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https://github.com/philippmatthes/tmd
TFLite models and Flutter library for transport mode detection on mobile devices. (IEEE IoTaIS 2022)
https://github.com/philippmatthes/tmd
flutter flutter-library shl shl-dataset tmd transport-mode-detection
Last synced: 4 days ago
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TFLite models and Flutter library for transport mode detection on mobile devices. (IEEE IoTaIS 2022)
- Host: GitHub
- URL: https://github.com/philippmatthes/tmd
- Owner: PhilippMatthes
- License: mit
- Created: 2022-07-21T10:58:37.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-09T09:57:21.000Z (12 months ago)
- Last Synced: 2024-05-02T03:27:54.954Z (8 months ago)
- Topics: flutter, flutter-library, shl, shl-dataset, tmd, transport-mode-detection
- Language: Jupyter Notebook
- Homepage:
- Size: 7.72 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# Transport Mode Detection for Smartphones
TFLite Machine learning models trained for transport mode detection (TMD) on smartphones. This library provides a simple API to use these models in your Flutter app. The models were trained and evaluated on the SHL (Sussex-Huawei-Locomotion) dataset.
## Installing
Add this library to your `pubspec.yaml` via GitHub:
```yaml
dependencies:
tmd:
git:
url: https://github.com/philippmatthes/tmd
ref: main
```
## Example Usage
```dart
import 'package:tmd/tmd.dart';
void main() async {
final tmd = TMD();
final stream = await tmd.startListeningForChanges()
stream.listen((newMode) {
print(newMode); // New transport mode detected
});
}
```
There is also the option to specify how often the model should be evaluated, and how often the changed transport mode should be cross-checked before reporting the change.
```dart
import 'package:tmd/tmd.dart';
void main() async {
final tmd = TMD();
final stream = await tmd.startListeningForChanges(
// Notify only if a new transport mode was detected that many
// consecutive times. This will improve the stability at the
// expense of a less frequent notification.
nChangedForNotification: 5,
// The inference period defines how often the transport mode
// is inferred. This will also affect the notification frequency.
// Note that the inference period also impacts the battery life.
// It is recommended to not go below 0.5 seconds.
inferencePeriod: const Duration(milliseconds: 1000),
);
stream.listen((newMode) {
print(newMode); // New transport mode detected
});
}
```
## Designed for Low Resource Usage
Read our paper to learn more about how we optimized the model for low resource usage: [Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices](https://ieeexplore.ieee.org/abstract/document/9976004/).
## Known Issues
- Currently, the IMU sensor stream is kept active even when the transport mode detection does not need it.
- Transport mode "car" may be detected instead of "still" when the smartphone lays flat on the table.
- The detection may vary between devices depending on the sensor quality and sampling rate.
Please help us improve this library by cross-checking results and reporting any issues you encounter.
## TFLite Models Download
The model in the first row is the model used by default in this library.
| Model ID | Type | Pooling | Optimizer | Optimization | Depth | LR | Val. Acc. (%) | Download |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|4ad2e7|1d ResNetV1|max|adam|Pruning & Separable Layers|2|0.010|84.91|[Google Drive](https://drive.google.com/file/d/1-JH0K0vsLOuqiTp2PAIPoLXRUyvvk36p/view?usp=sharing)|
|4ad2e7|1d ResNetV1|max|adam|Pruning|2|0.010|84.38|[Google Drive](https://drive.google.com/file/d/1Dj7Op4LArlK0gnkGpqvbj6uetNlxNSL3/view?usp=sharing)|
|4ad2e7|1d ResNetV1|max|adam|-|2|0.010|84.34|[Google Drive](https://drive.google.com/file/d/1-CyUZTibwya5l2j2l_StHtYe_u2tOJ04/view?usp=sharing)|
|6af8a1|1d ResNetV1|max|adam|-|4|0.010|84.18|[Google Drive](https://drive.google.com/file/d/1-LkSVciKyGQkO6YyAqHazqArNhfgc5U3/view?usp=sharing)|
|7cfd66|1d ResNetV1|max|sgd|-|2|0.100|84.18|[Google Drive](https://drive.google.com/file/d/1-8kybhtQut8Mz-6zz0UfFssz_t_yqCGQ/view?usp=sharing)|
|968ca3|1d ResNetV1|max|sgd|-|2|0.100|84.03|[Google Drive](https://drive.google.com/file/d/1-1UjIR3Qs-2oZD49AVnbmPpeLG05iKDJ/view?usp=sharing)|
|e6c367|1d ResNetV1|avg|adam|-|3|0.001|83.69|[Google Drive](https://drive.google.com/file/d/1-1Uurz7PfFuw9-wdV-DfH7EQGEFgopZs/view?usp=sharing)|
|998822|1d ResNetV2|max|sgd|-|2|0.010|82.92|[Google Drive](https://drive.google.com/file/d/1-_hmv7INFEIGTRC2Lz7H8NJCEC22zFLI/view?usp=sharing)|
|752315|2d ResNetV2|avg|rmsprop|-|2|0.010|81.33|[Google Drive](https://drive.google.com/file/d/1-4n6KBRXdm2Dz-D6tZHM4NYsf-d40FSj/view?usp=sharing)|
|35d171|2d ResNetV1|avg|adam|-|2|0.010|81.07|[Google Drive](https://drive.google.com/file/d/1-8DgA9znYzIKrfPmHxaCgkz8VPFRaswu/view?usp=sharing)|
|ea7037|1d ResNetV1|avg|adam|-|3|0.001|81.01|[Google Drive](https://drive.google.com/file/d/1-ROstQ3WI8ZtCy8xDzDMg-UhFlT8wC8s/view?usp=sharing)|
|cb5048|2d ResNetV1|max|adam|-|2|0.010|78.96|[Google Drive](https://drive.google.com/file/d/1-00pwU2B3yer4jPU4Wp_oP2OIVCNwB_5/view?usp=sharing)|
All models were trained and evaluated on the SHL dataset. If any of the download links do not work, here is a backup: [Google Drive](https://drive.google.com/file/d/15XombGG_j4ngN7l-tNMKB4WnXqRv6G7o/view?usp=sharing). Please open an issue in this case.
## Citing this Work
```bibtex
@inproceedings{matthes2022selecting,
title={Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices},
author={Matthes, Philipp and Springer, Thomas},
booktitle={2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)},
pages={135--141},
year={2022},
organization={IEEE}
}
```