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.

Add this library to your pubspec.yaml via GitHub:

dependencies:
  tmd:
    git:
      url: https://github.com/philippmatthes/tmd
      ref: main

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.

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
  });
}

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.

• 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.

The model in the first row is the model used by default in this library.

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. Please open an issue in this case.

@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}
}