Human Activity Recognition via Cellphone Sensor...
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Human Activity Recognition via Cellphone Sensor Data Wei Ji, Heguang Liu, Jonathan Fisher [email protected], [email protected], [email protected] Stanford University Overview The purpose of this project is to identify human activities while using cell phones via mobile sensor data. We collect 2085 data samples, which includes 3-axis acceleration, angular velocity and orientation sensor data, from 4 volunteers using MATLAB Mobile package. Aer cleaning, interpolating, FFT, we get 135 raw features, and we further reduce the feature number to 21 via feature selection. Aer comparing the results of dierent models, eg Decision Tree, SVM, KNN, we successfully build an Ensembled Bagged Trees Model which gives 95.7% prediction accuracy over 626 test data on 9 human activities(walking, running, driving, typing, writing, talking, laying, siing, standing). Data I Data Collection We collected 60M, approximately 15000 seconds, data at 50Hz sample rate from 4 volunteers using MATLAB Mobile, Android/iOS sensors support package and Mathworks Cloud. Connect Use MATLAB Mobile App on the mobile device to connect to MathWorks Cloud, load collecting script, and tag activities. Record Run the scripts which initialize a mobiledev object, enable sensors, set sample rate to 50Hz and start recording timer. I Data Processing Segment We divide each recording sample into 128*0.02=2.56 second segments. And each segment is used as a data point. We also remove duplicate, missing data. Interpolate Since sensors are not exactly synchronized, and time internal between collected samples is in a range of [0.018, 0.022] second, we use Linear Grid Interpolation to get synchronized data point. Features I Raw Features • 9*128 raw features from 9 sensors on time domain. I Features Transformation • Covariance matrix of every 2 sensors gives 45 features. • First 10 spectrum in frequency domain show high coeicients, gives 9*10 features. I Feature Selection • 135 features is still too large to make algorithms run eiciently. We adopt SVD and forward feature selection to reduce feature dimensions. • SVD reduce feature dimension Running a serious of SVD on a bagged tree model shows that Top 7 features can preserve 95% of total energy, and give a 9.3% test error. Top 22 features can preserve 99% of total energy, which gives 6.6% test error. • Forward selection reduce feature dimension Running series of forward feature selection on the bagged tree model shows that, 18, 21, 27-feature model overplay 135 feature model. The 21 feature model achieves lowest test error 4.3% at number of trees larger than 300. Models Comparison This is a standard Supervised Learning problem to find the best model to predict 9 labels with 21 features. We tried to solve this problem using Decision Tree, SVM and KNN. I Decision Tree Comparison We use bagged tree (Random Forest), Boosted Tree (Gentle Adaboost) with dierent number of trees. Results show that bagged trees with 300 trees achieves the lowest error rate 4.3%. I SVM Comparison We tried Linear, Polynomial and RBF(Gaussian) Kernel, using L1-regulation with various box constrains. Results show that linear kernel achieves lowest error rate at 6.8% with C=10000. RBF, Polynomial kernal models have the worse results. I KNN Comparison We tried Euclidean, Chebychev and Cityblock distance function to measure the KNN distance with various k. Results show that KNN has a worse performance comparing with Tree and SVM Models. Results and Discussion Based on the model comparison, we find the optimized algorithm is Bagged Trees with Number of trees bigger than 300. The Bagged Tree model is built using formula (1). Based on the Confusion Matrix and ROC, we discovered the following facts: I Less intensive activities, siing and standing are more diicult to dierentiate. We think it is because these activities are similar in the view of sensors. I Standing has a high false negative, might due to lack of training data samples. I Typing and Standing, Walking and Standing, Typing and Laying are harder to be classified from each other. This might be because of multiple activities are taken as the same time, but we only support user to tag one activity per data sample. ˆ f bag (x ) = 1 B B b=1 ˆ f (b) (x ) (1) Future work From this project, we noticed the following facts: 1) Adding more data is still improving our test error on the margin. We would like to try to add more data to see how much this will improve the model. 2) Having a beer definition of activities would improve this model. We would like to allow the possibility of tagging multiple activities at the same data point, since some of activities can be done simultaneously. 3) Sensor data collection from dierent types of phones can be significantly dierent. The reason for this could be the sensors calibration and precision are dierent for each phone. We end up using the data from the same phone. 4) There exists an activity paern for each user. Using one user’s training data to predict another user’s behavior is performance worse than predict this user’s behavior. Personalized model for each user might improve the prediction accuracy. We would like to solve these problem in the future. References and Acknowledgements Thanks for Professor Andrew Ng, Professor John Duchi, TA Rishabh Bhagava’s instruction. Shu Chen and Yan Huang, "Recognizing human activities from multi-modal sensors," Intelligence and Security Informatics, 2009. ISI ’09. IEEE International Conference on, Dallas, TX, 2009, pp. 220-222. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine, Davide Anguita, et. al. Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.gnal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4.
Transcript of Human Activity Recognition via Cellphone Sensor...
Human Activity Recognition via Cellphone Sensor DataWei Ji, Heguang Liu, Jonathan [email protected], [email protected], [email protected]
Stanford University
Overview
The purpose of this project is to identify human activities while using cell phones viamobile sensor data. We collect 2085 data samples, which includes 3-axis acceleration,angular velocity and orientation sensor data, from 4 volunteers using MATLAB Mobilepackage. A�er cleaning, interpolating, FFT, we get 135 raw features, and we furtherreduce the feature number to 21 via feature selection. A�er comparing the results ofdi�erent models, eg Decision Tree, SVM, KNN, we successfully build an EnsembledBagged Trees Model which gives 95.7% prediction accuracy over 626 test data on 9human activities(walking, running, driving, typing, writing, talking, laying, si�ing,standing).
Data
I Data CollectionWe collected 60M, approximately 15000 seconds, data at 50Hz sample rate from 4volunteers using MATLAB Mobile, Android/iOS sensors support package andMathworks Cloud.Connect Use MATLAB Mobile App on the mobile device to connect to MathWorks
Cloud, load collecting script, and tag activities.Record Run the scripts which initialize a mobiledev object, enable sensors, set
sample rate to 50Hz and start recording timer.I Data Processing
Segment We divide each recording sample into 128*0.02=2.56 second segments.And each segment is used as a data point. We also remove duplicate,missing data.
Interpolate Since sensors are not exactly synchronized, and time internal betweencollected samples is in a range of [0.018, 0.022] second, we use Linear GridInterpolation to get synchronized data point.
FeaturesI Raw Features• 9*128 raw features from 9 sensors on time domain.
I Features Transformation• Covariance matrix of every 2 sensors gives 45 features.• First 10 spectrum in frequency domain show high coe�icients, gives 9*10 features.
I Feature Selection• 135 features is still too large to make algorithms run e�iciently. We adopt SVD and
forward feature selection to reduce feature dimensions.• SVD reduce feature dimension
Running a serious of SVD ona bagged tree model showsthat Top 7 features canpreserve 95% of total energy,and give a 9.3% test error.Top 22 features can preserve99% of total energy, whichgives 6.6% test error.
• Forward selection reduce feature dimensionRunning series of forwardfeature selection on thebagged tree model showsthat, 18, 21, 27-featuremodel overplay 135 featuremodel. The 21 feature modelachieves lowest test error4.3% at number of treeslarger than 300.
Models Comparison
This is a standard Supervised Learning problem to find the best model topredict 9 labels with 21 features. We tried to solve this problem using DecisionTree, SVM and KNN.I Decision Tree Comparison
We use bagged tree (RandomForest), Boosted Tree (GentleAdaboost) with di�erent numberof trees. Results show thatbagged trees with 300 treesachieves the lowest error rate4.3%.
I SVM ComparisonWe tried Linear, Polynomial andRBF(Gaussian) Kernel, usingL1-regulation with various boxconstrains. Results show thatlinear kernel achieves lowesterror rate at 6.8% with C=10000.RBF, Polynomial kernal modelshave the worse results.I KNN Comparison
We tried Euclidean, Chebychevand Cityblock distance functionto measure the KNN distancewith various k. Results show thatKNN has a worse performancecomparing with Tree and SVMModels.
Results and Discussion
Based on the model comparison, we find the optimized algorithm is Bagged Trees withNumber of trees bigger than 300. The Bagged Tree model is built using formula (1).Based on the Confusion Matrix and ROC, we discovered the following facts:I Less intensive activities, si�ing and standing are more di�icult to di�erentiate. We
think it is because these activities are similar in the view of sensors.I Standing has a high false negative, might due to lack of training data samples.I Typing and Standing, Walking and Standing, Typing and Laying are harder to be
classified from each other. This might be because of multiple activities are taken asthe same time, but we only support user to tag one activity per data sample.
f̂ bag(x) = 1B
B∑b=1
f̂ (b)(x) (1)
Future work
From this project, we noticed the following facts: 1) Adding more data is still improvingour test error on the margin. We would like to try to add more data to see how muchthis will improve the model. 2) Having a be�er definition of activities would improvethis model. We would like to allow the possibility of tagging multiple activities at thesame data point, since some of activities can be done simultaneously. 3) Sensor datacollection from di�erent types of phones can be significantly di�erent. The reason forthis could be the sensors calibration and precision are di�erent for each phone. We endup using the data from the same phone. 4) There exists an activity pa�ern for eachuser. Using one user’s training data to predict another user’s behavior is performanceworse than predict this user’s behavior. Personalized model for each user mightimprove the prediction accuracy. We would like to solve these problem in the future.
References and Acknowledgements
Thanks for Professor Andrew Ng, Professor John Duchi, TA Rishabh Bhagava’sinstruction.
Shu Chen and Yan Huang, "Recognizing human activities from multi-modal sensors," Intelligence andSecurity Informatics, 2009. ISI ’09. IEEE International Conference on, Dallas, TX, 2009, pp. 220-222.
Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support VectorMachine, Davide Anguita, et. al.
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public DomainDataset for Human Activity Recognition Using Smartphones. 21th European Symposium on ArtificialNeural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium24-26 April 2013.gnal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4.
