Practical Machine Learning Project: Human Activity Recognition(Weight Lifting Exercise)
Someshwar Rao Sattiraju
September 20, 2017
Script was executed using RStudio on Windows OS
Background:
Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website here: http://groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset).
Refrence for the paper: The data for this project come from this source: http://groupware.les.inf.puc-rio.br/har.
Data
The training data for this project are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv
The test data are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv
About Human Activity Recognition
The human activity recognition research is done to determine different activities being performed( predict which activity was performed at a specific point in time), in this case it was a weight lifting exercise data set.
Read more: http://groupware.les.inf.puc-rio.br/har#ixzz4sQMF9HSB
Reproducability
Classification and model generation was done using a random number generator seed with value of 1223. In order to reproduce the result the same needs to be used. We will need packages such as RandomForest, caret, rpart, rpartplot for modeling the dataset.
About How The Model Was Built
Six young health participants were asked to perform one set of 10 repetitions of the Unilateral Dumbbell Biceps Curl in five different fashions: exactly according to the specification (Class A), throwing the elbows to the front (Class B), lifting the dumbbell only halfway (Class C), lowering the dumbbell only halfway (Class D) and throwing the hips to the front (Class E).
Essentially Class A pertains to actual exercise to be performed and rest of the classes - Class B to Class E are wrong ways of doing the exercise or mistakes.
The problem is a classification one and we need to fit a classification model, best model with least error and high accuracy has to be choosen, I chooe 3 methods - Recursive & Generalized Classification Trees, Random Forest and K- Nearest Neighbours to model this dataset.
Performing Analysis Of The Data Set
Loading Training and Testing data sets and elemenating Null values
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, lastset.seed(1223)
#The data set seems to have Missing values, NA, Div/0 error etc., the same needs to be replaced with NA using na.strings
training<-read.csv("S:/Software/R/Data Science Specialization/MACHINE LEARNING/Week4/pml-training.csv",na.strings=c("NA","#DIV/0!",""))
testing<-read.csv("S:/Software/R/Data Science Specialization/MACHINE LEARNING/Week4/pml-testing.csv",na.strings=c("NA","#DIV/0!",""))
dim(training)## [1] 19622 160dim(testing)## [1] 20 160Plot Of Frequency Of Classe
library(ggplot2)
ggplot(data=training,aes(classe))+geom_bar(aes(fill=classe))
We find that the columns 1 to 7 are irrelavant for analysis and hence the same is removed from the data set also we would like to remove those columns which sum to 0 or have only 0’s.
We subselect the data and devide the same to training and test sets using createDataPartition
This is done by looking at the data and summary of the data ( Attached in Appendix ).
library(caret)## Loading required package: latticetraining<- training[ ,colSums(is.na(training)) == 0]
testing<- testing[ ,colSums(is.na(testing)) == 0]
training<-training[,-c(1:7)]
testing<-testing[,-c(1:7)]
dim(training)## [1] 19622 53dim(testing)## [1] 20 53Cross Validation And Fitting Classification Tree Using ‘rpart’
Cross validation has been done using createDataPartition funcion to divide the data ( 75% for training and modeling purposes and 25% to testing), multiple models will be fit and the best one which has the most accuracy will be choosen. With the choosen model the test set will be validated.
library(rpart)
library(rpart.plot)
inTrain<-createDataPartition(y=training$classe, p=0.75, list=FALSE)
trainset<-training[inTrain,]
testset<-training[-inTrain, ]
mod1<-rpart(classe~., data=trainset, method="class")
prediction1<-predict(mod1, testset, type="class")
rpart.plot(mod1, main="Classification Tree", extra=102, under=TRUE,faclen=0)
confusionMatrix(prediction1, testset$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1251 145 20 40 20
## B 60 509 33 58 59
## C 37 113 732 118 98
## D 22 76 55 530 52
## E 25 106 15 58 672
##
## Overall Statistics
##
## Accuracy : 0.7533
## 95% CI : (0.7409, 0.7653)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6875
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.8968 0.5364 0.8561 0.6592 0.7458
## Specificity 0.9359 0.9469 0.9096 0.9500 0.9490
## Pos Pred Value 0.8476 0.7079 0.6667 0.7211 0.7671
## Neg Pred Value 0.9580 0.8949 0.9677 0.9343 0.9431
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2551 0.1038 0.1493 0.1081 0.1370
## Detection Prevalence 0.3010 0.1466 0.2239 0.1499 0.1786
## Balanced Accuracy 0.9163 0.7416 0.8829 0.8046 0.8474Random Forest Model:
library(randomForest)## randomForest 4.6-12## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
##
## combinelibrary(rattle)## Rattle: A free graphical interface for data mining with R.
## Version 4.1.0 Copyright (c) 2006-2015 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.mod2 <- randomForest(classe ~. , data= trainset, method="class")
prediction2 <- predict(mod2, testset, type="class")
confusionMatrix(prediction2, testset$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1395 3 0 0 0
## B 0 945 7 0 0
## C 0 1 848 9 0
## D 0 0 0 794 5
## E 0 0 0 1 896
##
## Overall Statistics
##
## Accuracy : 0.9947
## 95% CI : (0.9922, 0.9965)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9933
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 0.9958 0.9918 0.9876 0.9945
## Specificity 0.9991 0.9982 0.9975 0.9988 0.9998
## Pos Pred Value 0.9979 0.9926 0.9883 0.9937 0.9989
## Neg Pred Value 1.0000 0.9990 0.9983 0.9976 0.9988
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2845 0.1927 0.1729 0.1619 0.1827
## Detection Prevalence 0.2851 0.1941 0.1750 0.1629 0.1829
## Balanced Accuracy 0.9996 0.9970 0.9947 0.9932 0.9971K-Nearest Neighbours Model:
mod3 <- train(classe ~., data= trainset, method="knn")
prediction3 <- predict(mod3, testset)
confusionMatrix(prediction3, testset$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1336 49 10 17 12
## B 14 831 22 7 30
## C 19 25 791 45 20
## D 23 22 22 722 32
## E 3 22 10 13 807
##
## Overall Statistics
##
## Accuracy : 0.915
## 95% CI : (0.9068, 0.9226)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8924
## Mcnemar's Test P-Value : 1.351e-08
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9577 0.8757 0.9251 0.8980 0.8957
## Specificity 0.9749 0.9815 0.9731 0.9759 0.9880
## Pos Pred Value 0.9382 0.9192 0.8789 0.8794 0.9439
## Neg Pred Value 0.9830 0.9705 0.9840 0.9799 0.9768
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2724 0.1695 0.1613 0.1472 0.1646
## Detection Prevalence 0.2904 0.1843 0.1835 0.1674 0.1743
## Balanced Accuracy 0.9663 0.9286 0.9491 0.9369 0.9418Conclusion
Analysis was performed usign 3 models: Generalized Classification Trees, Random Forest and K-Means Clustering. Based on the result we find that Random Forest Model stands out to be the most accurate model to make the prediction with prediction accuracy of 99.47% and Confidence Interval of (99.22%, 99.65%).
Model Results:
Genralized Classification Tree using ‘rpart’ method Accuracy: 75.33% 95% CI: (0.7409, 0.7653)
Random Forest Model using RandomForest Accuracy: 99.47% 95% CI: (0.9922, 0.9965)
K-Means Clustering using “knn” method Accuracy: 91.50% 95% CI: (0.9068, 0.9226)
Test Data Set & Generation OF File
predictionfinal<-predict(mod2, testing, type="class")
write.csv(predictionfinal,file = "S:/Software/R/Data Science Specialization/MACHINE LEARNING/Week4/TestData.csv",row.names=FALSE)Appendix
trn<-read.csv("S:/Software/R/Data Science Specialization/MACHINE LEARNING/Week4/pml-training.csv",na.strings=c("NA","#DIV/0!",""))
summary(trn)## X user_name raw_timestamp_part_1 raw_timestamp_part_2
## Min. : 1 adelmo :3892 Min. :1.322e+09 Min. : 294
## 1st Qu.: 4906 carlitos:3112 1st Qu.:1.323e+09 1st Qu.:252912
## Median : 9812 charles :3536 Median :1.323e+09 Median :496380
## Mean : 9812 eurico :3070 Mean :1.323e+09 Mean :500656
## 3rd Qu.:14717 jeremy :3402 3rd Qu.:1.323e+09 3rd Qu.:751891
## Max. :19622 pedro :2610 Max. :1.323e+09 Max. :998801
##
## cvtd_timestamp new_window num_window roll_belt
## 28/11/2011 14:14: 1498 no :19216 Min. : 1.0 Min. :-28.90
## 05/12/2011 11:24: 1497 yes: 406 1st Qu.:222.0 1st Qu.: 1.10
## 30/11/2011 17:11: 1440 Median :424.0 Median :113.00
## 05/12/2011 11:25: 1425 Mean :430.6 Mean : 64.41
## 02/12/2011 14:57: 1380 3rd Qu.:644.0 3rd Qu.:123.00
## 02/12/2011 13:34: 1375 Max. :864.0 Max. :162.00
## (Other) :11007
## pitch_belt yaw_belt total_accel_belt kurtosis_roll_belt
## Min. :-55.8000 Min. :-180.00 Min. : 0.00 Min. :-2.121
## 1st Qu.: 1.7600 1st Qu.: -88.30 1st Qu.: 3.00 1st Qu.:-1.329
## Median : 5.2800 Median : -13.00 Median :17.00 Median :-0.899
## Mean : 0.3053 Mean : -11.21 Mean :11.31 Mean :-0.220
## 3rd Qu.: 14.9000 3rd Qu.: 12.90 3rd Qu.:18.00 3rd Qu.:-0.219
## Max. : 60.3000 Max. : 179.00 Max. :29.00 Max. :33.000
## NA's :19226
## kurtosis_picth_belt kurtosis_yaw_belt skewness_roll_belt
## Min. :-2.190 Mode:logical Min. :-5.745
## 1st Qu.:-1.107 NA's:19622 1st Qu.:-0.444
## Median :-0.151 Median : 0.000
## Mean : 4.334 Mean :-0.026
## 3rd Qu.: 3.178 3rd Qu.: 0.417
## Max. :58.000 Max. : 3.595
## NA's :19248 NA's :19225
## skewness_roll_belt.1 skewness_yaw_belt max_roll_belt max_picth_belt
## Min. :-7.616 Mode:logical Min. :-94.300 Min. : 3.00
## 1st Qu.:-1.114 NA's:19622 1st Qu.:-88.000 1st Qu.: 5.00
## Median :-0.068 Median : -5.100 Median :18.00
## Mean :-0.296 Mean : -6.667 Mean :12.92
## 3rd Qu.: 0.661 3rd Qu.: 18.500 3rd Qu.:19.00
## Max. : 7.348 Max. :180.000 Max. :30.00
## NA's :19248 NA's :19216 NA's :19216
## max_yaw_belt min_roll_belt min_pitch_belt min_yaw_belt
## Min. :-2.10 Min. :-180.00 Min. : 0.00 Min. :-2.10
## 1st Qu.:-1.30 1st Qu.: -88.40 1st Qu.: 3.00 1st Qu.:-1.30
## Median :-0.90 Median : -7.85 Median :16.00 Median :-0.90
## Mean :-0.22 Mean : -10.44 Mean :10.76 Mean :-0.22
## 3rd Qu.:-0.20 3rd Qu.: 9.05 3rd Qu.:17.00 3rd Qu.:-0.20
## Max. :33.00 Max. : 173.00 Max. :23.00 Max. :33.00
## NA's :19226 NA's :19216 NA's :19216 NA's :19226
## amplitude_roll_belt amplitude_pitch_belt amplitude_yaw_belt
## Min. : 0.000 Min. : 0.000 Min. :0
## 1st Qu.: 0.300 1st Qu.: 1.000 1st Qu.:0
## Median : 1.000 Median : 1.000 Median :0
## Mean : 3.769 Mean : 2.167 Mean :0
## 3rd Qu.: 2.083 3rd Qu.: 2.000 3rd Qu.:0
## Max. :360.000 Max. :12.000 Max. :0
## NA's :19216 NA's :19216 NA's :19226
## var_total_accel_belt avg_roll_belt stddev_roll_belt var_roll_belt
## Min. : 0.000 Min. :-27.40 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.100 1st Qu.: 1.10 1st Qu.: 0.200 1st Qu.: 0.000
## Median : 0.200 Median :116.35 Median : 0.400 Median : 0.100
## Mean : 0.926 Mean : 68.06 Mean : 1.337 Mean : 7.699
## 3rd Qu.: 0.300 3rd Qu.:123.38 3rd Qu.: 0.700 3rd Qu.: 0.500
## Max. :16.500 Max. :157.40 Max. :14.200 Max. :200.700
## NA's :19216 NA's :19216 NA's :19216 NA's :19216
## avg_pitch_belt stddev_pitch_belt var_pitch_belt avg_yaw_belt
## Min. :-51.400 Min. :0.000 Min. : 0.000 Min. :-138.300
## 1st Qu.: 2.025 1st Qu.:0.200 1st Qu.: 0.000 1st Qu.: -88.175
## Median : 5.200 Median :0.400 Median : 0.100 Median : -6.550
## Mean : 0.520 Mean :0.603 Mean : 0.766 Mean : -8.831
## 3rd Qu.: 15.775 3rd Qu.:0.700 3rd Qu.: 0.500 3rd Qu.: 14.125
## Max. : 59.700 Max. :4.000 Max. :16.200 Max. : 173.500
## NA's :19216 NA's :19216 NA's :19216 NA's :19216
## stddev_yaw_belt var_yaw_belt gyros_belt_x
## Min. : 0.000 Min. : 0.000 Min. :-1.040000
## 1st Qu.: 0.100 1st Qu.: 0.010 1st Qu.:-0.030000
## Median : 0.300 Median : 0.090 Median : 0.030000
## Mean : 1.341 Mean : 107.487 Mean :-0.005592
## 3rd Qu.: 0.700 3rd Qu.: 0.475 3rd Qu.: 0.110000
## Max. :176.600 Max. :31183.240 Max. : 2.220000
## NA's :19216 NA's :19216
## gyros_belt_y gyros_belt_z accel_belt_x accel_belt_y
## Min. :-0.64000 Min. :-1.4600 Min. :-120.000 Min. :-69.00
## 1st Qu.: 0.00000 1st Qu.:-0.2000 1st Qu.: -21.000 1st Qu.: 3.00
## Median : 0.02000 Median :-0.1000 Median : -15.000 Median : 35.00
## Mean : 0.03959 Mean :-0.1305 Mean : -5.595 Mean : 30.15
## 3rd Qu.: 0.11000 3rd Qu.:-0.0200 3rd Qu.: -5.000 3rd Qu.: 61.00
## Max. : 0.64000 Max. : 1.6200 Max. : 85.000 Max. :164.00
##
## accel_belt_z magnet_belt_x magnet_belt_y magnet_belt_z
## Min. :-275.00 Min. :-52.0 Min. :354.0 Min. :-623.0
## 1st Qu.:-162.00 1st Qu.: 9.0 1st Qu.:581.0 1st Qu.:-375.0
## Median :-152.00 Median : 35.0 Median :601.0 Median :-320.0
## Mean : -72.59 Mean : 55.6 Mean :593.7 Mean :-345.5
## 3rd Qu.: 27.00 3rd Qu.: 59.0 3rd Qu.:610.0 3rd Qu.:-306.0
## Max. : 105.00 Max. :485.0 Max. :673.0 Max. : 293.0
##
## roll_arm pitch_arm yaw_arm total_accel_arm
## Min. :-180.00 Min. :-88.800 Min. :-180.0000 Min. : 1.00
## 1st Qu.: -31.77 1st Qu.:-25.900 1st Qu.: -43.1000 1st Qu.:17.00
## Median : 0.00 Median : 0.000 Median : 0.0000 Median :27.00
## Mean : 17.83 Mean : -4.612 Mean : -0.6188 Mean :25.51
## 3rd Qu.: 77.30 3rd Qu.: 11.200 3rd Qu.: 45.8750 3rd Qu.:33.00
## Max. : 180.00 Max. : 88.500 Max. : 180.0000 Max. :66.00
##
## var_accel_arm avg_roll_arm stddev_roll_arm var_roll_arm
## Min. : 0.00 Min. :-166.67 Min. : 0.000 Min. : 0.000
## 1st Qu.: 9.03 1st Qu.: -38.37 1st Qu.: 1.376 1st Qu.: 1.898
## Median : 40.61 Median : 0.00 Median : 5.702 Median : 32.517
## Mean : 53.23 Mean : 12.68 Mean : 11.201 Mean : 417.264
## 3rd Qu.: 75.62 3rd Qu.: 76.33 3rd Qu.: 14.921 3rd Qu.: 222.647
## Max. :331.70 Max. : 163.33 Max. :161.964 Max. :26232.208
## NA's :19216 NA's :19216 NA's :19216 NA's :19216
## avg_pitch_arm stddev_pitch_arm var_pitch_arm avg_yaw_arm
## Min. :-81.773 Min. : 0.000 Min. : 0.000 Min. :-173.440
## 1st Qu.:-22.770 1st Qu.: 1.642 1st Qu.: 2.697 1st Qu.: -29.198
## Median : 0.000 Median : 8.133 Median : 66.146 Median : 0.000
## Mean : -4.901 Mean :10.383 Mean : 195.864 Mean : 2.359
## 3rd Qu.: 8.277 3rd Qu.:16.327 3rd Qu.: 266.576 3rd Qu.: 38.185
## Max. : 75.659 Max. :43.412 Max. :1884.565 Max. : 152.000
## NA's :19216 NA's :19216 NA's :19216 NA's :19216
## stddev_yaw_arm var_yaw_arm gyros_arm_x
## Min. : 0.000 Min. : 0.000 Min. :-6.37000
## 1st Qu.: 2.577 1st Qu.: 6.642 1st Qu.:-1.33000
## Median : 16.682 Median : 278.309 Median : 0.08000
## Mean : 22.270 Mean : 1055.933 Mean : 0.04277
## 3rd Qu.: 35.984 3rd Qu.: 1294.850 3rd Qu.: 1.57000
## Max. :177.044 Max. :31344.568 Max. : 4.87000
## NA's :19216 NA's :19216
## gyros_arm_y gyros_arm_z accel_arm_x accel_arm_y
## Min. :-3.4400 Min. :-2.3300 Min. :-404.00 Min. :-318.0
## 1st Qu.:-0.8000 1st Qu.:-0.0700 1st Qu.:-242.00 1st Qu.: -54.0
## Median :-0.2400 Median : 0.2300 Median : -44.00 Median : 14.0
## Mean :-0.2571 Mean : 0.2695 Mean : -60.24 Mean : 32.6
## 3rd Qu.: 0.1400 3rd Qu.: 0.7200 3rd Qu.: 84.00 3rd Qu.: 139.0
## Max. : 2.8400 Max. : 3.0200 Max. : 437.00 Max. : 308.0
##
## accel_arm_z magnet_arm_x magnet_arm_y magnet_arm_z
## Min. :-636.00 Min. :-584.0 Min. :-392.0 Min. :-597.0
## 1st Qu.:-143.00 1st Qu.:-300.0 1st Qu.: -9.0 1st Qu.: 131.2
## Median : -47.00 Median : 289.0 Median : 202.0 Median : 444.0
## Mean : -71.25 Mean : 191.7 Mean : 156.6 Mean : 306.5
## 3rd Qu.: 23.00 3rd Qu.: 637.0 3rd Qu.: 323.0 3rd Qu.: 545.0
## Max. : 292.00 Max. : 782.0 Max. : 583.0 Max. : 694.0
##
## kurtosis_roll_arm kurtosis_picth_arm kurtosis_yaw_arm skewness_roll_arm
## Min. :-1.809 Min. :-2.084 Min. :-2.103 Min. :-2.541
## 1st Qu.:-1.345 1st Qu.:-1.280 1st Qu.:-1.220 1st Qu.:-0.561
## Median :-0.894 Median :-1.010 Median :-0.733 Median : 0.040
## Mean :-0.366 Mean :-0.542 Mean : 0.406 Mean : 0.068
## 3rd Qu.:-0.038 3rd Qu.:-0.379 3rd Qu.: 0.115 3rd Qu.: 0.671
## Max. :21.456 Max. :19.751 Max. :56.000 Max. : 4.394
## NA's :19294 NA's :19296 NA's :19227 NA's :19293
## skewness_pitch_arm skewness_yaw_arm max_roll_arm max_picth_arm
## Min. :-4.565 Min. :-6.708 Min. :-73.100 Min. :-173.000
## 1st Qu.:-0.618 1st Qu.:-0.743 1st Qu.: -0.175 1st Qu.: -1.975
## Median :-0.035 Median :-0.133 Median : 4.950 Median : 23.250
## Mean :-0.065 Mean :-0.229 Mean : 11.236 Mean : 35.751
## 3rd Qu.: 0.454 3rd Qu.: 0.344 3rd Qu.: 26.775 3rd Qu.: 95.975
## Max. : 3.043 Max. : 7.483 Max. : 85.500 Max. : 180.000
## NA's :19296 NA's :19227 NA's :19216 NA's :19216
## max_yaw_arm min_roll_arm min_pitch_arm min_yaw_arm
## Min. : 4.00 Min. :-89.10 Min. :-180.00 Min. : 1.00
## 1st Qu.:29.00 1st Qu.:-41.98 1st Qu.: -72.62 1st Qu.: 8.00
## Median :34.00 Median :-22.45 Median : -33.85 Median :13.00
## Mean :35.46 Mean :-21.22 Mean : -33.92 Mean :14.66
## 3rd Qu.:41.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.:19.00
## Max. :65.00 Max. : 66.40 Max. : 152.00 Max. :38.00
## NA's :19216 NA's :19216 NA's :19216 NA's :19216
## amplitude_roll_arm amplitude_pitch_arm amplitude_yaw_arm
## Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 5.425 1st Qu.: 9.925 1st Qu.:13.00
## Median : 28.450 Median : 54.900 Median :22.00
## Mean : 32.452 Mean : 69.677 Mean :20.79
## 3rd Qu.: 50.960 3rd Qu.:115.175 3rd Qu.:28.75
## Max. :119.500 Max. :360.000 Max. :52.00
## NA's :19216 NA's :19216 NA's :19216
## roll_dumbbell pitch_dumbbell yaw_dumbbell
## Min. :-153.71 Min. :-149.59 Min. :-150.871
## 1st Qu.: -18.49 1st Qu.: -40.89 1st Qu.: -77.644
## Median : 48.17 Median : -20.96 Median : -3.324
## Mean : 23.84 Mean : -10.78 Mean : 1.674
## 3rd Qu.: 67.61 3rd Qu.: 17.50 3rd Qu.: 79.643
## Max. : 153.55 Max. : 149.40 Max. : 154.952
##
## kurtosis_roll_dumbbell kurtosis_picth_dumbbell kurtosis_yaw_dumbbell
## Min. :-2.174 Min. :-2.200 Mode:logical
## 1st Qu.:-0.682 1st Qu.:-0.721 NA's:19622
## Median :-0.033 Median :-0.133
## Mean : 0.452 Mean : 0.286
## 3rd Qu.: 0.940 3rd Qu.: 0.584
## Max. :54.998 Max. :55.628
## NA's :19221 NA's :19218
## skewness_roll_dumbbell skewness_pitch_dumbbell skewness_yaw_dumbbell
## Min. :-7.384 Min. :-7.447 Mode:logical
## 1st Qu.:-0.581 1st Qu.:-0.526 NA's:19622
## Median :-0.076 Median :-0.091
## Mean :-0.115 Mean :-0.035
## 3rd Qu.: 0.400 3rd Qu.: 0.505
## Max. : 1.958 Max. : 3.769
## NA's :19220 NA's :19217
## max_roll_dumbbell max_picth_dumbbell max_yaw_dumbbell min_roll_dumbbell
## Min. :-70.10 Min. :-112.90 Min. :-2.20 Min. :-149.60
## 1st Qu.:-27.15 1st Qu.: -66.70 1st Qu.:-0.70 1st Qu.: -59.67
## Median : 14.85 Median : 40.05 Median : 0.00 Median : -43.55
## Mean : 13.76 Mean : 32.75 Mean : 0.45 Mean : -41.24
## 3rd Qu.: 50.58 3rd Qu.: 133.22 3rd Qu.: 0.90 3rd Qu.: -25.20
## Max. :137.00 Max. : 155.00 Max. :55.00 Max. : 73.20
## NA's :19216 NA's :19216 NA's :19221 NA's :19216
## min_pitch_dumbbell min_yaw_dumbbell amplitude_roll_dumbbell
## Min. :-147.00 Min. :-2.20 Min. : 0.00
## 1st Qu.: -91.80 1st Qu.:-0.70 1st Qu.: 14.97
## Median : -66.15 Median : 0.00 Median : 35.05
## Mean : -33.18 Mean : 0.45 Mean : 55.00
## 3rd Qu.: 21.20 3rd Qu.: 0.90 3rd Qu.: 81.04
## Max. : 120.90 Max. :55.00 Max. :256.48
## NA's :19216 NA's :19221 NA's :19216
## amplitude_pitch_dumbbell amplitude_yaw_dumbbell total_accel_dumbbell
## Min. : 0.00 Min. :0 Min. : 0.00
## 1st Qu.: 17.06 1st Qu.:0 1st Qu.: 4.00
## Median : 41.73 Median :0 Median :10.00
## Mean : 65.93 Mean :0 Mean :13.72
## 3rd Qu.: 99.55 3rd Qu.:0 3rd Qu.:19.00
## Max. :273.59 Max. :0 Max. :58.00
## NA's :19216 NA's :19221
## var_accel_dumbbell avg_roll_dumbbell stddev_roll_dumbbell
## Min. : 0.000 Min. :-128.96 Min. : 0.000
## 1st Qu.: 0.378 1st Qu.: -12.33 1st Qu.: 4.639
## Median : 1.000 Median : 48.23 Median : 12.204
## Mean : 4.388 Mean : 23.86 Mean : 20.761
## 3rd Qu.: 3.434 3rd Qu.: 64.37 3rd Qu.: 26.356
## Max. :230.428 Max. : 125.99 Max. :123.778
## NA's :19216 NA's :19216 NA's :19216
## var_roll_dumbbell avg_pitch_dumbbell stddev_pitch_dumbbell
## Min. : 0.00 Min. :-70.73 Min. : 0.000
## 1st Qu.: 21.52 1st Qu.:-42.00 1st Qu.: 3.482
## Median : 148.95 Median :-19.91 Median : 8.089
## Mean : 1020.27 Mean :-12.33 Mean :13.147
## 3rd Qu.: 694.65 3rd Qu.: 13.21 3rd Qu.:19.238
## Max. :15321.01 Max. : 94.28 Max. :82.680
## NA's :19216 NA's :19216 NA's :19216
## var_pitch_dumbbell avg_yaw_dumbbell stddev_yaw_dumbbell
## Min. : 0.00 Min. :-117.950 Min. : 0.000
## 1st Qu.: 12.12 1st Qu.: -76.696 1st Qu.: 3.885
## Median : 65.44 Median : -4.505 Median : 10.264
## Mean : 350.31 Mean : 0.202 Mean : 16.647
## 3rd Qu.: 370.11 3rd Qu.: 71.234 3rd Qu.: 24.674
## Max. :6836.02 Max. : 134.905 Max. :107.088
## NA's :19216 NA's :19216 NA's :19216
## var_yaw_dumbbell gyros_dumbbell_x gyros_dumbbell_y
## Min. : 0.00 Min. :-204.0000 Min. :-2.10000
## 1st Qu.: 15.09 1st Qu.: -0.0300 1st Qu.:-0.14000
## Median : 105.35 Median : 0.1300 Median : 0.03000
## Mean : 589.84 Mean : 0.1611 Mean : 0.04606
## 3rd Qu.: 608.79 3rd Qu.: 0.3500 3rd Qu.: 0.21000
## Max. :11467.91 Max. : 2.2200 Max. :52.00000
## NA's :19216
## gyros_dumbbell_z accel_dumbbell_x accel_dumbbell_y accel_dumbbell_z
## Min. : -2.380 Min. :-419.00 Min. :-189.00 Min. :-334.00
## 1st Qu.: -0.310 1st Qu.: -50.00 1st Qu.: -8.00 1st Qu.:-142.00
## Median : -0.130 Median : -8.00 Median : 41.50 Median : -1.00
## Mean : -0.129 Mean : -28.62 Mean : 52.63 Mean : -38.32
## 3rd Qu.: 0.030 3rd Qu.: 11.00 3rd Qu.: 111.00 3rd Qu.: 38.00
## Max. :317.000 Max. : 235.00 Max. : 315.00 Max. : 318.00
##
## magnet_dumbbell_x magnet_dumbbell_y magnet_dumbbell_z roll_forearm
## Min. :-643.0 Min. :-3600 Min. :-262.00 Min. :-180.0000
## 1st Qu.:-535.0 1st Qu.: 231 1st Qu.: -45.00 1st Qu.: -0.7375
## Median :-479.0 Median : 311 Median : 13.00 Median : 21.7000
## Mean :-328.5 Mean : 221 Mean : 46.05 Mean : 33.8265
## 3rd Qu.:-304.0 3rd Qu.: 390 3rd Qu.: 95.00 3rd Qu.: 140.0000
## Max. : 592.0 Max. : 633 Max. : 452.00 Max. : 180.0000
##
## pitch_forearm yaw_forearm kurtosis_roll_forearm
## Min. :-72.50 Min. :-180.00 Min. :-1.879
## 1st Qu.: 0.00 1st Qu.: -68.60 1st Qu.:-1.398
## Median : 9.24 Median : 0.00 Median :-1.119
## Mean : 10.71 Mean : 19.21 Mean :-0.689
## 3rd Qu.: 28.40 3rd Qu.: 110.00 3rd Qu.:-0.618
## Max. : 89.80 Max. : 180.00 Max. :40.060
## NA's :19300
## kurtosis_picth_forearm kurtosis_yaw_forearm skewness_roll_forearm
## Min. :-2.098 Mode:logical Min. :-2.297
## 1st Qu.:-1.376 NA's:19622 1st Qu.:-0.402
## Median :-0.890 Median : 0.003
## Mean : 0.419 Mean :-0.009
## 3rd Qu.: 0.054 3rd Qu.: 0.370
## Max. :33.626 Max. : 5.856
## NA's :19301 NA's :19299
## skewness_pitch_forearm skewness_yaw_forearm max_roll_forearm
## Min. :-5.241 Mode:logical Min. :-66.60
## 1st Qu.:-0.881 NA's:19622 1st Qu.: 0.00
## Median :-0.156 Median : 26.80
## Mean :-0.223 Mean : 24.49
## 3rd Qu.: 0.514 3rd Qu.: 45.95
## Max. : 4.464 Max. : 89.80
## NA's :19301 NA's :19216
## max_picth_forearm max_yaw_forearm min_roll_forearm min_pitch_forearm
## Min. :-151.00 Min. :-1.900 Min. :-72.500 Min. :-180.00
## 1st Qu.: 0.00 1st Qu.:-1.400 1st Qu.: -6.075 1st Qu.:-175.00
## Median : 113.00 Median :-1.100 Median : 0.000 Median : -61.00
## Mean : 81.49 Mean :-0.689 Mean : -0.167 Mean : -57.57
## 3rd Qu.: 174.75 3rd Qu.:-0.600 3rd Qu.: 12.075 3rd Qu.: 0.00
## Max. : 180.00 Max. :40.100 Max. : 62.100 Max. : 167.00
## NA's :19216 NA's :19300 NA's :19216 NA's :19216
## min_yaw_forearm amplitude_roll_forearm amplitude_pitch_forearm
## Min. :-1.900 Min. : 0.000 Min. : 0.0
## 1st Qu.:-1.400 1st Qu.: 1.125 1st Qu.: 2.0
## Median :-1.100 Median : 17.770 Median : 83.7
## Mean :-0.689 Mean : 24.653 Mean :139.1
## 3rd Qu.:-0.600 3rd Qu.: 39.875 3rd Qu.:350.0
## Max. :40.100 Max. :126.000 Max. :360.0
## NA's :19300 NA's :19216 NA's :19216
## amplitude_yaw_forearm total_accel_forearm var_accel_forearm
## Min. :0 Min. : 0.00 Min. : 0.000
## 1st Qu.:0 1st Qu.: 29.00 1st Qu.: 6.759
## Median :0 Median : 36.00 Median : 21.165
## Mean :0 Mean : 34.72 Mean : 33.502
## 3rd Qu.:0 3rd Qu.: 41.00 3rd Qu.: 51.240
## Max. :0 Max. :108.00 Max. :172.606
## NA's :19300 NA's :19216
## avg_roll_forearm stddev_roll_forearm var_roll_forearm
## Min. :-177.234 Min. : 0.000 Min. : 0.00
## 1st Qu.: -0.909 1st Qu.: 0.428 1st Qu.: 0.18
## Median : 11.172 Median : 8.030 Median : 64.48
## Mean : 33.165 Mean : 41.986 Mean : 5274.10
## 3rd Qu.: 107.132 3rd Qu.: 85.373 3rd Qu.: 7289.08
## Max. : 177.256 Max. :179.171 Max. :32102.24
## NA's :19216 NA's :19216 NA's :19216
## avg_pitch_forearm stddev_pitch_forearm var_pitch_forearm
## Min. :-68.17 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.336 1st Qu.: 0.113
## Median : 12.02 Median : 5.516 Median : 30.425
## Mean : 11.79 Mean : 7.977 Mean : 139.593
## 3rd Qu.: 28.48 3rd Qu.:12.866 3rd Qu.: 165.532
## Max. : 72.09 Max. :47.745 Max. :2279.617
## NA's :19216 NA's :19216 NA's :19216
## avg_yaw_forearm stddev_yaw_forearm var_yaw_forearm gyros_forearm_x
## Min. :-155.06 Min. : 0.000 Min. : 0.00 Min. :-22.000
## 1st Qu.: -26.26 1st Qu.: 0.524 1st Qu.: 0.27 1st Qu.: -0.220
## Median : 0.00 Median : 24.743 Median : 612.21 Median : 0.050
## Mean : 18.00 Mean : 44.854 Mean : 4639.85 Mean : 0.158
## 3rd Qu.: 85.79 3rd Qu.: 85.817 3rd Qu.: 7368.41 3rd Qu.: 0.560
## Max. : 169.24 Max. :197.508 Max. :39009.33 Max. : 3.970
## NA's :19216 NA's :19216 NA's :19216
## gyros_forearm_y gyros_forearm_z accel_forearm_x accel_forearm_y
## Min. : -7.02000 Min. : -8.0900 Min. :-498.00 Min. :-632.0
## 1st Qu.: -1.46000 1st Qu.: -0.1800 1st Qu.:-178.00 1st Qu.: 57.0
## Median : 0.03000 Median : 0.0800 Median : -57.00 Median : 201.0
## Mean : 0.07517 Mean : 0.1512 Mean : -61.65 Mean : 163.7
## 3rd Qu.: 1.62000 3rd Qu.: 0.4900 3rd Qu.: 76.00 3rd Qu.: 312.0
## Max. :311.00000 Max. :231.0000 Max. : 477.00 Max. : 923.0
##
## accel_forearm_z magnet_forearm_x magnet_forearm_y magnet_forearm_z
## Min. :-446.00 Min. :-1280.0 Min. :-896.0 Min. :-973.0
## 1st Qu.:-182.00 1st Qu.: -616.0 1st Qu.: 2.0 1st Qu.: 191.0
## Median : -39.00 Median : -378.0 Median : 591.0 Median : 511.0
## Mean : -55.29 Mean : -312.6 Mean : 380.1 Mean : 393.6
## 3rd Qu.: 26.00 3rd Qu.: -73.0 3rd Qu.: 737.0 3rd Qu.: 653.0
## Max. : 291.00 Max. : 672.0 Max. :1480.0 Max. :1090.0
##
## classe
## A:5580
## B:3797
## C:3422
## D:3216
## E:3607
##
## Citation
- http://groupware.les.inf.puc-rio.br/har
- Velloso, E.; Bulling, A.; Gellersen, H.; Ugulino, W.; Fuks, H. Qualitative Activity Recognition of Weight Lifting Exercises. Proceedings of 4th International Conference in Cooperation with SIGCHI (Augmented Human ’13) . Stuttgart, Germany: ACM SIGCHI, 2013.