respecitvely,  any error in the acceleration signal translates into errors in the vertical oscillation.

Now to the data from Prof. Minetti and my model :

External power (Pext), vertical power (Pv), horizontal power (Px) and lateral power (Pz) are shown in the tabulated data in Figure 8 for a -5 to +5% variation in accelerometer derived vertical oscillations.

Figure 8 : External running power and it's 3D components per step for a front footed shod runner running at 4 m/s. Values were computed using the EESA algorithm (or Cavagna method) as described in literature for a range of vertical oscillations from -5% to +5% relative to the highlighted base value. Computer model was developed by Ron George. 

As shown in Figure 8, a variation of -5 to +5% in derived vertical oscillation has an impact of -0.3 to +1% in estimated external running power respectively, all other factors kept the same. 

Here, you can see power extending from 347 Watts to 351 Watts because of the error in vertical force-time signatures. 

A computational difference between platforms for vertical oscillation can arise from errors in the vertical acceleration and vertical force-time signatures computed by the device. 

III. Effect of Variation in Horizontal Speed On Running Power

The Stryd whitepaper revealed that an error of 5% existed between force plate horizontal speed and that derived from the footpod. This is shown in Figure 9. 

Figure 9 :  Stryd modeled forward speed change compared to force plate measures. Indicated accuracy = 95%. Grade of running surface unknown, but presumably level. Image courtesy of Stryd.

Now to the data from Prof. Minetti and my model :

External power (Pext), vertical power (Pv), horizontal power (Px) and lateral power (Pz) are shown in the tabulated data in Figure 10 for a -5 to +5% variation in accelerometer derived horizontal speed. 

Figue 10 : External running power and it's 3D components per step for a front footed shod runner running at 4 m/s. Values were computed using the EESA algorithm (or Cavagna method) as described in literature for a range of horizontal speeds from -5% to +5% relative to the highlighted base value. Computer model was developed by Ron George.

As shown in Figure 10, a variation of -5 to +5% in derived horizontal speed has an impact of -7 to +16% in estimated external running power respectively, all other factors kept the same. 

Here, you can see power extending from 324 Watts to 402 Watts because of the error in horizontal force-time signatures. 

This analysis is a little incomplete because if horizontal speed changes, so can the vertical force-time signature. Therefore, there are multiplicative effects on computed power from the coupling of horizontal speed and vertical force.

A computational difference between platforms for horizontal speed can arise from errors in the horizontal acceleration and horizontal force-time signatures computed by the device. 


A running powermeter that utilizes ground contact variables in it's calculation of running power can spell out a range of values for power depending on variation in those ground contact inputs. 

The impact of error in three of these ground contact variables, chiefly vertical oscillation, ground contact time and horizontal speed, were explored in this article by independantly varying force time signatures and inspecting their impact on computed power. 

Other sources of error exist. For example, if an accelerometer and the algorithm used cannot distinguish between a front footed strike and a rear footed one, the underlying force impulse characteristic is misjudged. Misjudging impact force-time signature impacts the computed potental energy change and hence potential work.  

Based on the analysis for one front foot runner at 4 m/s, one observes that some of the striking differences in reported power between accelerometer platforms like Stryd and Garmin may lie in the variations in derived ground contact variables. Out of the three explored variables, errors in vertical and horizontal force time signatures can make an appreciable impact on the theoretically computed power (See Figures 6 & 10). 

It is my hope that this writeup gives runners and coaches a qualitative and quantitative feel for the impact of accelerometer error upon the new metric of running power. Basing training prescription on faulty devices and secondary & tertiary metrics derived from them can carry a risk. Not being aware of such risk within new technology has implications for both undertraining and over-reaching.

Sunday, December 10, 2017

Technical Review of Stryd's Running Power Model

In my last post, I reviewed GOVSS Running Power, which is the model adopted by Runscribe Plus. 

As a recap : GOVSS power is a total energy model which includes both the external and the internal cost to move the limbs in relation to center of mass. It also accounts for wind and the kinetic component that play a prominent role during rapid, high speed transient running events such as a track sprint. 

In this post, I review the External Energy Summation Approach (EESA) applied to center of mass which can then be converted to an external power (Part A). I will describe the model in a bit, but one observation before that.

In a Stryd whitepaper published yesterday, the writeup suggested that ground reaction forces are being employed in the calculation of components of power.   After reading that, I hold a level of confidence that the EESA algorithm is used in some capacity, either at a very complex level or a very simplistic level with some inhouse modifications. 

I also review the same whitepaper and appraise the validation they've disclosed (Part B) and describe several aspects of what it shows AND do not show. Readers who are scientifically inclined like me will be interested in this section. 

I also point the reader to my Primer on Running Power in order to get a feel for the changes in mechanical energy during running and how a 9 / 10-DOF IMU basically works. 

PART A : Fundamentals

I. EESA Approach for Calculating External Power

In the EESA approach, the vertical (Y), anterio-posteral (X, fore-aft) and lateral ground reaction forces (Z) are used to calculate the instantaneous speed of body center of mass. 

The resulting velocities are used to calculate the change in potential energy function and kinetic energy function to lift and accelerate the body center of mass. (Note : EESA has been coined by me, for lack of a better term).

The potential energy function tracks the work done to lift the center of mass.The kinetic energy function contains the velocities to move in a forward and lateral direction. In common running situations, lateral motion maybe neglected but keeping with the intent to account for all 3D motion, the lateral kinetic energy is also included in the general equation. 

The computational equations have been summed up in this mathematical infographic in Figure 1.  FxFy  and Fz are the forces in the forward, vertical and lateral directions. Their speed counterparts are expressed as vx, vy and vy. Please click to zoom in.

Figure 1 : EESA computational approach for total external power for running. Adapted from Minetti (2002). Click to zoon in.

Referring to Figure 1, the EESA algorithm is the following :

STEP 1 : The X, Y and Z velocity functions are an integral of the ratio of the respective ground reaction forces and mass, or in other words the axis specific accelerations. After integration is performed, a constant has to be added to the right side which maybe different for level running and gradient running (Cavagna constant). 
STEP 2 : Since velocity is a vector, the resultant running velocity is calculated by the sum of the squares of the 3D velocities.
STEP 3 : Kinetic energy as a function of time is half of mass times the resultant velocity squared.
STEP 4 : Vertical displacement of center of mass is the integral of vertical velocity with respect to time. After integration is performed, a constant has to be added on the right side (Cavagna constant).
STEP 5 : The potential energy as a function of time is mass times gravitational acceleration times the vertical displacement of center of mass.
STEP 6 : The total energy of the center of mass as a function of time is a summation of the potential and kinetic energies. This will fluctuation function in time, with a minimum occuring at the middle of stance phase. For level running, the curve is symmetric. For gradient running, the curve is lopsided to the right side for uphills and to the left side for downhills. 
STEP 7 : External Power is the time derivative of the summation of changes in potential and kinetic energy functions. 

This is the generalized form of EESA.

Figure 2 : Kinetic energy, potential energy and total external energy functions vs grade. Fluctuations in total external energy as a function of grade show asymmetrical profiles for uphills and downhills and a symmetrical profile for level grade. On the downhills, the decrease in total external energy is progressively more than subsequent increases as grade steepens (net energy dissipation). On the uphills, the increase in total external energy is progressively more than it decreases as grade steepens (net energy addition).  Adapted from Snyder (2012). Markups by me.

The disadvantages of the EESA model are the following :

A) It accounts only for the external power to move center of mass. It does not take into the account another source of cost demand, that to swing the arms and the legs. Since it is not a total energy requirement to run, magnitude of calculated power will be lesser compared to a total energy approach such as GOVSS.
B) It doesn't tell us what goes on inside the body. External mechanical work reflects the overall behavior of the whole body center of mass mechanics. Unfortunately, this black box approach seldom provides us with a direct understanding of what is going on inside the body. It is difficult to draw specific relationships using just sheer "watts" to things like forces, moments and storage-recoil mechanisms at the level of joints, muscles and tendons.

II. Stryd Model

Chief features as I observe are :

Ground forces estimated from acceleration signatures : From the whitepaper, it seems that Stryd employs the equations in the EESA framework for external power in it's model. Otherwise, there'd be little meaning behind their efforts to approximate ground forces.

Ground forces are estimated from the acceleration-time signatures in the 3 axes multiplied by mass of the subject runner. What the whitepaper attempts to show is a validation of that force profile against force plate data. There is absolutely no measurement of forces, they are only estimated from the accelerations.

Leg symmetry assumption : Since the Stryd comes simply as one footpod, the mechanics in one leg is assumed to be an overall representation of the body's movement. This assumption carries through into the external power calculation.

Scaling factors for power on gradients : For outdoor gradient running, the ratio of negative and positive potential and kinetic energies are no longer in the ratio of 1:1 and mirrored (see Figure 1).

Stryd employs scaling factors for uphills to account for the greater degree of concentric work, in effect increasing the modeled external power. Conversely, they employ a negative scaling for downhills to account for the greater eccentric work. People have found varying degrees of correlation when comparing to their rates of perceived exertion.

These scaling factors came as a firmware update to Stryd users in early 2017. There has been little in the way of documentation and validaton of the approach for a wide range of runners so this area is an unknown. More about this is discussed in Part B.III below.

Indoor running distance / speed may differ : A small desirable nuance in the model might be for indoor running where the Stryd model would account for the difference in treadmill belt speeds when estimating a relative forward velocity. I do not think this is the case which explains why several people find a small discrepancy between treadmill recorded distance / pace and Stryd distance / pace. I think that the magnitude of this discrepancy maybe less than 5%. 

Form Power : Stryd adds a distinction by singling out vertical work rate done "in place", i.e the power required to displace the center of mass vertically without considering forward displacement. This is expressed as a separate metric called Form Power which other platforms do not highlight. I take the liberty to express it in the following form :

Lifting In-Place Power (due to vertical oscillation) = Step Rate x m x g x Vdisp

where :

Vdisp (m) = Vertical Oscillation [total vertical distance covered by center of mass]
Step Frequency (Hz) = 1/(Aerial Time + Ground Contact Time) = 1/60 x (Step Rate)
m (kg) = mass
g (m/s2) = gravitational acceleration

I've called it lifting in-place power to distinguish it from slope lifting power when running up a hill. 

Take note that this component of power is directly proportional to step frequency. Since step frequency is made up of the inverse of the sum of aerial time and contact time, this would suggest that increasing step frequency is done by decreasing contact time and/or decreasing aerial time. This couples with and affects Vdisp.

The consensus in literature around lifting-in-place power is conflicting. Some studies show that low vertical oscillations are correlated with better running economy (lower metabolic cost) while some studies show the exact opposite (higher metabolic cost). 

The latter point is interesting actually. It has been suggested that dropping vertical oscillation must be accompanied by an increase in step frequency (see figure above). But this increases the internal work needed to sustain high step frequencies, thereby increasing the overall metabolic cost and worsening running economy. Studies have published data backing this negative correlation. A confounding variable in these studies maybe how adapted and well trained the runners were to sustain high step frequencies. 

One point is clear. Studies that have looked at leg kinematics during actual running races find that top tier runners are almost always front foot strikers and show the lowest ground contact times compared to the rest of the field (eg Hasegawa, 2007). Again, this gives more power to ground contact time being an actionable variable. In this way, atleast in my mind, ground contact time and vertical oscillation are "coupled" variables which are again related to the running speed, as the figure above shows. 

Another point to keep in mind is that leg spring action contributes some of the force (and power) needed to oscillate upwards. Therefore, a runner with good spring mechanics provides lesser net force to lift himself than one without. This must mean not all vertical oscillation is "waste", as energy is recycled. The net positive power to oscillate is therefore lower than the suggested calculated value of lifting-in-place power. 

For example, a 64kg runner at 3 Hz step frequency and 5cm of vertical oscillation requires a lifting-in-place power = 95 W. But if 30% of that is supplied by leg spring, the rest 70% is the net positive power that the runner must produce. This equates to around 66 W. 

Leg Spring Stiffness : Another distinction that Stryd offers is displaying a mathematically calculated leg spring stiffness from some of the equations I described in a previous post. This maybe displayed for information only and I do not believe it is used to treat energy savings due to spring biomechanics. See part B.V below for more on this.

RSS : Native to Stryd is the Running Stress Score (RSS) that is a single number quantifying the effect of a daily run. One notes that this stress calculation is non-linear in nature as it credits higher volume and intensity in an exponential way. This thinking is similar to the weighted TRIMP method that assigns exponentially increasing weighting factors as exercise intensity increases. Although I don't know the exact algorithm used, a simple formula I developed approaches within 3% of Stryd RSS. This is based on Stryd's own RSS  rules for different intensity zones. 

It's as follows :

RSS/min = A x e^(B x Pext/CP)

where  parameter A = 0.0758
parameter B =  3.1297
Pext = External mechanical power

CP = Stryd Critical Power

Wind : The Stryd does not take wind into consideration. However, there are several reports of gusts negatively affecting reported power which seem to be a bug that is being investigated. See Part B.VII for more on this subject.

PART B : Stryd Whitepaper Review

I. Review of Stryd's Vertical Force-Time Curve

Vertical power is the rate of change in potential energy. Multiple studies have shown that this component constitutes the majority share in the metabolic cost of normal running.

Since the Stryd footpod has no means to measure force directly, in order to get the correct estimate of force, the IMU must capture the time course of instantaneous vertical acceleration profile which is proportional to the force signature. 

Continuing on, in order to get an accurate estimate of changes in potential work, the footpod IMU must capture time course of vertical displacement of center of mass. 

An image showing the first of these elements is in Figure 2.

Figure 3 : Modeled vs actual vertical force-time signatures in a rear-footed runner for an unspecified running speed. Base image courtesy of Stryd. Markups by the me. 

It is not known what speed the runner was running at but looking at the force signature, it is clear that it was a rear foot runner. The model estimation and force plate data nearly match, particularly when you look at the highest peak vertical force which is important in derivative calculations of impulse and leg spring stiffness.

Some key observations as far as vertical force-time (Figure 1) is concerned :

1) Misrepresentation of first footstrike peak : The Stryd models a nice rounded single peak force when there were actually two. This is significant because the footpod thinks that the footstrike is fore-footed when in reality it's rear-footed. The implications are :

a) The Stryd model neglects first impact peak. If the first rear-foot strike peak is completely neglected, that initial impact peak of landing will be distributed over an inaccurate and wider period of delta time which will suggest that there is no shock loading, when in reality there is.
b) Stryd's force profile estimation for a forward moving (or backward moving) fore-footed runner might be alright but it might be grossly over-looking rear footed mechanics by missing the first impact peak altogether. 
c) If a runner changes gait mechanics on the fly due to the effect of different running surfaces or due to fatigue in a long race, this 'tuning' may not be captured properly by the model.
d) Gradients may substantially influence these errors. To get an idea of grade influences on the vertical force-time profile from empirical studies, please see Appendix image A4. 

2) Mismatch of contact time : Although the actual and estimated signals are close, Stryd under-estimates initial footstrike and over-estimates the actual takeoff point with respect to time (Figure 2).

This has some implications, namely :

a) The ground contact time (GCT) is over-estimated. From the image, I estimate atleast 15-20 milliseconds greater than the force plate.
b) The stance-averaged vertical ground reaction force during GCT does not match 1-to-1 with the actual force plate data.
c) As a consequence of b), the Stryd estimated ground reaction impulse given by the product of force and GCT is different to the actual impulse.
d) Any calculation of aerial times, step lengths and step times using GCT will propagate the error through.
e) Since leg spring stiffness (LSS) is driven by GCT and involves a duty factor calculation treated with the maximum vertical ground reaction force, errors propogate into the LSS model as well.
f) Since the tests were carried out in the laboratory, the effect of gradients and different speeds on the error in GCT remains unknown.

3) Effect of Running Speed on Force-Time Curve Not Discussed : Stryd does not show the effect of running speed on the goodness-of-fit for vertical force curve.

That this important element is missing in the whitepaper prevents a discussion on the influence of variations among broader running speeds and broader gait mechanics. Within literature, researchers have found that simplistic vertical force-time curve models  derived from spring mass models lose their goodness-of-fit as the running speed increases due to the presence of high frequency components from the acceleration of the lower limb.  

This is where I'd exercise some caution. With the current state-of-the-art, I wonder if IMU's may still not be practical for application to short-distance, high speed track racing.

4) Impact of Shoe Type to Force-Time Curve Not Discussed : The impact of variations in footwear to the measured parameters is unknown. This is also an influencer of vertical ground reaction force profiles.

5) Impact of Treadmill Slope to Force-Time Curve Not Discussed : The whitepaper lacks a review of the force-time profile accuracy under the influence of slope (see Appendix image A4). Since step period generally decreases as slope increases and increases as slope decreases, what influence high running speeds have on the model fit when the slopes are greater than 7 degrees inclination is something important to document. 

Figure 4 : Plot showing goodness-of-fit of modeled GCT to force plate measurements. The average error is stated to be 2.83%. The number of runners, running speeds, shoes worn, footstrike mechanics and slope on the treadmill are all unknown which raises the question of how the error varies as a function of each.  Image courtesy of Stryd.

The second element in estimating potential energy changes is vertical oscillation, shown in Figure 3.

Figure 5: Estmated vs actual vertical oscillation across a wide range of runners. Sample size is not disclosed. Experimental method and running speeds to generate the plot is unknown. Stated average error with respect to force plate = 3.18%. Base image courtesy of Stryd. Markups by me.

Some key observations as far as vertical oscillation is concerned :

1) A close fit : A 3.18% average error in vertical oscillation has been shown which is quite good. It is desirable to understand the experimental method, equipment used and the sample size of the runners to put this into context.
2) Error Propagation : I understand that a small error propagates into the Form Power calculation due to vertical oscillation error.
3) The effect of gait parameters on this variation is not documented. The estimation error with respect to gait parameters such as velocity and step duration should be additionally plotted, for example, in the form of a Bland-Altman plot.
4) Explanation of influencing factors behind error : I'm very interested to know if 3% average error is the best achievable given current level of technology. It is desirable to get some sort of explanation to the influencers of this error. Do random influences play into this?

II. Review of Stryd's Horiztonal Force Model

Horizontal power is a function of the rate of changes in kinetic energy. Studies have suggested that the horizontal work, particularly that component of generating horizontal propulsive force, constitutes more than one-third of the total cost of steady speed running.

Figure 6 : Stryd modeled forward speed change compared to force plate measures. Indicated accuracy = 95%. Grade of running surface unknown, but presumably level. Image courtesy of Stryd. 

A review shows the following :

1) Accuracy is decent : Like the vertical force-time profile, the horizontal force-time profile is presumably derived from the horizontal acceleration signature from the IMU. Stryd states that it captures the speed attenuation during the contact phase with 95% accuracy. This is not bad when considering that this anterio-posterior dynamics is a difficult one to capture.
2) Error Propagation : A 5% error in estimation of kinetic energy change propagates a proportional error into the work and power calculations. 
3) Influence of speed and slope not discussed : Like in the case of the modeled vertical force-time profile, the influence of different speeds and slopes on the goodness-of-fit are not specifically mentioned. This is important to document in order to compare how the fit varies under different situations. See Appendix image A4 for grade influences on horizontal force-time curve curves.

III. Basis of concentric and eccentric work scaling factors not discussed : A technical basis for the scaling factors employed by the Stryd model to account for the dominance of concentric or eccentric work during gradient running is desirable (see Part A.II and Figure 1). 

This correction was made as a firmware update in early 2017. 

While I understand this is part of the secret sauce, some pedantic questions are necessary to be asked if we want to remain true to the estimation of a power : 

A) Is the scaling used related more to vertical and horiontal ground forces or is it calibrated with metabolic costs? What was the validation study behind these and will those findings translate well for the general public?

B) Is the power scaling a continuous linear decrease for downhill grades and a continuous linear increase for uphill grades? 

Let's suppose scaling is calibrated against metabolic costs. If power follows a strictly linear decrease for steep downhills while the metabolic cost decrease sharply, this will suggest that the metabolic efficiency increases. Conversely, if power follows a strictly linear increase for uphills while the metabolic cost follows a curvilinear relation to grade , that might suggest that metabolic efficiency becomes progressively worse. There are individual variations playing into metabolic cost dynamics on grade.  

For downhills, a strict metabolic cost decrease may not even hold. For example, it has been documented that beyond a grade of -9 degrees, the metabolic rate actually increases, presumably from the high eccentric cost to maintain balance of center of mass. In other words, there is an optimum downhill angle beyond which metabolic cost increases. 

How that scaling curve has been calibrated is of much interest to me, and I assume, to other scientifically minded runners.

C) Some reports indicate runners losing RPE-power correlation for gradient running. This begs the question whether the scaling factors should be something that is best left for the runner to tune and calibrate through the settings instead of being driven down from Stryd. 

VI. A Review of Stryd Statement on Correlation with VO2

Stryd states in the whitepaper that : "The external mechanical power reported by Stryd has a well established relationship with metabolic expenditure based on testing conducted by Stryd and other third party research teams. "

Now I have done this too in the past during a VO2max test, plotting the relationship of VO2 to W/kg. This can be done by absolutely anyone. 

I consider the statement regarding "well-established relationship" to be a sleight of hand.  In other words, if I take a powermeter that is algorithmically modeled in such away that external power is linearly proportional to running speed (atleast on flat terrain), ofcourse a VO2 test is going to show that ave VO2 at each speed is proportional to external power! This relationship has been "pegged" from the beginning due to speed being an input in the model. There is no unique science in this.

The linear relationship of VO2 to power is encouraging in as far as it only tells us that model algorithm involving speed works.

The next question would be : Can you use a running power meter be used to predict running economy? This carries a risk of mis-estimation because we do not know how transferable such simple relations are going from indoors to outdoors.

The estimation gets worse when it is a formula derived from a book which based it on data from a limited sample of runners that you weren't a part of.  I do not believe you can estimate metabolic cost this way with any reliable degree of accuracy just like HR or HR based formulas cannot estimate caloric expenditure with any reliable degree of accuracy.

Indeed, a study from the University of Guelph and presented at the recent Canadian Society for Exercise Physiology (CSEP) annual meeting in Winnipe challenged the idea.

The researchers found a significant difference in running economy between treadmill and track running for 11 experienced elite runners as measured by standard metabolic measurements. But in the same study, the Stryd power meter and formulaic implementations of economy couldn't pick up any difference between the two surfaces.

Figure 7 : Plot showing VO2 as a function of specific external power reported by Stryd. Since power is linearly proportional to speed, such a relationship is already mostly pegged by design of the algorithm.


V. Leg Spring Stiffness : Information Only Or Actually Used?

For level running, we understand from scientific literature that the storage and recoil of energy in the lower limbs restores an appreciable amount of energy into the positive work phase. Thus, the mechanical efficiency of running maybe greater than 30% depending on the skill and mechanics of the runner. This efficiency of running has a documented linear relationship to running speed. 

Stryd calculates leg spring stiffness (LSS) using published models derived from the mass-spring paradigm. But it is unclear how Stryd's model employs the stiffness into the mechanical work calculations to account for a "savings" in concentric work requirements. In contrast, the GOVSS model appreciates there maybe savings from efficiency increases as a function of speed and corrects the power demand depending on running speed.

So the question is whether leg spring stiffness is simply a metric displayed for information only or whether it is actually used in the model in a fashion as described in the para above? If it is not used, then someone can question the actual value of this. The LSS metric remains one of the most confusing metrics from a trainability standpoint.

VI. Apparent Mechanical Efficiency of Running = 25%

The apparent mechanical efficiency of running is defined as :

Meff, a = ratio of external power (Pext) and metabolic rate (Pmet). Meff, a = Pext/Pmet.

The gross mechanical efficiency of running is differentiated as :

Meff,g = ratio of total power (Ptot) and metabolic rate (Pmet). Meff,g = Ptot/Pmet.

In the context of a Stryd powermeter, we should be concerned about the apparent mechanical efficiency.

As alluded to in section V, human running involves energy storage and recoil going from the negative to positive phase. Several researchers have found that upto 40-50% of the energy stored during the eccentric phase can be returned to the concentric phase within the short time span of ground contact time for which those muscles remain in a loaded state.

Maximum possible elastic energy storage is defined in some papers (such as those written by Kram to be the difference between initial and minimum external energy of center of mass during the stance phase. Energy return is defined in the same papers to be the difference between the ending and minimum external energy of center of mass during the stance phase. The maximum possible energy storage and recovery is then taken as the smaller of these two values.

To me, the way the efficiency is defined and what it takes into account (or what it doesn't) explains a lot of the differences in calculated efficiency between different running power models now arriving in the market. I suspect that an apparent mechanical efficiency value of 25% is artificially low, atleast for running on level and shallow slopes, if it didn't take into account energy storage and recovery mechanics between the negative and positive phases of running.

VII. Effect of Wind on Stryd's Performance

The Stryd powermeter has no way to account for wind effects in the power calculation. In this respect, it will under-report power by a factor proportional to the correct relative velocity cubed. By "correct", I mean that the wind measured has to be applied at the height of the runner, and not what is reported from a 10m or 30m wind tower. 

That said, one of the "buggy" issues, as has been reported by several people both on the Stryd support forums and the Running Power Google Groups, is the sensitivity of power to sudden wind gusts. Reports indicate that gusts cause unsteady spikes in power for some and dips in the reported power for others, which is physically incorrect if you were trying to maintain speed in the face of a headwind. 

The root technical fault maybe with the barometer, which thinks that the a sudden pressure front is a change in pressure altitude. How that information is relayed through sensor fusion and into the Stryd algorithm to mess up the reported power is a mystery to me. Judging by the forums, even Stryd's engineers have a challenge grappling with this issue.

The fact that they may need several consistently gusty days outside to test what's wrong might mean the delay of a corrective action for users.

VIII. Conclusions

Stryd's description of the external running power model and a comparison of modeled variables against force plate data has been long in the making. It is appreciated but delivered a bit late.

From a brief reading, I assess that they employ the general EESA approach to external power with some "in-house" tweaking for uphills and downhills to account for net energy addition or dissipation. 

Stryd is thinking several things, some unique, some literature driven, about the kinematics of running. I give them credit for that.  However, it does not stop the questions about how the model employed will validate for a large number of runners in actual usage.  This same question also goes for the GOVSS run power model. 

The effect of running speed, footstrike variations and slopes on those errors were largely missing from the whitepaper. This was the most important aspect I would have liked to see. This unfortunately prevents an assessment of how closely IMUs can correctly decifer footstrike patterns across a broad range of runners, running speeds and terrain.

Though the stated errors in key variables and things like force-time curves are small, those errors propagate into the calculations of derived metrics. Users must be aware of this when trying to introduce running interventions to effect a change in some of these metrics.  

It is hoped that this technical review will encourage them to release another round of whitepapers so we can understand that aspect.  An independant scientific review from other laboratories is also desirable in order to establish the degree of reproducibility in these numbers.  

With Stryd and Runscribe having published their running models, the lights fall onto Garmin. With a far greater user base, they should find impetus to publish their running power framework soon or risk a lukewarm interest from the market.

In the next post, I'll explore how errors in estimated ground reaction forces translate into errors in the external power calculations from the EESA method. Stay tuned...


Figure A1 : An illustration of vertical ground reaction force-time curve along the gait cycle. Courtesy Weyand (2010).

Figure A2 : An illustration of the horizontal ground reaction force-time curve (lower plot). Courtesy Farley & Ferris.

Figure A3 : Specific vertical force-time profiles for unshod rearfoot-striker, shod
rear foot-striker and a barefoot forefoot-striker at 3.5 m/s running speed.  Courtesy Liebermann (2010).

Figure A4 : Vertical and horizontal force-time curves for a 73kg subject running at 3 m/s over the indicated grades. For the vertical force profile, the first impact peak substantially increases as grade plummets. On the uphills, the second peak substantially increases to the point where at +9 degrees, the slope is rounded. Peak vertical forces decrease as grade steepens. For the horizontal force profile, the negative part of the S curve substantially increases as grade plummets while the curve more or less assumes a half sinusoid. On the uphills, the positive part of the S curve substantially increases as the grade increases while the curve as a whole more or less assumes a half sinusoid. Courtesy Gotschall (2005).

Figure A5 : Submaximal VO2 is linearly related to speed. Courtesy Kram