diff --git a/examples/DrawingInTheAir/DrawingInTheAir_MotionActivated b/examples/DrawingInTheAir/DrawingInTheAir_MotionActivated
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+/*
+ * This example demonstrates using the pattern matching engine (CuriePME)
+ * to classify streams of accelerometer data from CurieIMU.
+ *** ENHANCEMENT: Automatic data capture with build in MOTION DETECTION functionality, no need of button or any extra input hardware.
+ *
+ * First, the sketch will prompt you to draw some letters in the air (just
+ * imagine you are writing on an invisible whiteboard, using your board as the
+ * pen), and the IMU data from these motions is used as training data for the
+ * PME. Once training is finished, you can keep drawing letters and the PME
+ * will try to guess which letter you are drawing.
+ *
+ * The built in motion detection interrupt is being used to detect whether u are writing or not.
+ * While drawing in the air, the buzzer sounds, so wait until the buzzer turns off after drawing a letter and repeat the cycle.
+ *
+ * NOTE: For best results, draw big letters, at least 1-2 feet tall.
+ *
+ * Copyright (c) 2016 Intel Corporation.  All rights reserved.
+ * See license notice at end of file.
+ */
+
+#include "CurieIMU.h"
+#include "CuriePME.h"
+
+/*  This controls how many times a letter must be drawn during training.
+ *  Any higher than 4, and you may not have enough neurons for all 26 letters
+ *  of the alphabet. Lower than 4 means less work for you to train a letter,
+ *  but the PME may have a harder time classifying that letter. */
+const unsigned int trainingReps = 4;
+
+/* Increase this to 'A-Z' if you like-- it just takes a lot longer to train */
+const unsigned char trainingStart = 'A';
+const unsigned char trainingEnd = 'D';
+
+/* An optional buzzer or LED might be attached to a digital pin for feedback */
+ 
+const unsigned int buzzerPin = 11;
+
+/* Sample rate for accelerometer */
+const unsigned int sampleRateHZ = 200;
+
+/* No. of bytes that one neuron can hold */
+const unsigned int vectorNumBytes = 128;
+
+/* Number of processed samples (1 sample == accel x, y, z)
+ * that can fit inside a neuron */
+const unsigned int samplesPerVector = (vectorNumBytes / 3);
+
+/* This value is used to convert ASCII characters A-Z
+ * into decimal values 1-26, and back again. */
+const unsigned int upperStart = 0x40;
+
+const unsigned int sensorBufSize = 2048;
+const int IMULow = -32768;
+const int IMUHigh = 32767;
+
+boolean motionFlag = false;  // Motion Activation Flag 
+
+void setup()
+{
+    Serial.begin(9600);
+    while(!Serial);
+
+    pinMode(buzzerPin, OUTPUT);
+    digitalWrite(buzzerPin, LOW);
+    /* Start the IMU (Intertial Measurement Unit) */
+    CurieIMU.begin();
+    CurieIMU.attachInterrupt(eventCallback);
+
+  /* Enable Zero Motion Detection */
+    CurieIMU.setDetectionThreshold(CURIE_IMU_ZERO_MOTION, 50);  // 50mg
+    CurieIMU.setDetectionDuration(CURIE_IMU_ZERO_MOTION, 2);    // 2s
+    CurieIMU.interrupts(CURIE_IMU_ZERO_MOTION);
+
+  /* Enable Motion Detection */
+  CurieIMU.setDetectionThreshold(CURIE_IMU_MOTION, 20);      // 20mg
+  CurieIMU.setDetectionDuration(CURIE_IMU_MOTION, 10);       // trigger times of consecutive slope data points
+  CurieIMU.interrupts(CURIE_IMU_MOTION);
+
+  Serial.println("IMU initialisation complete, waiting for events...");
+    /* Start the PME (Pattern Matching Engine) */
+    CuriePME.begin();
+
+    CurieIMU.setAccelerometerRate(sampleRateHZ);
+    CurieIMU.setAccelerometerRange(2);
+
+
+    trainLetters();
+    Serial.println("Training complete. Now, draw some letters (remember to ");
+    Serial.println(" Draw and see if the PME can classify them.");
+}
+
+void loop ()
+{
+    byte vector[vectorNumBytes];
+    unsigned int category;
+    char letter;
+
+    /* Record IMU data while button is being held, and
+     * convert it to a suitable vector */
+    readVectorFromIMU(vector);
+
+    /* Use the PME to classify the vector, i.e. return a category
+     * from 1-26, representing a letter from A-Z */
+    category = CuriePME.classify(vector, vectorNumBytes);
+
+    if (category == CuriePME.noMatch) {
+        Serial.println("Don't recognise that one-- try again.");
+    } else {
+        letter = category + upperStart;
+        Serial.println(letter);
+    }
+}
+
+/* Simple "moving average" filter, removes low noise and other small
+ * anomalies, with the effect of smoothing out the data stream. */
+byte getAverageSample(byte samples[], unsigned int num, unsigned int pos,
+                   unsigned int step)
+{
+    unsigned int ret;
+    unsigned int size = step * 2;
+
+    if (pos < (step * 3) || pos > (num * 3) - (step * 3)) {
+        ret = samples[pos];
+    } else {
+        ret = 0;
+        pos -= (step * 3);
+        for (unsigned int i = 0; i < size; ++i) {
+            ret += samples[pos - (3 * i)];
+        }
+
+        ret /= size;
+    }
+
+    return (byte)ret;
+}
+
+/* We need to compress the stream of raw accelerometer data into 128 bytes, so
+ * it will fit into a neuron, while preserving as much of the original pattern
+ * as possible. Assuming there will typically be 1-2 seconds worth of
+ * accelerometer data at 200Hz, we will need to throw away over 90% of it to
+ * meet that goal!
+ *
+ * This is done in 2 ways:
+ *
+ * 1. Each sample consists of 3 signed 16-bit values (one each for X, Y and Z).
+ *    Map each 16 bit value to a range of 0-255 and pack it into a byte,
+ *    cutting sample size in half.
+ *
+ * 2. Undersample. If we are sampling at 200Hz and the button is held for 1.2
+ *    seconds, then we'll have ~240 samples. Since we know now that each
+ *    sample, once compressed, will occupy 3 of our neuron's 128 bytes
+ *    (see #1), then we know we can only fit 42 of those 240 samples into a
+ *    single neuron (128 / 3 = 42.666). So if we take (for example) every 5th
+ *    sample until we have 42, then we should cover most of the sample window
+ *    and have some semblance of the original pattern. */
+void undersample(byte samples[], int numSamples, byte vector[])
+{
+    unsigned int vi = 0;
+    unsigned int si = 0;
+    unsigned int step = numSamples / samplesPerVector;
+    unsigned int remainder = numSamples - (step * samplesPerVector);
+
+    /* Centre sample window */
+    samples += (remainder / 2) * 3;
+    for (unsigned int i = 0; i < samplesPerVector; ++i) {
+        for (unsigned int j = 0; j < 3; ++j) {
+            vector[vi + j] = getAverageSample(samples, numSamples, si + j, step);
+        }
+
+        si += (step * 3);
+        vi += 3;
+    }
+}
+
+void readVectorFromIMU(byte vector[])
+{
+    byte accel[sensorBufSize];
+    int raw[3];
+
+    unsigned int samples = 0;
+    unsigned int i = 0;
+
+    /* Wait until Motion detection inturrupt flag  */
+    while (motionFlag == false)
+{
+    digitalWrite(buzzerPin, LOW);  // Optional Buzzer off
+}
+    /* While Motion detection inturrupt flag being held... */
+    while (motionFlag == true) {
+
+        if (CurieIMU.accelDataReady()) {
+
+            CurieIMU.readAccelerometer(raw[0], raw[1], raw[2]);
+
+            /* Map raw values to 0-255 */
+            accel[i] = (byte) map(raw[0], IMULow, IMUHigh, 0, 255);
+            accel[i + 1] = (byte) map(raw[1], IMULow, IMUHigh, 0, 255);
+            accel[i + 2] = (byte) map(raw[2], IMULow, IMUHigh, 0, 255);
+
+            i += 3;
+            ++samples;
+
+            /* If there's not enough room left in the buffers
+            * for the next read, then we're done */
+            if (i + 3 > sensorBufSize) {
+                break;
+            }
+
+        }
+            digitalWrite(buzzerPin, HIGH);   // Optional buzzer rings
+
+    }
+
+    undersample(accel, samples, vector);
+}
+
+void trainLetter(char letter, unsigned int repeat)
+{
+    unsigned int i = 0;
+
+    while (i < repeat) {
+        byte vector[vectorNumBytes];
+
+        if (i) Serial.println("And again...");
+
+        readVectorFromIMU(vector);
+        CuriePME.learn(vector, vectorNumBytes, letter - upperStart);
+
+        Serial.println("Got it!");
+        delay(1000);
+        ++i;
+    }
+}
+
+void trainLetters()
+{
+    for (char i = trainingStart; i <= trainingEnd; ++i) {
+        Serial.print(" Draw the letter in Air while the buzzer rings'");
+        Serial.print(String(i) + "' Detecting a motion automatically initiate data capture and the optional buzzer rings ");
+        Serial.println("while you are done.");
+
+        trainLetter(i, trainingReps);
+        Serial.println("OK, finished with this letter.");
+        delay(2000);
+    }
+}
+
+
+
+// MOTION DETECTION interrupt subroutine 
+
+
+
+
+static void eventCallback(void){
+  if (CurieIMU.getInterruptStatus(CURIE_IMU_ZERO_MOTION)) {
+    motionFlag = false; 
+   // Serial.println("zero motion detected...");
+  }  
+  if (CurieIMU.getInterruptStatus(CURIE_IMU_MOTION)) {
+    motionFlag = true;
+  
+  }  
+}
+/*
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with this library; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ */