Dante Goss Final Project
Dante Goss II
5/8/2022
Dante Goss II Final Project
This is document will provide a background of the data and its collection, how I prepared it for analysis, and the analysis itself. Background: This is pilot data I collected earlier in the semester. It comes from a device called the “Loadsol” which are basically force plates in the form of an insole. I’m interested in the effect of cadence on knee joint loading, which I’m using ground reaction force as a surrogate of. I wore the sensors and walked at increasing cadences on a treadmill, giving me the ability to hold speed constant. For this preliminary analysis I’ll be looking at cadences of 100, 110, 120, and 130 steps per minute at 2.2 miles per hour. These numbers were chosen based on previous research, with 2.2 miles per hour being the self selected speed of many adults with knee osteoarthritis, and cadence ranging between 100 and 130 depending on intensity. The data in it’s raw form is essentially a long time series with forces in the heel, midfoot, and forefoot of each foot, along with a total. My goal is to look at 5 consecutive steps for each cadence on both legs, compare the waveforms themselves graphically, as well as statistically via their averages.
Load in data
I make sure my directory exists, set the directory, check the file, and load in the data.
#check the path exists (2 Points-dir.exists)
dir.exists("C:/Users/ddg12/OneDrive - University of Virginia/Loadsol")## [1] TRUE#set the path
setwd("C:/Users/ddg12/OneDrive - University of Virginia/Loadsol")
#Check if file exists (2 Points-file.exists)
file.exists("Cadence Trials (DG 3-24).txt")## [1] TRUE#Read in data (1 Point-R)
DGalldata<-as.data.frame(read.table("Cadence Trials (DG 3-24).txt",header=TRUE,sep = "\t"))
#Cadence Trials (JX 3-24).txt
#DGalldata<-as.data.frame(read.table("Cadence Trials (JX 3-24).txt",header=TRUE,sep = "\t"))Start prepping data
Now that I have the data, I start off by getting rid of some extra columns (one was created by there being an extra tab in my import, one is because the data lists time for both legs simultaneously). I save the original names to a variable since it has the name of the sensor (I would like to record which sensors I used). Then I rename the columns to remove the sensor number.
head(DGalldata)#get rid of extra column $NA (1 Point-subset)
DGalldata<-DGalldata[-11]
head(DGalldata)#Get rid of the extra time column
DGalldata <- DGalldata[-6]
#Check if column names are correct (1 Point-head)
head(DGalldata)names(DGalldata)## [1] "Time" "PRX189.L..Heel" "PRX189.L..Midfoot"
## [4] "PRX189.L..Forefoot" "PRX189.L" "PRX198.R..Forefoot"
## [7] "PRX198.R..Midfoot" "PRX198.R..Heel" "PRX198.R"#Store original names since they have sensor values (1 Point-print)
OGnames <- print(names(DGalldata))## [1] "Time" "PRX189.L..Heel" "PRX189.L..Midfoot"
## [4] "PRX189.L..Forefoot" "PRX189.L" "PRX198.R..Forefoot"
## [7] "PRX198.R..Midfoot" "PRX198.R..Heel" "PRX198.R"#change names of columns to add units (1 Point-changing names)
names(DGalldata) <-c("Time","LeftHeel(N)", "LeftMidfoot(N)", "LeftForefoot(N)",
"LeftTotalForce","RightForefoot(N)", "RightMidfoot(N)",
"RightHeel(N)", "RightTotal(N)")Isolate waveforms
Now I went ahead and isolated the individual waveforms. The first step for this is putting in a threshold, in my case any data in the total force columns below 20N meant the foot wasn’t in contact with the ground. I made a list of all the indices where the data was below 20N in the total force columns (one for each leg).
#tells me when the value is less than 20
LeftLegFilter<-which(DGalldata[5]<20)
#LeftLegFilter<-as.data.frame(LeftLegFilter) #(1 Point-Change data type)
LeftLegFilter<-cbind(LeftLegFilter,diff(LeftLegFilter))#find differences for easier searching## Warning in cbind(LeftLegFilter, diff(LeftLegFilter)): number of rows of result
## is not a multiple of vector length (arg 2)head(LeftLegFilter)## LeftLegFilter
## [1,] 67 1
## [2,] 68 1
## [3,] 69 1
## [4,] 70 1
## [5,] 71 1
## [6,] 72 1#Right leg
RightLegFilter<-which(DGalldata$`RightTotal(N)`<20)
#RightLegFilter<-as.data.frame(RightLegFilter)
RightLegFilter<- cbind(RightLegFilter,diff(RightLegFilter))## Warning in cbind(RightLegFilter, diff(RightLegFilter)): number of rows of
## result is not a multiple of vector length (arg 2)head(RightLegFilter)## RightLegFilter
## [1,] 20 1
## [2,] 21 1
## [3,] 22 1
## [4,] 23 1
## [5,] 24 1
## [6,] 25 1Create data frames for waveforms
Going into this I already knew what timestamps would correspond to each cadence, so I used the filters to choose 5 consecutive cycles. I created data frames for each cycle including all the partial foot and total foot data, then put all of the 5 waveforms for once cadence on one leg into a list.
#based on known timestamps for each protocol, find 5 gait cycles in each
#100 SPM 1000-3000
Left100S1<-DGalldata[1490:1567,1:5]
Left100S2<-DGalldata[1610:1686,1:5]
Left100S3<-DGalldata[1729:1806,1:5]
Left100S4<-DGalldata[1851:1927,1:5]
Left100S5<-DGalldata[1972:2047,1:5]
left100list<- list(Left100S1,Left100S2,Left100S3,Left100S4,Left100S5)#puts all the data into a list
Right100S1<-DGalldata[1428:1509,c(1,6:9)]
Right100S2<- DGalldata[1549:1630,c(1,6:9)]
Right100S3<- DGalldata[1669:1753,c(1,6:9)]
Right100S4<- DGalldata[1789:1867,c(1,6:9)]
Right100S5<- DGalldata[1911:1988,c(1,6:9)]
right100list<- list(Right100S1,Right100S2,Right100S3,Right100S4,Right100S5) #2 Points (lists)
#110 SPM 4100-5400
Left110S1<- DGalldata[4647:4717,1:5]
Left110S2<- DGalldata[4756:4826,1:5]
Left110S3<- DGalldata[4865:4935,1:5]
Left110S4<- DGalldata[4973:5043,1:5]
Left110S5<- DGalldata[5083:5153,1:5]
left110list<- list(Left110S1,Left110S2,Left110S3,Left110S4,Left110S5)
Right110S1<- DGalldata[4701:4776,c(1,6:9)]
Right110S2<- DGalldata[4808:4882,c(1,6:9)]
Right110S3<- DGalldata[4917:4989,c(1,6:9)]
Right110S4<- DGalldata[5025:5102,c(1,6:9)]
Right110S5<- DGalldata[5136:5211,c(1,6:9)]
right110list<- list(Right110S1,Right110S2,Right110S3,Right110S4,Right110S5)
#120 SPM 6000-8000
Left120S1<- DGalldata[7007:7073,1:5]
Left120S2<- DGalldata[7105:7169,1:5]
Left120S3<- DGalldata[7202:7268,1:5]
Left120S4<- DGalldata[7304:7371,1:5]
Left120S5<- DGalldata[7404:7473,1:5]
left120list<- list(Left120S1,Left120S2,Left120S3,Left120S4,Left120S5)
Right120S1<- DGalldata[7055:7126,c(1,6:9)]
Right120S2<- DGalldata[7152:7222,c(1,6:9)]
Right120S3<- DGalldata[7251:7321,c(1,6:9)]
Right120S4<- DGalldata[7353:7422,c(1,6:9)]
Right120S5<- DGalldata[7453:7522,c(1,6:9)]
right120list<- list(Right120S1,Right120S2,Right120S3,Right120S4,Right120S5)
#130 SPM 8600-9900
Left130S1<- DGalldata[9042:9103,1:5]
Left130S2<- DGalldata[9133:9196,1:5]
Left130S3<- DGalldata[9225:9289,1:5]
Left130S4<- DGalldata[9319:9382,1:5]
Left130S5<- DGalldata[9412:9472,1:5]
left130list<- list(Left130S1,Left130S2,Left130S3,Left130S4,Left130S5)
Right130S1<- DGalldata[9086:9156,c(1,6:9)]
Right130S2<- DGalldata[9177:9245,c(1,6:9)]
Right130S3<- DGalldata[9272:9338,c(1,6:9)]
Right130S4<- DGalldata[9363:9432,c(1,6:9)]
Right130S5<- DGalldata[9456:9521,c(1,6:9)]
right130list<- list(Right130S1,Right130S2,Right130S3,Right130S4,Right130S5)Create New Time Variable
Each of the above variables “Time” column is going to start at some number that isn’t 0. To create clean graphs I needed to create a new “Time” column that starts at 0, increases at the same sampling rate, and stops with the same number of elements as the old variable. To accomplish this I used a loop. After I had this I renamed all the variables again since the new time variable was unnamed.
#100 SPM Left
listcheck<-list()
firstcheck<-list()
a=1#(3 points- For loop)
for (i in 1:5) {
CycleLength<- nrow(assign(paste0("left",i),left100list[[i]]))*.01
firstcheck[[a]]<-assign(paste0("left",i),left100list[[i]])
listcheck[[i]]<-list(assign(paste0("Timeinsec",i),seq(.01,CycleLength,.01)))
assign(paste0("spm100l",i),value = cbind(as.vector(listcheck[[i]]),firstcheck[[a]]))
a<-a+1
}
#rename everything
names(spm100l1)<- c("timee","X","LeftHeel1", "LeftMidfoot1", "LeftForefoot1",
"LeftTotalForce1")
names(spm100l2)<- c("timee","X","LeftHeel2", "LeftMidfoot2", "LeftForefoot2",
"LeftTotalForce2")
names(spm100l3)<- c("timee","X","LeftHeel3", "LeftMidfoot3", "LeftForefoot3",
"LeftTotalForce3")
names(spm100l4)<- c("timee","X","LeftHeel4", "LeftMidfoot4", "LeftForefoot4",
"LeftTotalForce4")
names(spm100l5)<- c("timee","X","LeftHeel5", "LeftMidfoot5", "LeftForefoot5",
"LeftTotalForce5")
#110 SPM Left
listcheck<-list()
firstcheck<-list()
a=1
for (i in 1:5) {
CycleLength<- nrow(assign(paste0("left",i),left110list[[i]]))*.01
firstcheck[[a]]<-assign(paste0("left",i),left110list[[i]])
listcheck[[i]]<-list(assign(paste0("Timeinsec",i),seq(.01,CycleLength,.01)))
assign(paste0("spm110l",i),value = cbind(as.vector(listcheck[[i]]),firstcheck[[a]]))
a<-a+1
}
#rename everything
names(spm110l1)<- c("timee","X","LeftHeel1", "LeftMidfoot1", "LeftForefoot1",
"LeftTotalForce1")
names(spm110l2)<- c("timee","X","LeftHeel2", "LeftMidfoot2", "LeftForefoot2",
"LeftTotalForce2")
names(spm110l3)<- c("timee","X","LeftHeel3", "LeftMidfoot3", "LeftForefoot3",
"LeftTotalForce3")
names(spm110l4)<- c("timee","X","LeftHeel4", "LeftMidfoot4", "LeftForefoot4",
"LeftTotalForce4")
names(spm110l5)<- c("timee","X","LeftHeel5", "LeftMidfoot5", "LeftForefoot5",
"LeftTotalForce5")
#---
#120 SPM Left
listcheck<-list()
firstcheck<-list()
a=1
for (i in 1:5) {
CycleLength<- nrow(assign(paste0("left",i),left120list[[i]]))*.01
firstcheck[[a]]<-assign(paste0("left",i),left120list[[i]])
listcheck[[i]]<-list(assign(paste0("Timeinsec",i),seq(.01,CycleLength,.01)))
assign(paste0("spm120l",i),value = cbind(as.vector(listcheck[[i]]),firstcheck[[a]]))
a<-a+1
}
#rename everything
names(spm120l1)<- c("timee","X","LeftHeel1", "LeftMidfoot1", "LeftForefoot1",
"LeftTotalForce1")
names(spm120l2)<- c("timee","X","LeftHeel2", "LeftMidfoot2", "LeftForefoot2",
"LeftTotalForce2")
names(spm120l3)<- c("timee","X","LeftHeel3", "LeftMidfoot3", "LeftForefoot3",
"LeftTotalForce3")
names(spm120l4)<- c("timee","X","LeftHeel4", "LeftMidfoot4", "LeftForefoot4",
"LeftTotalForce4")
names(spm120l5)<- c("timee","X","LeftHeel5", "LeftMidfoot5", "LeftForefoot5",
"LeftTotalForce5")
#130 SPM Left
listcheck<-list()
firstcheck<-list()
a=1
for (i in 1:5) {
CycleLength<- nrow(assign(paste0("left",i),left130list[[i]]))*.01
firstcheck[[a]]<-assign(paste0("left",i),left130list[[i]])
listcheck[[i]]<-list(assign(paste0("Timeinsec",i),seq(.01,CycleLength,.01)))
assign(paste0("spm130l",i),value = cbind(as.vector(listcheck[[i]]),firstcheck[[a]]))
a<-a+1
}
#rename everything
names(spm130l1)<- c("timee","X","LeftHeel1", "LeftMidfoot1", "LeftForefoot1",
"LeftTotalForce1")
names(spm130l2)<- c("timee","X","LeftHeel2", "LeftMidfoot2", "LeftForefoot2",
"LeftTotalForce2")
names(spm130l3)<- c("timee","X","LeftHeel3", "LeftMidfoot3", "LeftForefoot3",
"LeftTotalForce3")
names(spm130l4)<- c("timee","X","LeftHeel4", "LeftMidfoot4", "LeftForefoot4",
"LeftTotalForce4")
names(spm130l5)<- c("timee","X","LeftHeel5", "LeftMidfoot5", "LeftForefoot5",
"LeftTotalForce5")
#100 SPM Right
listcheck<-list()
firstcheck<-list()
a=1
for (i in 1:5) {
CycleLength<- nrow(assign(paste0("right",i),right100list[[i]]))*.01
firstcheck[[a]]<-assign(paste0("right",i),right100list[[i]])
listcheck[[i]]<-list(assign(paste0("Timeinsec",i),seq(.01,CycleLength,.01)))
assign(paste0("spm100r",i),value = cbind(as.vector(listcheck[[i]]),firstcheck[[a]]))
a<-a+1
}
#rename everything
names(spm100r1)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce1")
names(spm100r2)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce2")
names(spm100r3)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce3")
names(spm100r4)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce4")
names(spm100r5)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce5")
#110 SPM Right
listcheck<-list()
firstcheck<-list()
a=1
for (i in 1:5) {
CycleLength<- nrow(assign(paste0("right",i),right110list[[i]]))*.01
firstcheck[[a]]<-assign(paste0("right",i),right110list[[i]])
listcheck[[i]]<-list(assign(paste0("Timeinsec",i),seq(.01,CycleLength,.01)))
assign(paste0("spm110r",i),value = cbind(as.vector(listcheck[[i]]),firstcheck[[a]]))
a<-a+1
}
#rename everything
names(spm110r1)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce1")
names(spm110r2)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce2")
names(spm110r3)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce3")
names(spm110r4)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce4")
names(spm110r5)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce5")
#120 SPM Right
listcheck<-list()
firstcheck<-list()
a=1
for (i in 1:5) {
CycleLength<- nrow(assign(paste0("right",i),right120list[[i]]))*.01
firstcheck[[a]]<-assign(paste0("right",i),right120list[[i]])
listcheck[[i]]<-list(assign(paste0("Timeinsec",i),seq(.01,CycleLength,.01)))
assign(paste0("spm120r",i),value = cbind(as.vector(listcheck[[i]]),firstcheck[[a]]))
a<-a+1
}
#rename everything
names(spm120r1)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce1")
names(spm120r2)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce2")
names(spm120r3)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce3")
names(spm120r4)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce4")
names(spm120r5)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce5")
#130 SPM Right
listcheck<-list()
firstcheck<-list()
a=1
for (i in 1:5) {
CycleLength<- nrow(assign(paste0("right",i),right130list[[i]]))*.01
firstcheck[[a]]<-assign(paste0("right",i),right130list[[i]])
listcheck[[i]]<-list(assign(paste0("Timeinsec",i),seq(.01,CycleLength,.01)))
assign(paste0("spm130r",i),value = cbind(as.vector(listcheck[[i]]),firstcheck[[a]]))
a<-a+1
}
#rename everything
names(spm130r1)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce1")
names(spm130r2)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce2")
names(spm130r3)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce3")
names(spm130r4)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce4")
names(spm130r5)<- c("timee","X","RightHeel1", "RightMidfoot1", "RightForefoot1",
"RightTotalForce5")First set of graphs
The main purpose is just to be able to qualitatively look at the waveforms themselves, specifically how similar first and second peaks are and the slopes.
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
Put all the data together
I needed to use cbind.na since each of the waveforms may be different lengths. I used the apply function to get all the means and reshaped all that into a new table that fits my later analysis. I did this for both the means and the maxes.
#create a variable with all the total force data
alldataleft100<- qpcR:::cbind.na(spm100l1[6],spm100l2[6],spm100l3[6],spm100l4[6],spm100l5[6])
head(alldataleft100)aveforces100<-apply(alldataleft100,2,mean,na.rm=TRUE)#Base of data frame (2 points- Apply)
head(aveforces100)## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4 LeftTotalForce5
## 814.0044 838.2516 837.9572 834.6934 805.1296#reshape (2 points- reshape)
lforcetable100 <- melt(aveforces100, # our data
measure.vars=c("LeftTotalForce1","LeftTotalForce2","LeftTotalForce3",
"LeftTotalForce4","LeftTotalForce5"),
variable.name="Step",
value.name="Force"
)
#aggregate then describe then use breaks on the graph (2 points aggregate)
sapply(alldataleft100,describe,na.rm=TRUE)#Good info could also be lapply## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4
## vars 1 1 1 1
## n 78 77 78 77
## mean 814.0044 838.2516 837.9572 834.6934
## sd 314.6573 302.4364 316.6412 291.6486
## median 907.28 931.86 920.565 917.4
## trimmed 856.0097 878.4051 877.1439 882.2968
## mad 127.978 78.81502 110.9726 83.17386
## min 29.4 42.96 26.3 29.64
## max 1196.86 1232.4 1261.17 1153.44
## range 1167.46 1189.44 1234.87 1123.8
## skew -1.355889 -1.378621 -1.305791 -1.585975
## kurtosis 0.6638843 0.9851759 0.7410506 1.339833
## se 35.62792 34.46582 35.85256 33.23644
## LeftTotalForce5
## vars 1
## n 76
## mean 805.1296
## sd 295.4676
## median 872.94
## trimmed 840.5808
## mad 124.7904
## min 37.28
## max 1222.48
## range 1185.2
## skew -1.225259
## kurtosis 0.7259215
## se 33.89246sapply(alldataleft100,max,na.rm=TRUE)#find maxes for each (should be first peak)## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4 LeftTotalForce5
## 1196.86 1232.40 1261.17 1153.44 1222.48#----
#create a variable with all the total force data
alldataleft110<- qpcR:::cbind.na(spm110l1[6],spm110l2[6],spm110l3[6],spm110l4[6],spm110l5[6])
head(alldataleft100)aveforces110<-apply(alldataleft110,2,mean,na.rm=TRUE)#Base of data frame
head(aveforces110)## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4 LeftTotalForce5
## 811.7621 792.9970 788.1965 808.3651 790.7163#reshape
lforcetable110 <- melt(aveforces110,
measure.vars=c("LeftTotalForce1","LeftTotalForce2","LeftTotalForce3",
"LeftTotalForce4","LeftTotalForce5"),
variable.name="Step",
value.name="Force"
)
#aggregate then describe then use breaks on the graph
sapply(alldataleft110,describe,na.rm=TRUE)#Good info could also be lapply## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4
## vars 1 1 1 1
## n 71 71 71 71
## mean 811.7621 792.997 788.1965 808.3651
## sd 323.9938 311.894 310.2024 321.3114
## median 882.34 880.09 871.92 908.04
## trimmed 849.3319 837.6274 829.49 848.9663
## mad 169.6539 176.9187 169.2536 147.9931
## min 30.36 28.98 30 34.77
## max 1264.45 1172.48 1183.26 1226.16
## range 1234.09 1143.5 1153.26 1191.39
## skew -1.09525 -1.239815 -1.19594 -1.1622
## kurtosis 0.2909189 0.3830478 0.3432578 0.1969641
## se 38.45099 37.01501 36.81426 38.13265
## LeftTotalForce5
## vars 1
## n 71
## mean 790.7163
## sd 307.7261
## median 879.06
## trimmed 832.2207
## mad 166.407
## min 29.37
## max 1178.1
## range 1148.73
## skew -1.204518
## kurtosis 0.3250981
## se 36.52037sapply(alldataleft110,max,na.rm=TRUE)#find maxes for each (should be first peak)## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4 LeftTotalForce5
## 1264.45 1172.48 1183.26 1226.16 1178.10#---
#create a variable with all the total force data
alldataleft120<- qpcR:::cbind.na(spm120l1[6],spm120l2[6],spm120l3[6],spm120l4[6],spm120l5[6])
head(alldataleft120)aveforces120<-apply(alldataleft120,2,mean,na.rm=TRUE)#Base of data frame
head(aveforces120)## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4 LeftTotalForce5
## 774.8794 749.9098 779.9093 777.5176 753.0646#reshape
lforcetable120 <- melt(aveforces120,
measure.vars=c("LeftTotalForce1","LeftTotalForce2","LeftTotalForce3",
"LeftTotalForce4","LeftTotalForce5"),
variable.name="Step",
value.name="Force"
)
#aggregate then describe then use breaks on teh graph
sapply(alldataleft120,describe,na.rm=TRUE)#Good info could also be lapply## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4
## vars 1 1 1 1
## n 67 65 67 68
## mean 774.8794 749.9098 779.9093 777.5176
## sd 312.5489 305.7865 310.8621 321.3536
## median 855.1 850.42 858.69 872.275
## trimmed 811.4525 789.8087 816.9696 813.662
## mad 197.2451 167.7117 194.2206 153.9013
## min 36 23.52 33.96 22.96
## max 1159.68 1108.98 1168.12 1180.8
## range 1123.68 1085.46 1134.16 1157.84
## skew -1.14848 -1.209867 -1.171235 -1.192992
## kurtosis 0.1531566 0.2195751 0.2250702 0.2282887
## se 38.18392 37.92814 37.97785 38.96984
## LeftTotalForce5
## vars 1
## n 70
## mean 753.0646
## sd 343.5593
## median 874.69
## trimmed 792.915
## mad 187.245
## min 24.94
## max 1175.64
## range 1150.7
## skew -1.02514
## kurtosis -0.3589164
## se 41.06319sapply(alldataleft120,max,na.rm=TRUE)#find maxes for each (should be first peak)## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4 LeftTotalForce5
## 1159.68 1108.98 1168.12 1180.80 1175.64#-
#create a variable with all the total force data
alldataleft130<- qpcR:::cbind.na(spm130l1[6],spm130l2[6],spm130l3[6],spm130l4[6],spm130l5[6])
head(alldataleft130)aveforces130<-apply(alldataleft130,2,mean,na.rm=TRUE)#Base of data frame
head(aveforces130)## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4 LeftTotalForce5
## 729.2513 735.2509 740.9426 759.4850 784.1110#reshape
lforcetable130 <- melt(aveforces130,
measure.vars=c("LeftTotalForce1","LeftTotalForce2","LeftTotalForce3",
"LeftTotalForce4","LeftTotalForce5"),
variable.name="Step",
value.name="Force"
)
#aggregate then describe then use breaks on the graph
sapply(alldataleft130,describe,na.rm=TRUE)#Good info could also be lapply## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4
## vars 1 1 1 1
## n 62 64 65 64
## mean 729.2513 735.2509 740.9426 759.485
## sd 323.4696 324.3103 325.7353 323.1088
## median 834.15 866.065 853.3 878.51
## trimmed 764.0992 777.3606 778.3136 801.6044
## mad 192.649 147.7337 196.4445 129.9351
## min 20.58 25 22.14 21.06
## max 1147.7 1093.07 1123.06 1130.78
## range 1127.12 1068.07 1100.92 1109.72
## skew -0.9792441 -1.151426 -1.062635 -1.208204
## kurtosis -0.3047226 -0.1488493 -0.2039797 0.0974081
## se 41.08068 40.53879 40.40249 40.3886
## LeftTotalForce5
## vars 1
## n 61
## mean 784.111
## sd 308.4293
## median 886.5
## trimmed 831.1943
## mad 124.2715
## min 30.88
## max 1138.66
## range 1107.78
## skew -1.34792
## kurtosis 0.6182807
## se 39.49032sapply(alldataleft130,max,na.rm=TRUE)#find maxes for each (should be first peak)## LeftTotalForce1 LeftTotalForce2 LeftTotalForce3 LeftTotalForce4 LeftTotalForce5
## 1147.70 1093.07 1123.06 1130.78 1138.66#----
#create a variable with all the total force data
alldataright100<- qpcR:::cbind.na(spm100r1[6],spm100r2[6],spm100r3[6],spm100r4[6],spm100r5[6])
head(alldataright100)aveforcesr100<-apply(alldataright100,2,mean,na.rm=TRUE)#Base of data frame
head(aveforcesr100)## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## 753.6280 797.2289 771.6980 820.4609
## RightTotalForce5
## 831.9099#reshape
rforcetable100 <- melt(aveforcesr100,
measure.vars=c("RightTotalForce1","RightTotalForce2","RightTotalForce3",
"RightTotalForce4","RightTotalForce5"),
variable.name="Step",
value.name="Force"
)
#aggregate then describe then use breaks on the graph
sapply(alldataright100,describe,na.rm=TRUE)#Good info could also be lapply## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## vars 1 1 1 1
## n 82 82 85 79
## mean 753.628 797.2289 771.698 820.4609
## sd 335.8591 350.2264 371.0091 322.3425
## median 896.37 916.5 922.54 929.74
## trimmed 805.1776 848.205 818.0974 869.3714
## mad 124.8423 203.2645 192.1746 140.9804
## min 4.08 21.89 21.12 24
## max 1078.35 1159.78 1141.56 1152.21
## range 1074.27 1137.89 1120.44 1128.21
## skew -1.270864 -1.20321 -1.111472 -1.453367
## kurtosis 0.01285476 0.01716406 -0.3670893 0.7209503
## se 37.08943 38.67603 40.24159 36.26637
## RightTotalForce5
## vars 1
## n 78
## mean 831.9099
## sd 318.3615
## median 933.24
## trimmed 878.0252
## mad 141.529
## min 29.51
## max 1198.92
## range 1169.41
## skew -1.407374
## kurtosis 0.7532469
## se 36.04734sapply(alldataright100,max,na.rm=TRUE)#find maxes for each (should be first peak)## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## 1078.35 1159.78 1141.56 1152.21
## RightTotalForce5
## 1198.92#------
#create a variable with all the total force data
alldataright110<- qpcR:::cbind.na(spm110r1[6],spm110r2[6],spm110r3[6],spm110r4[6],spm110r5[6])
head(alldataright110)aveforcesr110<-apply(alldataright110,2,mean,na.rm=TRUE)#Base of data frame
head(aveforcesr110)## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## 759.9618 784.6687 776.9873 751.9101
## RightTotalForce5
## 766.1709#reshape
rforcetable110 <- melt(aveforcesr110,
measure.vars=c("RightTotalForce1","RightTotalForce2","RightTotalForce3",
"RightTotalForce4","RightTotalForce5"),
variable.name="Step",
value.name="Force"
)
#aggregate then describe then use breaks on the graph
sapply(alldataright110,describe,na.rm=TRUE)#Good info could also be lapply## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## vars 1 1 1 1
## n 76 75 73 78
## mean 759.9618 784.6687 776.9873 751.9101
## sd 359.1293 323.1705 303.1072 315.0184
## median 895.785 906.52 873.61 873.4
## trimmed 803.4366 836.3689 827.8414 799.4944
## mad 207.3564 165.3989 149.2385 149.6018
## min 22.26 25.62 25.47 20
## max 1124.01 1084.74 1074.6 1030.05
## range 1101.75 1059.12 1049.13 1010.05
## skew -1.092674 -1.344556 -1.412058 -1.336916
## kurtosis -0.3749179 0.3252876 0.5999207 0.2739863
## se 41.19496 37.31652 35.47602 35.66882
## RightTotalForce5
## vars 1
## n 76
## mean 766.1709
## sd 323.2115
## median 878.52
## trimmed 813.1329
## mad 179.0091
## min 21.9
## max 1084.66
## range 1062.76
## skew -1.263917
## kurtosis 0.1532133
## se 37.0749sapply(alldataright110,max,na.rm=TRUE)#find maxes for each (should be first peak)## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## 1124.01 1084.74 1074.60 1030.05
## RightTotalForce5
## 1084.66#----
#create a variable with all the total force data
alldataright120<- qpcR:::cbind.na(spm120r1[6],spm120r2[6],spm120r3[6],spm120r4[6],spm120r5[6])
head(alldataright120)aveforcesr120<-apply(alldataright120,2,mean,na.rm=TRUE)#Base of data frame
head(aveforcesr120)## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## 694.7671 723.0069 743.9772 761.6057
## RightTotalForce5
## 738.8877#reshape
rforcetable120 <- melt(aveforcesr120, # our data
measure.vars=c("RightTotalForce1","RightTotalForce2","RightTotalForce3",
"RightTotalForce4","RightTotalForce5"), # The source columns (the columns we're combining)
variable.name="Step", # Name of the destination column that will identify the original IV
value.name="Force" # column where the measurement values will be located DV
)
#aggregate then describe then use breaks on the graph
sapply(alldataright120,describe,na.rm=TRUE)#Good info could also be lapply## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## vars 1 1 1 1
## n 72 71 71 70
## mean 694.7671 723.0069 743.9772 761.6057
## sd 337.8474 329.2689 310.4402 314.1729
## median 818.675 834.33 850.65 857.5
## trimmed 735.0359 767.287 794.4049 811.6521
## mad 226.8749 205.8738 177.8379 208.9354
## min 20 24.48 22.33 23.73
## max 1039.35 1069.81 1031.7 1070.46
## range 1019.35 1045.33 1009.37 1046.73
## skew -0.971744 -1.089933 -1.278608 -1.223399
## kurtosis -0.5749888 -0.2875661 0.2009041 0.1776556
## se 39.8157 39.07703 36.84247 37.55084
## RightTotalForce5
## vars 1
## n 70
## mean 738.8877
## sd 334.5904
## median 848.85
## trimmed 786.845
## mad 220.4849
## min 20.32
## max 1067.4
## range 1047.08
## skew -1.112961
## kurtosis -0.2484853
## se 39.9912sapply(alldataright120,max,na.rm=TRUE)#find maxes for each (should be first peak)## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## 1039.35 1069.81 1031.70 1070.46
## RightTotalForce5
## 1067.40#-----
#create a variable with all the total force data
alldataright130<- qpcR:::cbind.na(spm130r1[6],spm130r2[6],spm130r3[6],spm130r4[6],spm130r5[6])
head(alldataright130)aveforcesr130<-apply(alldataright130,2,mean,na.rm=TRUE)#Base of data frame
head(aveforcesr130)## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## 660.9980 718.0684 705.4901 692.1166
## RightTotalForce5
## 693.5103#reshape
rforcetable130 <- melt(aveforcesr130, # our data
measure.vars=c("RightTotalForce1","RightTotalForce2","RightTotalForce3",
"RightTotalForce4","RightTotalForce5"),
variable.name="Step",
value.name="Force"
)
#aggregate then describe then use breaks on the graph
sapply(alldataright130,describe,na.rm=TRUE)#Good info could also be lapply## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## vars 1 1 1 1
## n 71 69 67 70
## mean 660.998 718.0684 705.4901 692.1166
## sd 366.831 361.7734 347.742 356.4685
## median 811.44 863.27 842.24 832.245
## trimmed 695.6035 752.7518 743.1965 727.8366
## mad 249.4326 210.3216 203.472 192.5156
## min 11.97 7.36 17.01 7.36
## max 1029.6 1108.93 1050.92 1103.49
## range 1017.63 1101.57 1033.91 1096.13
## skew -0.8204367 -0.9614745 -1.008916 -0.9019037
## kurtosis -1.016178 -0.6698267 -0.5722025 -0.7467457
## se 43.53483 43.5524 42.48345 42.60614
## RightTotalForce5
## vars 1
## n 66
## mean 693.5103
## sd 352.6463
## median 822.43
## trimmed 727.4359
## mad 244.0953
## min 14.48
## max 1077.12
## range 1062.64
## skew -0.9225121
## kurtosis -0.7008526
## se 43.40776sapply(alldataright130,max,na.rm=TRUE)#find maxes for each (should be first peak)## RightTotalForce1 RightTotalForce2 RightTotalForce3 RightTotalForce4
## 1029.60 1108.93 1050.92 1103.49
## RightTotalForce5
## 1077.12Combine all cadences
I combined all the cadences for each leg first. Then I created factored columns for each of the limbs and an ordered column for cadence. Combine all those into dataframes with cadence, force, and limb, then make one master data frame that has both limbs.
#combine all the spm's
combinedleftforces<-rbind(lforcetable100,lforcetable110,lforcetable120,lforcetable130)
combinedrightforces<-rbind(rforcetable100,rforcetable110,rforcetable120,rforcetable130)
#create factored limb column
limb<- factor(rep("L",20))
head(limb)## [1] L L L L L L
## Levels: Lrlimb<-factor(rep("R",20))
head(rlimb)## [1] R R R R R R
## Levels: R#create Factored cadence column
tempseq<-seq(100,130,10)#A sequence of 100 to 130 by 10
dupseq<-rep(tempseq,each=5)
Cadence<-ordered(dupseq) #(2 points-Ordering Data)
head(Cadence)## [1] 100 100 100 100 100 110
## Levels: 100 < 110 < 120 < 130levels(Cadence)## [1] "100" "110" "120" "130"#combine forces, cadence, and limb
combinedleftforcesprime<-cbind(Cadence,combinedleftforces,limb)
str(combinedleftforcesprime,20)## 'data.frame': 20 obs. of 3 variables:
## $ Cadence: Ord.factor w/ 4 levels "100"<"110"<"120"<..: 1 1 1 1 1 2 2 2 2 2 ...
## $ Force : num 814 838 838 835 805 ...
## $ limb : Factor w/ 1 level "L": 1 1 1 1 1 1 1 1 1 1 ...head(combinedleftforcesprime)combinedrightforcesprime<- cbind(Cadence,combinedrightforces,rlimb)
names(combinedrightforcesprime)<- c("Cadence","Force","limb")
head(combinedrightforcesprime)#lastly combine both legs
bothlegforces<-rbind(combinedleftforcesprime,combinedrightforcesprime)
head(bothlegforces)str(bothlegforces)## 'data.frame': 40 obs. of 3 variables:
## $ Cadence: Ord.factor w/ 4 levels "100"<"110"<"120"<..: 1 1 1 1 1 2 2 2 2 2 ...
## $ Force : num 814 838 838 835 805 ...
## $ limb : Factor w/ 2 levels "L","R": 1 1 1 1 1 1 1 1 1 1 ...Make more relevant graphs
In this section I made lots of other graphs as well as made some new variables to analyze the different groups. I made barplots for mean and max force at each cadence by leg. Violin plots for each cadence and limb, scatter plots, and a boxplot.
#This graph is meant to compare avg left forces at each cadence
cadencemeansl<-tapply(combinedleftforcesprime$Force,combinedleftforcesprime$Cadence,mean)
head(cadencemeansl)## 100 110 120 130
## 826.0072 798.4074 767.0561 749.8082coul <- brewer.pal(5, "Set2") #(2 Points- Rcolorbrewer)
lcadencebar<-barplot(cadencemeansl,beside = TRUE,
xlab = "Cadence",ylab = "Force (N)",
main = "Average Force by Cadence (Left)",
col=coul,ylim = c(0,1000))
#This graph is meant to compare avg right forces at each cadence
cadencemeansr<-tapply(combinedrightforcesprime$Force,combinedrightforcesprime$Cadence,mean)
head(cadencemeansr)## 100 110 120 130
## 794.9851 767.9398 732.4489 694.0367coull <- brewer.pal(5, "Set1")
rcadencebar<-barplot(cadencemeansr,beside = TRUE,
xlab = "Cadence",ylab = "Force (N)",
main = "Average Force by Cadence (Right)",
col=coull,ylim = c(0,1000))
#This graph is meant to compare average force by limb
limbmean<-tapply(bothlegforces$Force,bothlegforces$limb,mean)
head(limbmean)## L R
## 785.3197 747.3526limbmeanbar<-barplot(limbmean,beside=TRUE,
xlab="Limb",ylab="Force (N)",
main= "Average Force by Limb",
col=coul, ylim = c(0,1000))
#max average force for each limb
limbmax<-aggregate(Force~limb,data = bothlegforces,mean)
limbmax#max average force for each cadence avg of both legs
cadencemaxbothleg<-aggregate(Force~Cadence,data = bothlegforces,max)
cadencemaxbothleg# (Cell) Means of both Cadence and limb
cellavg<-tapply(bothlegforces$Force, list(bothlegforces$Cadence,bothlegforces$limb), mean)
head(cellavg)## L R
## 100 826.0072 794.9851
## 110 798.4074 767.9398
## 120 767.0561 732.4489
## 130 749.8082 694.0367# (Cell) Maxes of both Cadence and limb
cellmax<-tapply(bothlegforces$Force, list(bothlegforces$Cadence,bothlegforces$limb), max)
head(cellmax)## L R
## 100 838.2516 831.9099
## 110 811.7621 784.6687
## 120 779.9093 761.6057
## 130 784.1110 718.0684#check for skewness of individual variables (1 Point-Describe)
describe(bothlegforces)#Violin plot of Force across both cadence and limb groups #(5 Points-Violin Plot)
ggplot(data = bothlegforces) + geom_violin(aes(x = Cadence, y = Force, fill = limb))+
ggtitle("Violin Plot: Average Force (N) by Cadence and Limb")+
ylab("Force (N)")+
coord_cartesian(ylim=c(650,850))
#as separate plots
ggplot(data = bothlegforces) + geom_violin(aes(x = Cadence, y = Force, fill = limb))+
facet_grid(Cadence~limb) + xlab("Cadence")+ ylab("Force (N)") +
ggtitle("Violin Plot: Average Force (N) by Cadence and Limb")+
coord_cartesian(ylim=c(650,850))
# Scatterplot of cadence predicting force #(8 Points-Scatterplot)
cadencescatter <- ggplot(data = bothlegforces, aes(x = Cadence, y = Force)) + geom_point()+
xlab("Cadence")+ ylab("Force (N)") + ggtitle("Scatterplot: Average Force (N) by Cadence for Both Limbs")+
coord_cartesian(ylim=c(650,850))
cadencescatter
# loess curve (3 Points-Loess)
cadencescatter + geom_line(data=bothlegforces,aes(x=Cadence,y=Force))+geom_smooth(method = "loess")## `geom_smooth()` using formula = 'y ~ x'
#scatterplot of limb predicting force-----useless
limbscatter<- ggplot(data=bothlegforces,aes(x=limb,y=Force))+geom_line()
limbscatter
#Boxplot of cadence on both leg force (5 Points-Boxplot)
ggplot(bothlegforces)+ geom_boxplot(aes(x=Cadence,y=Force,fill=Cadence)) +
xlab("Cadence")+ ylab("Force (N)") + ggtitle("Average Force (N) by Cadence for Both Limbs")+
scale_fill_brewer(palette = "Dark2")+coord_cartesian(ylim=c(650,850))
ggplot(bothlegforces)+ geom_boxplot(aes(x=Cadence,y=Force,fill=limb)) +
xlab("Cadence")+ ylab("Force (N)") + ggtitle("Average Force (N) by Cadence for By Limb")+
scale_fill_brewer(palette = "Dark2")+coord_cartesian(ylim=c(650,850))
Stats
I ran a multiple regression with cadence and limb predicting force, allowing for the two to interact. I ran an anova and summary on that model to further examine it. I also plotted it’s effects. Then I checked for homogeneity and normality. Then I looked at some specific post hoc tests.
#multiple regression with cadence and limb on force (1 Point-General linear model)
allvariablelm<-lm(Force~Cadence*limb,data=bothlegforces)
Anova(allvariablelm,type = "III")summary(allvariablelm)##
## Call:
## lm(formula = Force ~ Cadence * limb, data = bothlegforces)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.357 -12.500 0.859 11.634 36.925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 785.320 4.596 170.858 < 2e-16 ***
## Cadence.L -58.126 9.193 -6.323 4.27e-07 ***
## Cadence.Q 5.176 9.193 0.563 0.577
## Cadence.C 3.992 9.193 0.434 0.667
## limbR -37.967 6.500 -5.841 1.72e-06 ***
## Cadence.L:limbR -17.528 13.000 -1.348 0.187
## Cadence.Q:limbR -10.859 13.000 -0.835 0.410
## Cadence.C:limbR -2.757 13.000 -0.212 0.833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.56 on 32 degrees of freedom
## Multiple R-squared: 0.8169, Adjusted R-squared: 0.7768
## F-statistic: 20.39 on 7 and 32 DF, p-value: 3.978e-10#Effects plot of cadence and limb force w/conf int
plot(allEffects(allvariablelm),#(3 Points-Effects package)
lines=list(multiline=TRUE),
confint=list(style = "bars"),ylim=c(650,850))
#look at heterogeneity of variance
plot(allvariablelm,which=1)
#look at normality of residuals
plot(allvariablelm,which=2)
#specific contrasts post hoc in cadence
pairs(emmeans(allvariablelm,~Cadence))## NOTE: Results may be misleading due to involvement in interactions## contrast estimate SE df t.ratio p.value
## Cadence100 - Cadence110 27.3 9.19 32 2.972 0.0272
## Cadence100 - Cadence120 60.7 9.19 32 6.608 <.0001
## Cadence100 - Cadence130 88.6 9.19 32 9.635 <.0001
## Cadence110 - Cadence120 33.4 9.19 32 3.636 0.0051
## Cadence110 - Cadence130 61.3 9.19 32 6.663 <.0001
## Cadence120 - Cadence130 27.8 9.19 32 3.027 0.0238
##
## Results are averaged over the levels of: limb
## P value adjustment: tukey method for comparing a family of 4 estimates#limb
pairs(emmeans(allvariablelm,~limb))## NOTE: Results may be misleading due to involvement in interactions## contrast estimate SE df t.ratio p.value
## L - R 38 6.5 32 5.841 <.0001
##
## Results are averaged over the levels of: Cadence#both
pairs(emmeans(allvariablelm,~Cadence*limb))#too much## contrast estimate SE df t.ratio p.value
## Cadence100 L - Cadence110 L 27.600 13 32 2.123 0.4226
## Cadence100 L - Cadence120 L 58.951 13 32 4.535 0.0018
## Cadence100 L - Cadence130 L 76.199 13 32 5.861 <.0001
## Cadence100 L - Cadence100 R 31.022 13 32 2.386 0.2819
## Cadence100 L - Cadence110 R 58.067 13 32 4.467 0.0021
## Cadence100 L - Cadence120 R 93.558 13 32 7.197 <.0001
## Cadence100 L - Cadence130 R 131.971 13 32 10.151 <.0001
## Cadence110 L - Cadence120 L 31.351 13 32 2.412 0.2701
## Cadence110 L - Cadence130 L 48.599 13 32 3.738 0.0148
## Cadence110 L - Cadence100 R 3.422 13 32 0.263 1.0000
## Cadence110 L - Cadence110 R 30.468 13 32 2.344 0.3025
## Cadence110 L - Cadence120 R 65.958 13 32 5.074 0.0004
## Cadence110 L - Cadence130 R 104.371 13 32 8.028 <.0001
## Cadence120 L - Cadence130 L 17.248 13 32 1.327 0.8818
## Cadence120 L - Cadence100 R -27.929 13 32 -2.148 0.4077
## Cadence120 L - Cadence110 R -0.884 13 32 -0.068 1.0000
## Cadence120 L - Cadence120 R 34.607 13 32 2.662 0.1716
## Cadence120 L - Cadence130 R 73.019 13 32 5.617 0.0001
## Cadence130 L - Cadence100 R -45.177 13 32 -3.475 0.0285
## Cadence130 L - Cadence110 R -18.132 13 32 -1.395 0.8530
## Cadence130 L - Cadence120 R 17.359 13 32 1.335 0.8784
## Cadence130 L - Cadence130 R 55.771 13 32 4.290 0.0034
## Cadence100 R - Cadence110 R 27.045 13 32 2.080 0.4480
## Cadence100 R - Cadence120 R 62.536 13 32 4.810 0.0008
## Cadence100 R - Cadence130 R 100.948 13 32 7.765 <.0001
## Cadence110 R - Cadence120 R 35.491 13 32 2.730 0.1503
## Cadence110 R - Cadence130 R 73.903 13 32 5.685 0.0001
## Cadence120 R - Cadence130 R 38.412 13 32 2.955 0.0945
##
## P value adjustment: tukey method for comparing a family of 8 estimatesSummary
Through looking at our graphs and tables, there is quite a lot going on in the data. The data showed a statistically significant difference between all cadence groups. There was a significant difference between limbs overall, but only at 130bpm controlling for cadence, though my right limb was always underloaded compared to my left. The most important finding of my preliminary search is that force decreases with increasing cadence at the same speed, with the difference notably increasing at 130bpm. In the literature when adults in my target population try to increase their speed, they increase their cadence up to 130, but I’m curious now if there may be changes beyond this. This will support my further research as I will be investigating these cadences over several gait speeds to evaluate changes in knee joint loading.