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. Prefix st_ denotes it is a variation of the model built by STMicroelectronics. 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
Metrics
Measures are done with default STEdge AI Dev Cloud version 3.0.0 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)
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).
Confusion matrix for st_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).
