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GMP HAR model

Use case : Human activity recognition

Model description

GMP is an acronym for Global Max Pooling. It is a convolutional neural network (CNN) based model that uses Global Max Pooling before feeding the data to the fully-connected (Dense) layer for performing the human activity recognition (HAR) task based on the accelerometer data. It uses the 3D raw data with gravity rotation and supression filter as preprocessing. This is a very light model with very small foot prints in terms of FLASH and RAM as well as computational requirements.

This network supports any input size greater than (3 x 3 x 1) but we recommend to use at least (24 x 3 x 1), i.e. a window length of 24 samples. In this folder we provide GMP models trained with two different window lenghts [24 and 48].

The only input required to the model is the input shape and the number of output classes.

In this folder you will find different copies of the GMP model pretrained on a public dataset (WISDM) and a custom dataset collected by ST (mobility_v1).

Network information (for WISDM at wl = 24)

Network Information Value
Framework TensorFlow
Params 1,528

Network inputs / outputs

For a frame of resolution of (wl x 3) and P classes

Input Shape Description
(1, wl, 3, 1) Single ( wl x 3 x 1 ) matrix of accelerometer values, wl is window lenght, for 3 axes and 1 is channel in FLOAT32.
Output Shape Description
(1, P) Per-class confidence for P classes in FLOAT32

Recommended platforms

Platform Supported Recommended
STM32L4 [x] []
STM32U5 [x] [x]

Performances

Training

../../src/user_config.yaml

To train a gmp model, you need to configure the user_config.yaml file following the tutorial under the training section.

To train a GMP model, you need to configure the user_config.yaml file following the tutorial under the training section.

As an example, gmp_wl_24_config.yaml file is used to train this model on WISDM dataset. Users can copy its content in the user_config.yaml file provided under the human_acitivity_recognition/src directory to reproduce the results presented below.

Deployment

To deploy your trained model, you need to configure the user_config.yaml file following the tutorial in the deployment directory for WISDM dataset.

Metrics

Measures are done with default STM32Cube.AI Dev Cloud version 9.1.0 and for target board B-U585I-IOT02A. In addition the configuration were enabled input / output allocated option and balanced as optimization choice.

The inference time is reported is calculated on STM32 board B-U585I-IOT02A running at Frequency of 160 MHz.

Reference memory footprint based on WISDM dataset (see Accuracy for details on dataset)

Model Format Input Shape Target Board Activation RAM (KiB) Runtime RAM (KiB) Weights Flash (KiB) Code Flash (KiB) Total RAM (KiB) Total Flash (KiB) Inference Time (ms) STM32Cube.AI version
GMP wl 24 FLOAT32 24 x 3 x 1 B-U585I-IOT02A 4.25 2.08 5.70 12.29 6.33 18.96 4.42 9.1.0
GMP wl 48 FLOAT32 48 x 3 x 1 B-U585I-IOT02A 8.83 2.08 5.70 12.29 10.91 18.96 10.64 9.1.0

Accuracy with mobility_v1 dataset

Dataset details: A custom dataset and not publically available, Number of classes: 5 [Stationary, Walking, Jogging, Biking, Vehicle]. (We kept only 4, [Stationary, Walking, Jogging, Biking]) and removed Driving, Number of input frames: 81,151 (for wl = 24), and 40,575 for (wl = 48).

Model Format Resolution Accuracy (%)
GMP wl 24 FLOAT32 24 x 3 x 1 94.08
GMP wl 48 FLOAT32 48 x 3 x 1 93.84

Confusion matrix for GMP wl 24 with Float32 weights for mobility_v1 dataset is given below.

plot

Accuracy with WISDM dataset

Dataset details: link , License CC BY 2.0 , Quotation[1] , Number of classes: 6 (we are combining Upstairs and Downstairs into Stairs and Standing and Sitting into Stationary), Number of samples: 45,579 (at wl = 24), and 22,880 (at wl = 48).

Model Format Resolution Accuracy (%)
GMP wl 24 FLOAT32 24 x 3 x 1 84.49
GMP wl 48 FLOAT32 48 x 3 x 1 87.05

Training and code generation

  • Link to training script: here training section
  • Link to STM32Cube.AI generation and deployment script: here

Demos

Integration in a simple example

Please refer to the generic guideline here.

References

[1] “WISDM : Human activity recognition datasets". [Online]. Available: "https://www.cis.fordham.edu/wisdm/dataset.php".