##### https://github.com/kuo-lab/simplelocomotionmodel
Tip revision: fc46ca7
shortwalks.qmd
---
title: "Dynamic optimization of human walking speeds"
format:
html:
code-fold: true
ipynb: default
jupyter: julia-1.8
bibliography: simplelocomotionmodel.bib
---
## Optimization of energy and time predicts dynamic speeds for human walking [@carlisle2022OptimizationEnergyTime]
Take walks of varying distances, and show how the optimal trajectory has an inverted-U
velocity profile, with peak speed that increases with distance up to about 12 steps, leveling off thereafter.
The cost function is net mechanical work, plus a linear cost of time with coefficient ctime.

### Go for a single walk
Take a walk of 10 steps, starting and ending at rest. Find the optimal push-offs that minimize total work. The optimization is performed with optwalktime which uses a time cost (relative to work) of tchange.
{julia}
#| fig-cap: "A short walk"
#| fig-cap: "Speed vs time for a short walk. The mid-stance speeds, push-offs, and terrain heights are plotted vs discrete step number."
using DynLoco, Plots, Statistics

wstar4 = findgait(WalkRW2l(α=0.35,safety=true), target=:speed=>0.3, varying=:P)
ctime = 0.015 # cost of time, to encourage hurrying
tchange = 1.75 # boundary condition time to get up to speed (arbitrary, excluded from optimization)
p = plot()
nsteps = 10
result = optwalktime(wstar4, nsteps, ctime=ctime) # optimize work and time
multistepplot(result, legend=false) # plot speed, push-off, terrain trajectories

All quantities are plotted dimensionlessly, with base units of body mass $M$, leg length $L$, and gravitational acceleration $g$. Thus speed is normalized by $\sqrt(gL)$ and time by $\sqrt(L/g)$. For a typical leg length of $L = 1 \mathrm{m}$, the dimensional speed is about 1.25 m/s, and step time about 0.55 s.

### Go for walks of verying distance
The predicted speed profile varies with distance. Shorter walks have very brief and slower peaks, and are dominated by speeding up and slowing down. Longer walks have flatter and faster peaks, approching a steady walking speed. Relatively less time is spent speeding up and slowing down.
{julia}
#| fig-cap: "Short walks of varying distance"
#| fig-cap: "Speed vs time for short walks; each trace is a different bout distance"
p = plot()
walksteps = [1, 2, 3, 4, 5, 6, 7, 10, 15, 20] # take walks of this # of steps
results = Array{MultiStepResults,1}(undef,0) # store each optimization result here
for (i,nsteps) in enumerate(walksteps)
result = optwalktime(wstar4, nsteps, ctime=ctime) # optimize with a cost of time
plotvees!(p, result, tchange=tchange, usespline=false, color=i, speedtype=:shortwalks, rampuporder=1, markersize=2) # plot instantaneous body speed vs. time
push!(results, result) # add this optimization to results array
end
Plots.display(p) # instantaneous speed vs. distance profiles

Here the speeds are plotted as "body speed" each step, to match IMU. Model is parameterized by mid-stance speed each step, but IMU data only yields strides. As described by @carlisle2022OptimizationEnergyTime, we use estimated mid-stance times to estimate body speed.

## Compare three objectives: Energy-Time, min-COT, constant acceleration
Walk a fixed number of steps, starting and ending at rest. The
objectives are:

* **Energy-Time** minimizes total energy (positive work) plus proportional time cost
* **min-COT** walks at a constant speed that minimizes cost of transport (energy per weight and distance traveled), with a trapezoidal speed profile. This is achieved by minimizing deviation from minCOT speed, to allow model to accelerate to that speed.
* **Constant accel** accelerates at a constant rate, to yield a triangular speed profile. Uses a minimum variance objective to
produce a constant rate of velocity change.

Compare for a fixed number of steps.
{julia}
# A minCOT nominal gait
wstar4n = findgait(WalkRW2l(α=0.35, safety=true), target=:speed=>0.4, varying=:P) # use a speed of 0.4 to match minCOT
nsteps = 10
ctime = 0.0195
tchange = 1.75
nominalresult=optwalktime(wstar4n, nsteps, ctime = ctime, boundarywork=true) # to compare with our usual solution

# minCOT walk. optwalkvar minimizes variance from nominal
minvarresult=optwalkvar(wstar4n, nsteps, boundarywork=true)

# steady acceleration walk, which tries to maintain same
# acceleration each step, resulting in a triangular speed
# profile
A = 0.0655 # 1.9*wstar4n.vm/(nsteps*onestep(wstar4n).tf) # acceleration
v0 = 0.11 # 0.8*A*tchange # a couple acceleration constants
# chosen by hand to allow smooth transition from initiation
# to push-off.

constaccelresult = optwalktriangle(wstar4n, nsteps, A = A, boundarywork=false,boundaryvels=(v0,v0))

# Make the plots
p = plot(layout=(1,2))
plotvees!(p[1],nominalresult, tchange=tchange, rampuporder=1, usespline = false, markershape=:circle,speedtype=:shortwalks)
plotvees!(p[1],minvarresult, tchange=tchange, rampuporder=1, usespline = false,markershape=:circle, speedtype=:shortwalks)
plotvees!(p[1],constaccelresult, tchange=tchange, rampuporder=1, usespline = false,markershape=:circle, speedtype=:shortwalks, seriescolor=:auto)
plot!(p[2],[0:nsteps+1], [1/2*nominalresult.vm0^2; nominalresult.steps.Pwork; NaN],markershape=:circle,seriescolor=:auto)
plot!(p[2],[0:nsteps+1], [1/2*minvarresult.vm0^2; minvarresult.steps.Pwork; NaN],markershape=:circle,xticks=0:nsteps+1)
plot!(p[2],[0:nsteps+1], [1/2*constaccelresult.vm0^2; constaccelresult.steps.Pwork; NaN],markershape=:circle,xticks=0:nsteps+1,seriescolor=:auto)
plot!(p[2],xlabel="step", ylabel="push-off work", legend=false)
energytimework = 1/2*nominalresult.vm0^2 + sum(nominalresult.steps.Pwork)
mincotwork = 1/2*minvarresult.vm0^2 + sum(minvarresult.steps.Pwork)
trianglework = (1/2*constaccelresult.vm0^2 + sum(constaccelresult.steps.Pwork))/(1/2*nominalresult.vm0^2 + sum(nominalresult.steps.Pwork))
Plots.display(p)

Quantify the three predictions. The energy cost for each walk consists of the positive work for gait initiation plus the positive push-off work for all steps. Negative work is ignored, because equal magnitudes of positive and negative work are performed for this task. If there are constant efficiencies for muscles to perform positive and negative work, the physiological cost of negative work is proportional to positive work. This affects the total metabolic cost, but does not affect the optimal solutions.
{julia}
threecosts = [1/2*nominalresult.vm0^2 + sum(nominalresult.steps.Pwork), 1/2*minvarresult.vm0^2 + sum(minvarresult.steps.Pwork), 1/2*constaccelresult.vm0^2 + sum(constaccelresult.steps.Pwork)]
println("The energy-time work is $(threecosts[1])") println("The min-COT work is$(threecosts[2])")
println("The const accel work is $(threecosts[3])") bar(threecosts,xticks=((1,2,3),("Energy-Time", "Steady min-COT", "Steady accel")),legend=false,ylabel="Total work")  Minimization of cost of transport is not energetically optimal, in part because it requires a costly fast speed-up to reach the min-COT speed. It can be less costly to vary speed dynamically. Steady acceleration is also costly, due to expense of accelerating at high speeds, which requires considerable power. ## Shorter and longer step lengths do not affect waveform shapes Longer steps are more costly because of collisions, but doesn't change peak speed much. For fixed number of steps, longer steps travel a greater total distance (in greater time) and reach a slightly higher speed. For a fixed distance, longer steps also obviously require fewer steps. The following plot shows fixed step lengths slightly shorter or longer than nominal. Humans take longer steps at faster steady speeds. Applying the approximate preferred relationship here (step length increasing with$v^0.42$) yields similar speed profiles to fixed step lengths. The model here modulates step length, but does not include the swing cost for doing so. That cost is thought to be relatively higher at fast speeds [@kuo2001SimpleModelBipedala]. {julia} wstar4 = findgait(WalkRW2l(α=0.35,safety=true), target=:speed=>0.3, varying=:P) wstar43 = findgait(WalkRW2l(α=0.3,safety=true), target=:speed=>0.3, varying=:P) # shorter steps wstar44 = findgait(WalkRW2l(α=0.4,safety=true), target=:speed=>0.3, varying=:P) # longer steps wsteplens = [wstar43, wstar4, wstar44] ctime = 0.015 # cost of time, to encourage hurrying tchange = 1.75 p = plot() walksteps = [1, 2, 3, 4, 5, 6, 7, 10, 15, 20] # take walks of this # of steps results43 = Array{MultiStepResults,1}(undef,0) # store each optimization result here results = Array{MultiStepResults,1}(undef,0) # store each optimization result here results44 = Array{MultiStepResults,1}(undef,0) # store each optimization result here peakspds = zeros(length(walksteps)) peakspeeds43 = zeros(length(walksteps)) peakspeeds44 = zeros(length(walksteps)) durations = zeros(length(walksteps)) durations43 = zeros(length(walksteps)) durations44 = zeros(length(walksteps)) for (i,nsteps) in enumerate(walksteps) result = optwalktime(wsteplens[1], nsteps, ctime=ctime) # optimize with a cost of time plotvees!(result, tchange=tchange, usespline=false, color=i, speedtype=:shortwalks, rampuporder=1, markersize=2) # plot instantaneous speed vs. time push!(results43, result) # add this optimization to results array peakspeeds43[i] = maximum(stepspeeds(result.steps)[2]) durations43[i] = result.totaltime result = optwalktime(wsteplens[2], nsteps, ctime=ctime) # optimize with a cost of time plotvees!(result, tchange=tchange, usespline=false, color=i, speedtype=:shortwalks, rampuporder=1, markersize=2) # plot instantaneous speed vs. time push!(results, result) # add this optimization to results array peakspds[i] = maximum(stepspeeds(result.steps)[2]) durations[i] = result.totaltime result = optwalktime(wsteplens[3], nsteps, ctime=ctime)# # optimize with a cost of time plotvees!(result, tchange=tchange, usespline=false, color=i, speedtype=:shortwalks, rampuporder=1, markersize=2) # plot instantaneous speed vs. time push!(results44, result) # add this optimization to results array peakspeeds44[i] = maximum(stepspeeds(result.steps)[2]) durations44[i] = result.totaltime end # longer steps took longer and resulted in almost same peak speed but of course traveled farther distances43 = [sum(result.steps.steplength) for result in results43] distances = [sum(result.steps.steplength) for result in results] distances44 = [sum(result.steps.steplength) for result in results44] # now applying steps increasing with v^0.42 using WalkRW2lvs (linearized, varying step length) wstar4vs = findgait(WalkRW2lvs(α=0.35,safety=true), target=:speed=>0.3, varying=:P) ctime = 0.05 # cost of time, to encourage hurrying tchange = 1.75 #pv = plot() walksteps = [1, 2, 3, 4, 5, 6, 7, 10, 15, 20] # take walks of this # of steps resultvss = Array{MultiStepResults,1}(undef,0) # store each optimization result here tees = zeros(length(walksteps),3) peakspeedvss = zeros(length(walksteps)) durationvss = zeros(length(walksteps)) for (i,nsteps) in enumerate(walksteps) result = optwalktime(wstar4vs, nsteps, ctime=ctime) # optimize with a cost of time plotvees!(result, tchange=tchange, usespline=false, speedtype=:shortwalks, color=i, rampuporder=1, markersize=2, linestyle=:dot) # plot instantaneous speed vs. time push!(resultvss, result) peakspeedvss[i] = maximum(stepspeeds(result.steps)[2]) durationvss[i] = result.totaltime # add this optimization to results array end distancevss = [sum(result.steps.steplength) for result in resultvss] Plots.display(p) # instantaneous speed vs. distance profiles  ## The valuation of time affects peak speed, but preserves self-similarity of speed profiles Here we vary the valuation of time$c_T$by an order of magnitude, and find that it causes peak speeds to approximately double. The main plot below shows variations in$c_T$and in step length all scaled to resemble each other and superimposed. The scaling is used to demonstrate self-similarity of speed profiles regardless of step length and$c_T$. The insets show variation in step length in unscaled form (horizontal insets, also shown above), and variation in$c_T$in unscaled form (vertical insets). Walks are shown for between 1 and 20 steps, for three fixed step lengths (plus varying step length according to human preference), for six different valuations of time ($c_T$ranging 0.006 to 0.06). All units are dimensionless, using body mass$M$, leg length$L$, and gravitational acceleration$g$as base units. For a person of typical leg length$L = 1\,\textrm{m}$, the nominal speed is equivalent to 1.25 m/s. {julia} wstar4 = findgait(WalkRW2l(α=0.35), target=:speed=>0.3, varying=:P) ctimes = (0.006, 0.015, 0.0276, 0.0384, 0.0492, 0.06) # valuation of time over a large range tchange = 1.75 walksteps = [1, 2, 3, 4, 5, 6, 7, 10, 15, 20] # take walks of this # of steps peaks = zeros(length(walksteps),length(ctimes)) durations = similar(peaks) results = Array{MultiStepResults,2}(undef,(length(walksteps),length(ctimes))) # store each optimization result here for (j,ctime) in enumerate(ctimes) for (i,nsteps) in enumerate(walksteps) result = optwalktime(wstar4, nsteps, ctime=ctime) # optimize with a cost of time peaks[i,j] = maximum(stepspeeds(result.steps)[2]) durations[i,j] = result.totaltime results[i,j] = result end end tbase = durations[end,2] vbase = peaks[end,2] pleft = plot(; )#@layout [grid(1,3); a{0.86h}]) pright = plot(; layout=grid(6,1)) ptop = plot(; layout=grid(1,3)) for (j, ctime) in enumerate(ctimes) for (i,nsteps) in enumerate(walksteps) result = results[i,j] plotvees!(pright,result, tchange=tchange, color=i, usespline=:false, speedtype=:shortwalks,markersize=0, subplot=j, xticks = [20,40], yticks=[0.2,0.4,0.6],xguide="",yguide="",tickfontsize=4, xlims=(0,maximum(durations)+3tchange), ylims=(0,maximum(peaks)), linewidth=0.5) # subplot instantaneous speed vs. time plotvees!(pleft,result, tchange=tchange, color=i, usespline=:false, speedtype=:shortwalks,markersize=2, tscale = tbase/(durations[end,j]), vscale = vbase/peaks[end,j],subplot=1) # main scaled speed vs time end end for (i,result) in enumerate(resultvss) # add in the variable step length results computed above in resultvss (dash-dot lines) plotvees!(pleft,result, tchange=tchange, usespline=false, speedtype=:shortwalks, color=i, markersize=2, linestyle=:dashdot,subplot=1, tscale = tbase/(durationvss[end]),vscale = vbase/peakspeedvss[end]) # plot instantaneous speed vs. time plotvees!(ptop,result, tchange=tchange, color=i, usespline=:false, speedtype=:shortwalks,markersize=0, xticks = [20,40], yticks=[0.2,0.4,0.6],subplot=2,xguide="",yguide="",tickfontsize=4, xlims=(0,maximum(durations)+3tchange), ylims=(0,maximum(peaks)), linewidth=0.5) end for (i,result) in enumerate(results43) # add in shorter steps (dashed lines) plotvees!(pleft,result, tchange=tchange, color=i, usespline=:false, speedtype=:shortwalks,markersize=2, tscale = tbase/(durations43[end]), vscale = vbase/peakspeeds43[end],subplot=1, linestyle=:dash) plotvees!(ptop,result, tchange=tchange, color=i, usespline=:false, speedtype=:shortwalks,markersize=0, xticks = [20,40], yticks=[0.2,0.4,0.6],subplot=1,xguide="",yguide="",tickfontsize=4, xlims=(0,maximum(durations)+3tchange), ylims=(0,maximum(peaks)), linewidth=0.5) end for (i,result) in enumerate(results44) # add in longer steps (dotted lines) plotvees!(pleft,result, tchange=tchange, color=i, usespline=:false, speedtype=:shortwalks,markersize=2, tscale = tbase/(durations44[end]), vscale = vbase/peakspeeds44[end],subplot=1, linestyle=:dot) plotvees!(ptop,result, tchange=tchange, color=i, usespline=:false, speedtype=:shortwalks,markersize=0, xticks = [20,40], yticks=[0.2,0.4,0.6],subplot=3, xguide="",yguide="",tickfontsize=4, xlims=(0,maximum(durations)+3tchange), ylims=(0,maximum(peaks)), linewidth=0.5) end println("Durations of a factor of ", (durations[end,1]+2tchange)/(durations[end,end]+2tchange)) println("Peak speeds over a range of ", peaks[end,end]/peaks[end,1]) println(" about ", peaks[end,1]*sqrt(9.81)," to ", peaks[end,end]*sqrt(9.81), "m/s") peaks[end,:]*sqrt(9.81) plot(ptop, pleft, pright, layout= @layout [ [a{0.15h}; b] c{0.15w}])  ## Peak speeds and durations increase with distance Peak speeds increase toward a saturating value with increasing distance. This relationship is self-similar, in that even with different fixed or varying step lengths, the saturating behavior is similar. The time duration to walk a distance also increases with distance, approaching a straight asymptote with longer distances. The prediction is more curved for shorter distances. The following plot shows how peak speeds increase with distance. Greater$c_T$shifts the peak speed upward. The thin lines show the absolute durations, and the thick lines show the durations after scaling them to demonstrate self-similarity. (The results here also include different step lengths.) {julia} distances = [sum(result.steps.steplength) for result in results] p1=plot(distances, peaks, xlabel="Distance", ylabel="Peak speed", xlims=(0,maximum(distances)), ylims=(0,Inf),legend=false, linewidth=0.2) plot!(p1, distances43, peakspeeds43, linestyle=:dash, linewidth=0.2) plot!(p1, distances44, peakspeeds44, linestyle=:dot, linewidth=0.2) plot!(p1, distancevss, peakspeedvss, linestyle=:dashdot, linewidth=0.2) plot!(p1, distances, (peaks .* middle(peaks[end,:]) ./ maximum(peaks,dims=1)), linewidth=0.5)  The following shows how walking durations increase with distance. Greater$c_T$results in shorter durations, but the relationship is self-similar. The thin lines show the absolute durations, and the thick lines show the durations after scaling them to demonstrate self-similarity. {julia} p1=plot(distances, durations, xlabel="Distance", ylabel="Duration",legend=false,linewidth=0.2) plot!(distances, durations .* middle(durations[end,:])./ maximum(durations,dims=1), linewidth=0.5,xlabel="Distance", ylabel="Duration",legend=false) plot!(distances43, durations43 .* middle(durations[end,:])./ maximum(durations43,dims=1), linewidth=0.5,xlabel="Distance", ylabel="Duration",legend=false,linestyle=:dash) plot!(distances44, durations44 .* middle(durations[end,:])./ maximum(durations44,dims=1), linewidth=0.5,xlabel="Distance", ylabel="Duration",legend=false,linestyle=:dash) plot!(distancevss, durationvss .* middle(durations[end,:])./ maximum(durationvss,dims=1), linewidth=0.5,xlabel="Distance", ylabel="Duration",legend=false,linestyle=:dash)  ## Valuation of time affects peak speed The following plot shows peak speed vs valuation of time$c_T$. The thin lines show the absolute speeds, and the thick lines show the speeds after scaling them to demonstrate self-similarity. Also included here are walks of various distances, where longer walks have a peak speed that is nearly steady, and shorter walks have only brief peaks. Nevertheless, peak speed increases with$c_T\$, with
a roughly cube root relationship.

{julia}
## Vary ct and # steps, with finer increments than before
morectimes = range(ctimes[begin], ctimes[end], length=16)
walksteps = [1,2,3,4,5,6, 7, 10, 15, 20] # take walks of this # of steps

cresults = [optwalktime(wstar4, nsteps, ctime=ctime) for nsteps in walksteps, ctime in morectimes]
cpeaks = [maximum(stepspeeds(r.steps)[2]) for r in cresults]
cdurations = [r.totaltime for r in cresults]
sec = sqrt(1/9.81); mps = sqrt(9.81)
p1=plot(morectimes, mps.*cpeaks',legend=false,xlabel="c_t",ylabel="peak speed",linewidth=0.2)
plot!(p1,morectimes, mps.*(cpeaks .* middle(cpeaks[:,end])./ maximum(cpeaks,dims=2))',linewidth=1,legend=false, xlabel="valuation of time c_t",ylabel="peak speed")


## Experimental data
The data from accompanying human subjects experiment are available in a [separate data and code repository](https://github.com/kuo-lab/short_walk_experiment/). The code is in Matlab, and the data files are in .mat format, which
is compatible with HDF5.

# Julia code
This page is viewable as [Jupyter notebook](shortwalks.ipynb), [plain Julia](shortwalks.jl) text, or [HTML](shortwalks.html).

## References
::: {#refs}
:::