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Activity recognition with wearable accelerometers Mitja Luštrek Jožef Stefan Institute Department of Intelligent Systems Slovenia Tutorial at the University.

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Presentation on theme: "Activity recognition with wearable accelerometers Mitja Luštrek Jožef Stefan Institute Department of Intelligent Systems Slovenia Tutorial at the University."— Presentation transcript:

1 Activity recognition with wearable accelerometers Mitja Luštrek Jožef Stefan Institute Department of Intelligent Systems Slovenia Tutorial at the University of Bremen, November 2012

2 Outline Accelerometers Activity recognition with machine learning Random Forest machine learning algorithm Machine learning tools

3 Outline Accelerometers Activity recognition with machine learning Random Forest machine learning algorithm Machine learning tools

4 Whan an accelerometer does It measures acceleration! 9.81 m/s 2 at rest (1 g, the Earth’s gravity) 0 m/s 2 in free fall Gravity + whatever additional forces are present in typical conditions

5 How does it work In MEMS accelerometers the mass pushes: Metal plates of a capacitor to change capacitance A resistor to change resistance A piezoelectric crystal to produce voltage

6 Three axes Accelerometers normally sense acceleration along a single axis To sense all acceleration, three-axial accelerometers are typically used Since accelerometers sense the gravity, this means we know which way the ground is – Auto-rotation of smartphone display – Orientation of body parts

7 Best placement for activity recognition

8 Shimmer accelerometers Also available: Gyroscope Magnetometer ECG EMG Galvanic skin response Ambient (temperature, humidity, motion) GPS Bluetooth 802.15.4 (ZigBee) Programmable microcontroller (TinyOS) 200 EUR

9 TinyOS programming OS for low-power wireless devices nesC – a C dialect, event-driven A bit tricky to get used to, not perfectly documented Quite capable, can do any (not overly computationally demanding) data manipulation and transmission

10

11 Bluetooth Low Energy Important limitation of wearable sensors: – Bluetooth consumes too much power (Shimmer drains battery in <8 hours of continuous transmission) – 802.15.4 (ZigBee) a bit better, but incompatible with smartphones Bluetooth Low Energy coming to the market: – 50 hours of continuous transmission from a coin battery – Works on Motorola RAZR smartphone (more phones coming)

12 Outline Accelerometers Activity recognition with machine learning Random Forest machine-learning algorithm Machine learning tools

13 Sensor signal → features 1 g xyzxyz Compute features/attributes for each window Sliding window (overlapping or no) 20–50 Hz (our choice is 50 Hz) 1–5 s (our choice is 2 s)

14 Features The average acceleration along the x, y and z axes, and the average length of the acceleration vector within the window.

15 Features The variance of the acceleration along x, y and z axes and the variance of the length of the acceleration vector The maximum and the minimum acceleration along the x, y and z axes and the maximum and the minimum length of the acceleration vector

16 Features The orientation of the accelerometer along the x, y and z axes The sum of absolute differences between the consecutive lengths of the acceleration vector

17 Features The angle of change in the acceleration between the maximum and the minimum acceleration along the x, y and z axes and the length of the acceleration vector

18 Features The correlation between all pairs of axes of the accelerometer, computed as the Pearson product-moment correlation coefficient And others, including frequency-domain features...

19 Machine learning f1f1 f2f2 f3f3...Activity Feature vector Features Class Training data needed, in which the class is manually labelled Time-consuming to prepare Labelling during recording can speed it up

20 Machine learning f1f1 f2f2 f3f3...Activity Feature vector Features Class Machine-learning algorithm Training Classifier

21 Machine learning f1f1 f2f2 f3f3... Feature vector Features Use/testing Activity Predicted class Classifier

22 Outline Accelerometers Activity recognition with machine learning Random Forest machine learning algorithm Machine learning tools

23 Decision tree Play tennis? Feature Feature value Class

24 ID3/C4.5 algorithm FS = the set of all features TS = whole training set T = tree with a single node BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti)

25 ID3/C4.5 algorithm FS = {outlook, temperature, humidity, windy} TS = T =

26 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = outlook

27 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = outlook outlook = sunny

28 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = outlook outlook = sunny

29 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = humidity

30 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = humidity

31 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = outlook outlook = overcast

32 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = outlook outlook = rainy

33 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = outlook outlook = rainy

34 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = windy

35 BuildTree (FS, TS, T) F = feature from FS that best classifies TS Assign F to the node in T For each possible value of F = f1... fN TSi = subset of TS where F = fi If all instances in TSi have the same class then stop else Ti = newly crated descendant of T BuildTree (FS without F, TSi, Ti) F = windy

36 Feature that best classifies... Measured by information gain Information gain of the feature F measures how much information about the class C one gains by knowing the value of F In other words, it is the reduction in uncertainy about C, which is measured by entropy H (C)... entropy of C H (C|F)... conditional entropy of C if the value of F is known IG (C, F) = H (C) – H (C|F)

37 Entropy Entropy of the class C is the expected number of bits needed to (optimally) encode the class of a randomly drawn instance Assume C has 4 possible values c 1, c 2, c 3, c 4 Evenly distributedUnevenly distributed P (c 1 ) = 0.25, c 1 = 00P (c 1 ) = 0.90, c 1 = 0 P (c 2 ) = 0.25, c 2 = 01P (c 2 ) = 0.08, c 2 = 10 P (c 3 ) = 0.25, c 3 = 10P (c 3 ) = 0.01, c 3 = 110 P (c 4 ) = 0.25, c 4 = 11P (c 4 ) = 0.01, c 4 = 111 H (C) = 2 bitH (C) = 1.12 bit

38 Entropy & information gain The class C has possible values c 1,..., c M The feature F has possible values f 1,..., f N IG (C, F) = H (C) – H (C|F)

39 Random forest Random forest is composed of multiple trees (it is an ensemble of trees) The trees are somewhat random No surprises so far! Works really well on activity recognition

40 Random forest N... number of instances M... number of features N instances are selected randomly with replacement m features (m << M) are selected randomly A decision tree is built from the selected instances and features The procedure is repeated an arbitrary number of times A new instance is classified by all the trees in the forest The final class is selected by majority voting

41 Outline Accelerometers Activity recognition with machine learning Random Forest machine learning algorithm Machine learning tools

42 Free machine learning tools Weka (Waikato Environment for Knowledge Analysis) Orange RapidMiner KNIME (Konstanz Information Miner)...

43 Weka Written in Java Open source University of Waikato, New Zealand Graphical user interface Command-line interface Java API

44 //Train classifier Instances dsTrain = new Instances (new BufferedReader (new FileReader ("arff/chest&thigh_train.arff"))); dsTrain.setClassIndex (dsTrain.numAttributes () - 1); RandomForest rf = new RandomForest (); rf.buildClassifier (dsTrain); //Classify Instances dsTest = new Instances (new BufferedReader (new FileReader ("arff/chest&thigh_test.arff"))); dsTest.setClassIndex (dsTest.numAttributes () - 1); int correctCount = 0; for (int i = 0; i < dsTest.numInstances (); i++) { double cl = rf.classifyInstance (dsTest.instance (i)); if (cl == dsTest.instance (i).classValue ()) { correctCount++; } //Output accuracy System.out.println ("Accuracy: " + (double)correctCount / (double)dsTest.numInstances ());

45 Orange Written in C++ and Python Open source University of Ljubljana, Faculty of Computer and Information Science, Slovenia Graphical user interface Scripting in Python

46

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