4e3095f218
- Shuffles the accelerometer data axes so that they match the training data orientation with the Arduino mounted as desired. - Replaced the run-based post-processing with averaging over time. - Reverted the model data back to the original version. - Replaced the output buffer resetting with a detection suppression window after a gesture is found. - Removed the wait on the serial port, and added LED indicators for 'ring' and 'slope' (which seem to be the most accurately-detected gestures). For an Arduino library zip file based off this change, see https://storage.googleapis.com/download.tensorflow.org/deps/tflite/arduino_library/tensorflow_lite_2020_02_09.zip PiperOrigin-RevId: 296528242 Change-Id: I5219484de8f003bdac5fdd75bd6ec65ee51006e2
39 lines
1.6 KiB
C++
39 lines
1.6 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
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#define TENSORFLOW_LITE_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
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// The expected accelerometer data sample frequency
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const float kTargetHz = 25;
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// What gestures are supported.
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constexpr int kGestureCount = 4;
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constexpr int kWingGesture = 0;
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constexpr int kRingGesture = 1;
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constexpr int kSlopeGesture = 2;
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constexpr int kNoGesture = 3;
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// These control the sensitivity of the detection algorithm. If you're seeing
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// too many false positives or not enough true positives, you can try tweaking
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// these thresholds. Often, increasing the size of the training set will give
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// more robust results though, so consider retraining if you are seeing poor
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// predictions.
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constexpr float kDetectionThreshold = 0.8f;
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constexpr int kPredictionHistoryLength = 5;
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constexpr int kPredictionSuppressionDuration = 25;
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#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
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