Fixed the bug of miss calculation during sensor data fusion and updated models

Author: hkxhrwang

Android/build.gradle

@@ -3,9 +3,10 @@
 buildscript {
     repositories {
         jcenter()
+        google()
     }
     dependencies {
-        classpath 'com.android.tools.build:gradle:2.3.3'
+        classpath 'com.android.tools.build:gradle:3.0.1'
 
         // NOTE: Do not place your application dependencies here; they belong
         // in the individual module build.gradle files
@@ -15,6 +16,7 @@ buildscript {
 allprojects {
     repositories {
         jcenter()
+        google()
     }
 }
 

Android/demo_myna/build.gradle

@@ -1,16 +1,8 @@
 apply plugin: 'com.android.application'
 
 android {
-    signingConfigs {
-        config {
-            keyAlias 'tdsdk'
-            keyPassword 'TDRocks!12'
-            storeFile file('/Users/xhrwang/work/tdsdktest.keystore')
-            storePassword 'TDRocks!12'
-        }
-    }
-    compileSdkVersion 25
-    buildToolsVersion "25.0.3"
+    compileSdkVersion 27
+    buildToolsVersion '27.0.3'
 
     defaultConfig {
         applicationId "com.talkingdata.myna.demo"
@@ -24,19 +16,17 @@ android {
     buildTypes {
         release {
             minifyEnabled true
-            signingConfig signingConfigs.config
             proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'
         }
         debug {
-            signingConfig signingConfigs.config
         }
     }
 }
 
 dependencies {
 //    compile fileTree(dir: 'libs', include: ['*.jar'])
-    compile 'com.android.support:appcompat-v7:25.3.1'
+    compile 'com.android.support:appcompat-v7:27.1.0'
     compile project(':lib-Myna')
-    compile 'com.android.support:multidex:1.0.1'
-    compile 'com.google.android.gms:play-services-awareness:11.0.0'
+    compile 'com.android.support:multidex:1.0.3'
+    compile 'com.google.android.gms:play-services-awareness:11.8.0'
 }

Android/demo_myna/src/main/java/com/talkingdata/myna/demo/DataScientistActivity.java

@@ -102,8 +102,8 @@ private void xgBoost(){
     }
 
     private void handHolding(){
-        HandHoldingClassifier xgBoostClassifier = new HandHoldingClassifier();
-        HandHoldingRecognizer handHoldingRecognizer = new HandHoldingRecognizer(xgBoostClassifier, new MyCallback());
+        HandHoldingClassifier classifier = new HandHoldingClassifier();
+        HandHoldingRecognizer handHoldingRecognizer = new HandHoldingRecognizer(classifier, new MyCallback());
         handHoldingRecognizer.setSamplingInterval(20);
         handHoldingRecognizer.addSensorType(Sensor.TYPE_GAME_ROTATION_VECTOR);
         DataScientistAPI.addRecognizer(handHoldingRecognizer);

Android/gradle.properties

@@ -1,17 +1,18 @@
-# Project-wide Gradle settings.
-
-# IDE (e.g. Android Studio) users:
-# Gradle settings configured through the IDE *will override*
-# any settings specified in this file.
-
+## Project-wide Gradle settings.
+#
 # For more details on how to configure your build environment visit
 # http://www.gradle.org/docs/current/userguide/build_environment.html
-
+#
 # Specifies the JVM arguments used for the daemon process.
 # The setting is particularly useful for tweaking memory settings.
-org.gradle.jvmargs=-Xmx1536m
-
+# Default value: -Xmx1024m -XX:MaxPermSize=256m
+# org.gradle.jvmargs=-Xmx2048m -XX:MaxPermSize=512m -XX:+HeapDumpOnOutOfMemoryError -Dfile.encoding=UTF-8
+#
 # When configured, Gradle will run in incubating parallel mode.
 # This option should only be used with decoupled projects. More details, visit
 # http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
 # org.gradle.parallel=true
+#Mon Dec 11 17:40:25 CST 2017
+systemProp.http.proxyHost=127.0.0.1
+org.gradle.jvmargs=-Xmx1536m
+systemProp.http.proxyPort=8090

Android/lib-Myna/build.gradle

@@ -1,8 +1,8 @@
 apply plugin: 'com.android.library'
 
 android {
-    compileSdkVersion 25
-    buildToolsVersion '25.0.3'
+    compileSdkVersion 27
+    buildToolsVersion '27.0.3'
 
     defaultConfig {
         minSdkVersion 14
@@ -19,6 +19,6 @@ android {
 }
 
 dependencies {
-    compile 'org.tensorflow:tensorflow-android:1.2.0'
-    provided 'com.google.android.gms:play-services-awareness:11.0.2'
+    compile 'org.tensorflow:tensorflow-android:1.6.0'
+    provided 'com.google.android.gms:play-services-awareness:11.8.0'
 }

Android/lib-Myna/src/main/assets/rhar.model

@@ -189,8 +189,8 @@ public void run() {
                         }
                         recognizer.dataSet[recognizer.getSamplingPointCount() / 2 + recognizer.dataSetIndex] = new SensorData();
                         recognizer.dataSet[recognizer.getSamplingPointCount() / 2 + recognizer.dataSetIndex].clone(latestSampledData);
-                        recognizer.dataSet[recognizer.dataSetIndex].timestamp = System.currentTimeMillis();
-                        Utils.calculateWorldAcce(recognizer.dataSet[recognizer.dataSetIndex]);
+                        recognizer.dataSet[recognizer.getSamplingPointCount() / 2 + recognizer.dataSetIndex].timestamp = System.currentTimeMillis();
+                        Utils.calculateWorldAcce(recognizer.dataSet[recognizer.getSamplingPointCount() / 2 + recognizer.dataSetIndex]);
                         if (recognizer.dataSetIndex == recognizer.getSamplingPointCount() / 2 - 1) {
                             try {
                                 recognizer.recognitionHandler.post(new Runnable() {

Android/lib-Myna/src/main/java/com/talkingdata/myna/MynaService.java

@@ -6,7 +6,7 @@ Android 平台情景感知框架
 
 
 ![](images/myna.png)
- 
+
 
 Myna 项目中包含一个测试 Demo 工程：demo-myna, 将该工程和 Myna 项目本身导入到 Android Studio 中，就可以开始调试了。
 
@@ -17,16 +17,17 @@ Myna 提供了两套接口：
 
 ## 更新 2017-07-12
 
-- 支持下面五种行为类型：
+- 支持下面 6 种行为类型：
 	1. Walking
 	2. Running
 	3. Bus
 	4. Subway
 	5. Car
 	6. Stationary
 - 新增 XGBoost 和基于 Tensorflow 的 LSTM 分类模型。
-	1. `Dataset` 目录下有数据集下载链接、数据处理脚本和 XGBoost 模型训练脚本。
-	2. 使用 LSTM 模型需要时需要提供训练好的模型和 Tensorflow 的动态库以及 Java Interface 包。Tensorflow 官方已经提供了通过 `AAR` 格式依赖 Tensorflow 动态库和 interface 的方法，Myna 已经更新使用该方式。具体请参考：[Android TensorFlow support](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md)。LSTM 预训练模型位于测试工程的 `assets` 目录下。由于训练数据量不够，该模型的准确率较低，仅可用于 End2End 方案验证，请勿用于生产环境。
+  1. `Dataset` 目录下有数据集下载链接、数据处理脚本和 XGBoost 模型训练脚本。
+  2. 使用 LSTM 模型需要时需要提供训练好的模型和 Tensorflow 的动态库以及 Java Interface 包。Tensorflow 官方已经提供了通过 `AAR` 格式依赖 Tensorflow 动态库和 interface 的方法，Myna 已经更新使用该方式。具体请参考：[Android TensorFlow support](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md)。LSTM 预训练模型位于测试工程的 `assets` 目录下。由于训练数据量不够，该模型的准确率较低，仅可用于 End2End 方案验证，请勿用于生产环境。
+- 增加手持状态检测。
 
 Myna 使用随机森林分类算法的一种开源实现--**Dice** 进行的实时用户行为识别：
 
@@ -72,7 +73,7 @@ ROC:
 开源协议： [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 
 	Copyright 2016 TalkingData
-	
+
 	Licensed 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