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:

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

Example Usage

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
  });
}

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.

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
4ad2e7	1d ResNetV1	max	adam	Pruning	2	0.010	84.38	Google Drive
4ad2e7	1d ResNetV1	max	adam	-	2	0.010	84.34	Google Drive
6af8a1	1d ResNetV1	max	adam	-	4	0.010	84.18	Google Drive
7cfd66	1d ResNetV1	max	sgd	-	2	0.100	84.18	Google Drive
968ca3	1d ResNetV1	max	sgd	-	2	0.100	84.03	Google Drive
e6c367	1d ResNetV1	avg	adam	-	3	0.001	83.69	Google Drive
998822	1d ResNetV2	max	sgd	-	2	0.010	82.92	Google Drive
752315	2d ResNetV2	avg	rmsprop	-	2	0.010	81.33	Google Drive
35d171	2d ResNetV1	avg	adam	-	2	0.010	81.07	Google Drive
ea7037	1d ResNetV1	avg	adam	-	3	0.001	81.01	Google Drive
cb5048	2d ResNetV1	max	adam	-	2	0.010	78.96	Google Drive

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.

Citing this Work

@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}
}