ai-techsystems · Apr 8, 2020
diff --git a/‎examples/asl_model.cpp
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+// Copyright 2018 The AITS DNNC Authors.All Rights Reserved.
+//
+// Licensed to the Apache Software Foundation(ASF) under one
+// or more contributor license agreements.See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.See the License for the
+// specific language governing permissionsand limitations
+// under the License.
+//
+// This file is part of AITS arduino library maintained at
+// https://github.com/ai-techsystems/arduino
+//
+// Description:
+//          American sign language model created using 
+// bit.ly/dnnCompiler and converted to inference application
+// for arduino nano 33 ble sense
+//
+// Compile: /home/aits/.arduino15/packages/arduino/tools/arm-none-eabi-gcc/7-2017q4/bin/arm-none-eabi-g++ -c asl_model.cpp -O3 -Wall -DARDUINO -std=c++17 -I../src -I ../src/Eigen
+
+#if defined(ARDUINO)
+#include <Arduino_LSM9DS1.h>
+#include "deepC.h"
+#endif
+
+#include "operators/Constant.h"
+#include "operators/Reshape.h"
+#include "operators/Gemm.h"
+#include "operators/Relu.h"
+#include "operators/LogSoftmax.h"
+
+using namespace dnnc;
+
+const PROGMEM float dnnc_fc1_dot_bias_data[] = {-0.003086,-0.031912,0.020685,-0.013325,-0.194618,-0.022973,0.014909,-0.002588} ;
+const PROGMEM float dnnc_fc1_dot_weight_data[] = 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;
+const PROGMEM float dnnc_fc2_dot_bias_data[] = {-0.216914,1.873187,-0.050328,-0.169354} ;
+const PROGMEM float dnnc_fc2_dot_weight_data[] = {0.218817,0.088550,0.318061,0.248361,-0.327833,-0.017152,0.042804,0.100464,0.186706,0.048677,0.100916,-0.290552,-0.645874,0.250051,-0.194110,-0.018149,0.237442,0.141186,0.316304,-0.139612,1.487356,0.013215,0.214298,-0.217383,0.091865,0.062357,0.290944,0.339741,0.022039,-0.166652,0.248257,0.136047} ;
+const PROGMEM float dnnc_fc3_dot_bias_data[] = {0.235671,1.771050} ;
+const PROGMEM float dnnc_fc3_dot_weight_data[] = {0.192724,-0.386185,1.199224,-0.352258,0.136847,1.955971,-0.911277,0.226869} ;
+
+tensor<float> dnnc_model(tensor<float>& dnnc_0 /*{714}*/) {
+
+  tensor<float> dnnc_fc1_dot_weight({8, 714}); dnnc_fc1_dot_weight.load(dnnc_fc1_dot_weight_data);
+  tensor<float> dnnc_fc1_dot_bias({8}); dnnc_fc1_dot_bias.load(dnnc_fc1_dot_bias_data);
+  tensor<float> dnnc_fc2_dot_weight({4, 8}); dnnc_fc2_dot_weight.load(dnnc_fc2_dot_weight_data);
+  tensor<float> dnnc_fc2_dot_bias({4}); dnnc_fc2_dot_bias.load(dnnc_fc2_dot_bias_data);
+  tensor<float> dnnc_fc3_dot_weight({2, 4}); dnnc_fc3_dot_weight.load(dnnc_fc3_dot_weight_data);
+  tensor<float> dnnc_fc3_dot_bias({2}); dnnc_fc3_dot_bias.load(dnnc_fc3_dot_bias_data);
+
+  Constant<int64_t> dnnc___1("dnnc___1");
+  std::vector<int64_t> dnnc___1_value_vec = {-1,714};
+  tensor<int64_t> dnnc___1_value({2}); dnnc___1_value.load(dnnc___1_value_vec);
+  dnnc___1.setAttribute ( attr_value, dnnc___1_value );
+  tensor<int64_t> dnnc_dnnc___1_7 = dnnc___1.compute ();
+
+
+  Reshape<float, float, int64_t> dnnc___2("dnnc___2");
+  tensor<float> dnnc_dnnc___2_8 = dnnc___2.compute ( dnnc_0, dnnc_dnnc___1_7);
+
+
+  Gemm<float, float, float> dnnc___3("dnnc___3");
+  float dnnc___3_alpha = 1.000000 ;
+  dnnc___3.setAttribute ( attr_alpha, dnnc___3_alpha );
+  float dnnc___3_beta = 1.000000 ;
+  dnnc___3.setAttribute ( attr_beta, dnnc___3_beta );
+  int dnnc___3_transB = 1 ;
+  dnnc___3.setAttribute ( attr_transB, dnnc___3_transB );
+  tensor<float> dnnc_dnnc___3_9 = dnnc___3.compute ( dnnc_dnnc___2_8, dnnc_fc1_dot_weight, dnnc_fc1_dot_bias);
+
+
+  Relu<float, float> dnnc___4("dnnc___4");
+  tensor<float> dnnc_dnnc___4_10 = dnnc___4.compute ( dnnc_dnnc___3_9);
+
+
+  Gemm<float, float, float> dnnc___5("dnnc___5");
+  float dnnc___5_alpha = 1.000000 ;
+  dnnc___5.setAttribute ( attr_alpha, dnnc___5_alpha );
+  float dnnc___5_beta = 1.000000 ;
+  dnnc___5.setAttribute ( attr_beta, dnnc___5_beta );
+  int dnnc___5_transB = 1 ;
+  dnnc___5.setAttribute ( attr_transB, dnnc___5_transB );
+  tensor<float> dnnc_dnnc___5_11 = dnnc___5.compute ( dnnc_dnnc___4_10, dnnc_fc2_dot_weight, dnnc_fc2_dot_bias);
+
+
+  Relu<float, float> dnnc___6("dnnc___6");
+  tensor<float> dnnc_dnnc___6_12 = dnnc___6.compute ( dnnc_dnnc___5_11);
+
+
+  Gemm<float, float, float> dnnc___7("dnnc___7");
+  float dnnc___7_alpha = 1.000000 ;
+  dnnc___7.setAttribute ( attr_alpha, dnnc___7_alpha );
+  float dnnc___7_beta = 1.000000 ;
+  dnnc___7.setAttribute ( attr_beta, dnnc___7_beta );
+  int dnnc___7_transB = 1 ;
+  dnnc___7.setAttribute ( attr_transB, dnnc___7_transB );
+  tensor<float> dnnc_dnnc___7_13 = dnnc___7.compute ( dnnc_dnnc___6_12, dnnc_fc3_dot_weight, dnnc_fc3_dot_bias);
+
+
+  Relu<float, float> dnnc___8("dnnc___8");
+  tensor<float> dnnc_dnnc___8_14 = dnnc___8.compute ( dnnc_dnnc___7_13);
+
+
+  return dnnc_dnnc___8_14 ;
+}
+
+#if defined(ARDUINO)
+
+const int REDpin = 24;
+const int GREENpin = 23;
+const int BLUEpin = 22;
+
+void setup() {
+  Serial.begin(9600);
+    while (!Serial);
+
+  // initialize the IMU
+  if (!IMU.begin()) {
+    Serial.println("Failed to initialize IMU!");
+    while (1);
+  }
+  pinMode(REDpin, OUTPUT);
+  pinMode(GREENpin, OUTPUT);
+  pinMode(BLUEpin, OUTPUT);
+  digitalWrite(REDpin, LOW);
+  digitalWrite(GREENpin, LOW);
+  digitalWrite(BLUEpin,  LOW);
+}
+
+void lightLED(int prediction) {
+
+  digitalWrite(REDpin,   LOW);
+  if ( prediction ) { // how are you??
+    digitalWrite(GREENpin, LOW);
+    digitalWrite(BLUEpin,  HIGH);
+  } else { // hello
+    digitalWrite(GREENpin, HIGH);
+    digitalWrite(BLUEpin,  LOW);
+  }
+  delay(1000);
+
+  digitalWrite(REDpin, LOW);
+  digitalWrite(GREENpin, LOW);
+  digitalWrite(BLUEpin, LOW);
+}
+
+// RMS (root mean square) threshold of significant motion in G's
+const float ACC_LIMIT = 1.5; // 2.0
+const int NUM_CAPTURED_SAMPLES_PER_GESTURE = 119;
+const int N_FEATURES = 6;
+const int TOTAL_SAMPLES = NUM_CAPTURED_SAMPLES_PER_GESTURE * N_FEATURES;
+const int START_INDEX =  0; //(NUM_CAPTURED_SAMPLES_PER_GESTURE/3)*N_FEATURES;
+
+
+void loop() {
+//float hello[714] = {0.772,0.220,0.448,-6.531,-15.686,2.686, 0.789,0.215,0.450,-2.625,-15.991,-0.854, 0.787,0.192,0.462,2.136,-17.273,-4.700, 0.773,0.168,0.494,5.615,-18.433,-8.301, 0.763,0.148,0.513,7.690,-16.968,-11.597, 0.774,0.140,0.512,6.531,-13.367,-13.916, 0.769,0.146,0.517,5.493,-11.230,-16.602, 0.764,0.136,0.502,4.150,-8.667,-20.264, 0.775,0.114,0.472,2.441,-6.653,-24.048, 0.778,0.087,0.448,-0.610,-6.042,-26.611, 0.770,0.060,0.433,-5.249,-6.836,-28.198, 0.771,0.066,0.431,-12.207,-7.935,-28.137, 0.760,0.102,0.444,-20.325,-9.277,-27.527, 0.737,0.127,0.462,-26.794,-10.315,-27.466, 0.718,0.164,0.467,-33.508,-10.315,-28.381, 0.699,0.194,0.458,-41.138,-10.437,-29.541, 0.676,0.266,0.501,-48.767,-11.963,-30.457, 0.646,0.359,0.537,-53.589,-12.939,-30.884, 0.608,0.437,0.559,-56.580,-13.611,-30.884, 0.577,0.608,0.639,-57.861,-15.320,-30.823, 0.522,0.808,0.764,-51.819,-19.165,-31.433, 0.473,1.005,0.862,-42.236,-18.616,-36.072, 0.421,1.186,0.963,-31.799,-15.381,-47.241, 0.370,1.366,1.051,-26.611,-8.301,-65.063, 0.320,1.516,1.123,-24.292,2.441,-89.294, 0.237,1.636,1.210,-21.790,14.771,-120.789, 0.148,1.684,1.284,-15.991,29.968,-157.776, 0.064,1.685,1.309,-7.446,46.570,-196.350, -0.013,1.707,1.321,2.563,65.369,-233.582, -0.119,1.626,1.300,13.000,85.449,-270.386, -0.224,1.519,1.245,22.217,108.704,-305.847, -0.352,1.266,1.067,42.969,130.249,-339.478, -0.484,0.971,0.930,58.533,147.461,-368.225, -0.629,0.637,0.795,72.632,162.231,-390.015, -0.728,0.191,0.644,82.642,177.917,-406.311, -0.846,-0.254,0.471,86.487,192.017,-415.161, -0.945,-0.901,0.229,80.811,200.256,-418.213, -0.942,-1.734,-0.028,53.894,204.346,-421.021, -0.834,-2.550,-0.263,14.099,199.097,-409.058, -0.669,-3.255,-0.471,-24.475,183.105,-373.718, -0.425,-3.708,-0.663,-52.246,157.471,-319.336, -0.205,-3.640,-0.741,-82.214,127.563,-242.187, -0.024,-3.208,-0.738,-110.657,101.379,-160.217, 0.082,-2.743,-0.704,-127.014,78.613,-103.394, 0.256,-2.316,-0.611,-132.996,55.298,-67.749, 0.350,-1.917,-0.503,-128.357,37.170,-48.645, 0.416,-1.554,-0.411,-113.647,23.071,-39.368, 0.412,-1.270,-0.266,-94.299,12.268,-39.856, 0.332,-1.176,-0.095,-76.965,5.188,-51.025, 0.316,-1.286,-0.002,-65.735,5.493,-70.740, 0.318,-1.498,0.002,-59.021,15.503,-90.332, 0.273,-1.711,-0.020,-40.100,21.851,-98.633, 0.209,-2.020,0.025,-6.592,13.000,-97.961, 0.190,-2.239,0.066,11.536,5.066,-86.182, 0.207,-2.198,0.077,12.512,4.395,-60.059, 0.250,-2.022,0.058,10.254,6.653,-32.593, 0.293,-1.839,0.023,7.141,8.911,-12.207, 0.342,-1.677,0.001,2.197,8.728,-0.366, 0.384,-1.504,-0.016,-5.310,7.507,9.033, 0.402,-1.346,-0.018,-9.644,4.883,15.564, 0.411,-1.196,0.005,-12.817,1.587,19.348, 0.426,-1.050,0.044,-13.672,-0.427,21.179, 0.443,-0.954,0.063,-9.583,-1.221,19.592, 0.456,-0.914,0.069,-2.502,-2.808,16.296, 0.455,-0.899,0.090,5.188,-5.188,13.306, 0.455,-0.892,0.114,12.451,-6.897,11.475, 0.458,-0.886,0.118,18.860,-7.385,10.437, 0.465,-0.882,0.108,23.437,-7.324,9.216, 0.471,-0.893,0.093,26.489,-7.874,8.118, 0.477,-0.899,0.089,27.283,-9.766,7.446, 0.482,-0.907,0.112,25.208,-12.024,7.141, 0.482,-0.915,0.143,23.560,-13.733,6.775, 0.482,-0.926,0.161,21.423,-13.794,7.690, 0.486,-0.918,0.179,18.372,-12.573,9.888, 0.494,-0.907,0.182,16.357,-11.536,12.207, 0.497,-0.884,0.193,14.832,-11.719,13.733, 0.492,-0.871,0.214,14.343,-11.841,13.916, 0.492,-0.870,0.228,14.282,-11.353,13.794, 0.487,-0.884,0.235,16.235,-11.292,13.794, 0.486,-0.888,0.242,18.066,-11.353,14.526, 0.481,-0.891,0.246,19.470,-11.292,15.625, 0.475,-0.906,0.242,20.081,-10.742,16.541, 0.458,-0.920,0.225,20.874,-10.071,16.846, 0.452,-0.923,0.209,20.569,-9.827,15.686, 0.446,-0.988,0.186,22.034,-11.169,12.512, 0.429,-1.050,0.179,23.499,-13.977,10.986, 0.415,-1.084,0.159,23.804,-16.113,11.475, 0.424,-1.107,0.117,23.193,-17.212,12.817, 0.430,-1.122,0.077,22.278,-19.043,15.015, 0.440,-1.116,0.055,21.790,-22.278,18.250, 0.445,-1.112,0.032,21.667,-25.879,21.851, 0.453,-1.125,0.011,22.339,-29.846,24.719, 0.452,-1.147,0.012,23.132,-34.485,27.832, 0.438,-1.164,0.043,23.499,-39.063,31.677, 0.421,-1.162,0.071,24.780,-43.030,35.828, 0.410,-1.156,0.075,25.085,-46.265,39.490, 0.392,-1.150,0.067,25.269,-48.645,42.297, 0.375,-1.138,0.065,27.100,-52.063,45.227, 0.365,-1.128,0.063,30.701,-56.396,48.279, 0.379,-1.143,0.054,36.316,-60.852,50.476, 0.390,-1.178,0.045,42.053,-65.979,52.368, 0.386,-1.221,0.065,42.480,-70.618,56.458, 0.382,-1.239,0.112,39.001,-71.777,63.538, 0.388,-1.207,0.153,33.325,-68.359,72.510, 0.409,-1.148,0.154,27.893,-62.134,81.665, 0.441,-1.076,0.125,24.231,-56.641,89.111, 0.475,-1.017,0.095,25.940,-55.542,93.933, 0.473,-0.999,0.098,30.396,-58.289,96.497, 0.484,-1.002,0.121,34.241,-61.096,96.497, 0.489,-1.035,0.122,34.485,-60.791,96.497, 0.517,-1.074,0.115,35.583,-61.340,97.534, 0.529,-1.103,0.120,37.048,-64.636,100.586, 0.546,-1.135,0.144,35.889,-68.298,104.553, 0.549,-1.135,0.169,33.081,-71.350,111.084, 0.543,-1.081,0.200,27.893,-73.303,122.131, 0.538,-1.008,0.234,24.719,-74.585,134.583, 0.539,-0.925,0.266,21.973,-75.317,146.423, 0.542,-0.843,0.305,19.836,-75.562,155.151, 0.545,-0.790,0.340,19.348,-75.684,160.095};
+//float sup[714] = {0.679,0.046,0.412,43.945,12.329,-69.275, 0.729,0.066,0.307,40.588,10.620,-76.294, 0.753,0.048,0.261,40.527,2.686,-78.979, 0.743,0.066,0.320,42.786,-9.094,-82.214, 0.681,-0.002,0.511,43.396,-16.235,-86.548, 0.661,-0.086,0.642,39.856,-8.606,-84.839, 0.699,-0.026,0.627,30.518,9.094,-73.547, 0.744,0.154,0.511,21.729,25.085,-69.214, 0.779,0.244,0.378,21.240,34.119,-75.439, 0.783,0.170,0.273,29.297,34.485,-88.562, 0.782,0.014,0.208,38.879,29.114,-100.830, 0.778,-0.126,0.152,48.523,19.531,-109.436, 0.770,-0.172,0.097,57.007,5.249,-115.051, 0.753,-0.162,0.051,70.007,-11.475,-120.972, 0.703,-0.228,0.074,89.233,-31.128,-128.967, 0.691,-0.358,0.084,110.107,-48.645,-135.559, 0.667,-0.497,0.102,124.207,-62.622,-137.878, 0.605,-0.637,0.170,130.005,-73.303,-137.451, 0.575,-0.699,0.195,131.958,-80.688,-134.827, 0.521,-0.802,0.278,142.517,-88.806,-132.141, 0.390,-1.071,0.591,160.828,-96.130,-126.160, 0.309,-1.337,0.782,174.561,-89.478,-110.229, 0.371,-1.435,0.676,185.913,-71.472,-82.642, 0.444,-1.337,0.450,199.280,-57.068,-52.307, 0.504,-1.227,0.261,213.501,-57.312,-33.569, 0.542,-1.188,0.112,227.844,-70.923,-30.945, 0.541,-1.238,-0.009,243.896,-92.407,-35.522, 0.494,-1.428,-0.018,263.123,-118.652,-38.940, 0.455,-1.635,0.020,275.024,-141.541,-35.583, 0.454,-1.640,0.003,279.114,-157.410,-19.409, 0.440,-1.481,-0.052,286.682,-172.302,-0.061, 0.398,-1.397,-0.071,300.110,-191.528,7.507, 0.381,-1.407,-0.088,316.895,-212.585,4.333, 0.346,-1.389,-0.188,346.924,-236.145,6.775, 0.279,-1.456,-0.260,395.264,-268.127,13.123, 0.173,-1.681,-0.344,448.914,-304.077,18.738, 0.103,-2.046,-0.627,503.052,-336.670,34.912, -0.006,-2.427,-0.881,536.255,-369.568,66.833, -0.066,-2.677,-1.043,515.442,-396.240,111.572, -0.133,-2.672,-1.267,475.769,-411.499,163.635, -0.153,-2.579,-1.346,432.373,-432.007,217.407, -0.236,-2.439,-1.350,423.950,-460.876,252.136, -0.365,-2.545,-1.180,423.950,-493.347,260.620, -0.447,-2.824,-0.881,418.945,-516.418,259.705, -0.550,-2.909,-0.577,402.283,-532.593,264.282, -0.624,-2.728,-0.320,368.164,-538.574,283.936, -0.664,-2.320,-0.106,329.712,-533.508,309.265, -0.680,-1.843,0.250,295.471,-519.470,323.425, -0.650,-1.508,0.776,269.653,-491.211,322.083, -0.516,-1.271,1.078,237.244,-442.566,311.157, -0.349,-1.034,1.164,199.707,-383.606,296.143, -0.183,-0.806,1.207,166.870,-329.712,277.954, -0.047,-0.634,1.156,131.287,-283.630,254.578, 0.062,-0.555,0.994,88.989,-241.699,229.675, 0.115,-0.491,0.796,45.776,-207.703,208.618, 0.123,-0.381,0.676,5.920,-183.472,192.078, 0.110,-0.289,0.622,-27.588,-167.175,176.636, 0.113,-0.122,0.549,-60.303,-153.564,153.748, 0.151,0.161,0.484,-93.384,-147.156,116.333, 0.165,0.133,0.510,-85.388,-144.470,89.661, 0.210,-0.047,0.604,-71.106,-142.883,62.805, 0.244,-0.120,0.694,-69.641,-141.602,31.555, 0.270,-0.198,0.770,-69.641,-131.653,19.470, 0.308,-0.189,0.795,-67.932,-116.638,17.517, 0.345,-0.142,0.788,-67.932,-100.708,17.334, 0.390,-0.093,0.768,-65.063,-84.106,18.921, 0.412,-0.070,0.783,-63.293,-71.167,22.156, 0.426,-0.091,0.827,-57.007,-58.716,27.527, 0.437,-0.122,0.868,-46.204,-46.509,30.090, 0.456,-0.113,0.895,-39.673,-34.302,33.936, 0.465,-0.048,0.906,-37.476,-22.522,38.757, 0.493,0.024,0.892,-37.170,-10.803,41.382, 0.514,0.101,0.851,-37.048,0.366,40.222, 0.548,0.155,0.792,-35.706,9.033,35.156, 0.574,0.202,0.741,-33.508,15.198,26.550, 0.588,0.233,0.730,-31.067,19.165,15.503, 0.591,0.228,0.745,-27.832,22.766,2.625, 0.601,0.207,0.758,-25.635,28.198,-9.766, 0.606,0.198,0.767,-27.161,35.034,-22.461, 0.615,0.184,0.752,-31.494,43.945,-35.278, 0.621,0.153,0.739,-36.072,54.382,-48.706, 0.612,0.094,0.746,-40.161,64.758,-61.646, 0.604,0.015,0.750,-44.922,74.707,-73.792, 0.602,-0.074,0.715,-51.636,84.167,-84.106, 0.603,-0.125,0.661,-62.317,93.018,-90.515, 0.590,-0.120,0.646,-74.524,99.182,-94.421, 0.567,-0.090,0.681,-83.801,104.614,-98.328, 0.531,-0.071,0.738,-90.088,110.474,-105.103, 0.493,-0.088,0.807,-97.351,119.995,-114.197, 0.465,-0.131,0.873,-109.009,136.230,-124.207, 0.434,-0.175,0.894,-128.967,158.630,-134.155, 0.438,-0.205,0.848,-155.334,184.875,-146.179, 0.434,-0.237,0.754,-185.059,209.412,-159.424, 0.434,-0.309,0.679,-217.407,229.004,-174.500, 0.424,-0.410,0.622,-254.578,246.460,-191.040, 0.410,-0.531,0.522,-296.570,262.756,-206.970, 0.443,-0.686,0.400,-343.628,276.245,-222.290, 0.456,-0.815,0.345,-395.081,284.119,-238.586, 0.472,-0.904,0.283,-447.510,290.466,-249.146, 0.490,-0.968,0.167,-503.784,295.837,-257.324, 0.510,-1.019,-0.034,-561.218,296.143,-269.287, 0.609,-1.144,-0.290,-615.112,286.865,-283.386, 0.709,-1.281,-0.493,-665.466,260.925,-290.039, 0.792,-1.398,-0.579,-707.825,223.389,-278.320, 0.805,-1.370,-0.495,-750.000,179.932,-249.146, 0.850,-1.211,-0.368,-783.752,139.893,-207.336, 0.848,-0.940,-0.195,-799.866,105.225,-157.166, 0.958,-0.697,-0.001,-787.964,69.336,-112.183, 0.742,-0.401,0.357,-764.771,31.433,-75.928, 0.900,-0.321,0.513,-700.012,3.845,-33.936, 0.868,-0.104,0.829,-659.485,-12.878,-6.775, 0.859,0.026,1.022,-617.065,-16.052,13.123, 0.854,0.144,1.194,-552.551,-16.907,32.410, 0.880,0.301,1.288,-485.291,-11.414,43.335, 0.876,0.452,1.320,-410.461,-5.188,43.274, 0.860,0.531,1.336,-340.454,0.549,36.438, 0.860,0.501,1.322,-278.748,8.850,22.644, 0.815,0.424,1.338,-222.534,19.531,6.531, 0.761,0.297,1.268,-168.396,32.532,-4.761};
+
+  float gesture_in[TOTAL_SAMPLES] ;
+  for (size_t i=0; i<TOTAL_SAMPLES; i++) gesture_in[i] = 0.0;
+
+  float aX=0.0, aY=0.0, aZ=0.0, gX=0.0, gY=0.0, gZ=0.0 ;
+  while ( sqrt(aX*aX + aY*aY + aZ*aZ) < ACC_LIMIT )  {
+    // Wait for the signals from accelerator and gyroscope 
+    if (IMU.accelerationAvailable() && IMU.gyroscopeAvailable()) {
+      // read the acceleration and gyroscope data
+      IMU.readAcceleration(aX, aY, aZ);
+      IMU.readGyroscope(gX, gY, gZ);
+
+      // insert the new data at the threshold index
+      gesture_in[START_INDEX + 0] = (aX + 4.0) / 8.0;
+      gesture_in[START_INDEX + 1] = (aY + 4.0) / 8.0;
+      gesture_in[START_INDEX + 2] = (aZ + 4.0) / 8.0;
+      gesture_in[START_INDEX + 3] = (gX + 2000.0) / 4000.0;
+      gesture_in[START_INDEX + 4] = (gY + 2000.0) / 4000.0;
+      gesture_in[START_INDEX + 5] = (gZ + 2000.0) / 4000.0;
+      
+    }
+  }
+  
+  int capturedSamples = START_INDEX + N_FEATURES;
+    // collect the remaining samples
+  while (capturedSamples+N_FEATURES <= TOTAL_SAMPLES) {
+    // wait for both acceleration and gyroscope data to be available
+    if (IMU.accelerationAvailable() && IMU.gyroscopeAvailable()) {
+      // read the acceleration and gyroscope data
+      IMU.readAcceleration(aX, aY, aZ);
+      IMU.readGyroscope(gX, gY, gZ);
+
+      // insert the new data
+      gesture_in[capturedSamples + 0] = (aX + 4.0) / 8.0;
+      gesture_in[capturedSamples + 1] = (aY + 4.0) / 8.0;
+      gesture_in[capturedSamples + 2] = (aZ + 4.0) / 8.0;
+      gesture_in[capturedSamples + 3] = (gX + 2000.0) / 4000.0;
+      gesture_in[capturedSamples + 4] = (gY + 2000.0) / 4000.0;
+      gesture_in[capturedSamples + 5] = (gZ + 2000.0) / 4000.0;
+
+      capturedSamples += N_FEATURES;
+    }
+  }
+
+  // inferencing
+  tensor<float> in({TOTAL_SAMPLES}); in.load(gesture_in); 
+  tensor<float> out = dnnc_model(in);
+
+  const char* GESTURES[] = {"\thello", "\thow are you?"};
+  Serial.print("Gesture prob. ");
+  Serial.println(out.to_string().c_str());
+
+  int prediction = out(0,0)<out(0,1);
+  // Print
+  Serial.print("\t\t\t PREDICTION :");
+  Serial.println(GESTURES[prediction]);
+  Serial.flush();
+
+  // LED
+  lightLED(prediction);
+}
+#else
+int main() {
+    float gesture_in[714] = {0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.48,0.20,0.49,0.53,0.49,0.47,0.47,0.18,0.49,0.52,0.49,0.48,0.46,0.16,0.48,0.52,0.49,0.49,0.45,0.15,0.47,0.51,0.49,0.50,0.45,0.16,0.47,0.51,0.49,0.52,0.45,0.20,0.47,0.50,0.49,0.54,0.46,0.26,0.48,0.50,0.49,0.56,0.46,0.32,0.50,0.50,0.48,0.57,0.48,0.37,0.52,0.50,0.48,0.58,0.49,0.41,0.53,0.49,0.48,0.58,0.50,0.44,0.55,0.49,0.48,0.58,0.52,0.46,0.56,0.49,0.48,0.58,0.53,0.48,0.57,0.49,0.48,0.58,0.54,0.49,0.59,0.49,0.48,0.58,0.54,0.50,0.60,0.49,0.48,0.58,0.55,0.52,0.62,0.48,0.48,0.58,0.55,0.54,0.63,0.48,0.49,0.58,0.55,0.57,0.63,0.48,0.49,0.58,0.56,0.60,0.62,0.48,0.50,0.59,0.56,0.63,0.60,0.49,0.51,0.59,0.56,0.67,0.58,0.49,0.52,0.59,0.56,0.70,0.55,0.49,0.52,0.58,0.57,0.72,0.53,0.50,0.53,0.57,0.58,0.74,0.51,0.50,0.53,0.56,0.58,0.74,0.50,0.50,0.52,0.55,0.59,0.74,0.49,0.50,0.52,0.53,0.59,0.73,0.48,0.51,0.51,0.52,0.59,0.70,0.49,0.51,0.50,0.51,0.59,0.66,0.51,0.51,0.50,0.50,0.60,0.63,0.52,0.51,0.49,0.49,0.60,0.61,0.53,0.51,0.49,0.49,0.60,0.59,0.54,0.51,0.49,0.49,0.60,0.58,0.53,0.51,0.50,0.49,0.60,0.58,0.52,0.51,0.50,0.49,0.60,0.58,0.51,0.51,0.50,0.49,0.61,0.58,0.51,0.50,0.50,0.49,0.61,0.58,0.52,0.50,0.50,0.49,0.61,0.57,0.52,0.50,0.49,0.49,0.61,0.56,0.53,0.51,0.49,0.49,0.61,0.56,0.53,0.51,0.50,0.49,0.61,0.55,0.52,0.51,0.50,0.48,0.61,0.53,0.53,0.51,0.50,0.48,0.61,0.52,0.53,0.51,0.49,0.48,0.61,0.52,0.53,0.51,0.49,0.48,0.62,0.51,0.53,0.51,0.50,0.48,0.62,0.51,0.53,0.51,0.50,0.48,0.62,0.51,0.54,0.50,0.50,0.49,0.62,0.51,0.54,0.50,0.50,0.49,0.62,0.51,0.55,0.50,0.50,0.49,0.62,0.52,0.55,0.50,0.50,0.49,0.62,0.52,0.56,0.50,0.50,0.49,0.62,0.52,0.56,0.50,0.50,0.49,0.62,0.53,0.56,0.50,0.50,0.49,0.63,0.54,0.56,0.50,0.50,0.49,0.62,0.55,0.55,0.50,0.50,0.49,0.62,0.55,0.53,0.50,0.51,0.49,0.62,0.54,0.52,0.50,0.51,0.48,0.61,0.52,0.52,0.50,0.50,0.48,0.61,0.51,0.52,0.50,0.50,0.48,0.61,0.50,0.53,0.50,0.50,0.48,0.61,0.50,0.53,0.50,0.50,0.48,0.61,0.49,0.54,0.50,0.50,0.48,0.62,0.50,0.54,0.50,0.50,0.48,0.61,0.50,0.54,0.50,0.50,0.48,0.61,0.50,0.54,0.49,0.50,0.48,0.61,0.49,0.53,0.49,0.50,0.48,0.61,0.49,0.52,0.49,0.50,0.48,0.61,0.49,0.53,0.50,0.50,0.48,0.61,0.48,0.53,0.50,0.50,0.48,0.61,0.48,0.54,0.50,0.50,0.48,0.61,0.49,0.55,0.50,0.50,0.48,0.61,0.49,0.56,0.49,0.50,0.48,0.61,0.50,0.56,0.49,0.50,0.48,0.61,0.52,0.56,0.49,0.50,0.48,0.61,0.52,0.55,0.49,0.50,0.48,0.60,0.52,0.52,0.49,0.51,0.48,0.60,0.51,0.51,0.49,0.51,0.47,0.60,0.49,0.52,0.50,0.50,0.47,0.59,0.48,0.54,0.50,0.49,0.46,0.59,0.46,0.57,0.51,0.49,0.46};
+    tensor<float> in({714});
+    in.load(gesture_in);
+
+    tensor<float> out =  dnnc_model(in);
+    std::cout << out.to_string() << "\n";
+    return 0;
+}
+#endif