7.8.20. Dynamic Hand Gesture Recognition

Real-time dynamic hand gesture classification from 3-axis accelerometer data on MSPM0G5187 with integrated NPU.

7.8.20.1. Overview

  • Task: Multi-class classification (4 gesture types)

  • Application: Gesture-based human-machine interface

  • Dataset: 3-axis accelerometer recordings from TI Sensor BoosterPack

  • Device: MSPM0G5187 (NPU-accelerated)

7.8.20.2. Gesture Classes

  1. Circle — Clockwise or counter-clockwise circular motion

  2. Wave — Side-to-side waving motion

  3. Tap — Short impact gesture

  4. Others — Non-gesture or unrecognized motion

7.8.20.3. Running the Example

cd tinyml-modelzoo
./run_tinyml_modelzoo.sh examples/dynamic_hand_gesture_recognition/config_MSPM0.yaml

7.8.20.4. Device Support

Device

Hardware

Configuration File

MSPM0G5187

MSPM0 with NPU + TI Sensor BoosterPack (3-axis accelerometer)

config_MSPM0.yaml

7.8.20.5. Configuration

common:
  task_type: 'generic_timeseries_classification'
  target_device: 'MSPM0G5187'

dataset:
  dataset_name: 'Hand_gesture_dataset'
  input_data_path: 'https://software-dl.ti.com/C2000/esd/mcu_ai/01_04_00/datasets/hand_gesture_dataset.zip'

data_processing_feature_extraction:
  feature_extraction_name: 'Input256_RAW_256Feature_1Frame_3InputChannel_removeDC_2D1'
  variables: 3

training:
  model_name: 'CLS_55k_NPU'
  training_epochs: 40
  batch_size: 30
  quantization: 2
  quantization_method: 'QAT'
  quantization_weight_bitwidth: 8
  quantization_activation_bitwidth: 8

testing:
  enable: True

compilation:
  enable: True

7.8.20.6. Dataset Description

  • Sensor: 3-axis accelerometer (X, Y, Z) from TI Sensor BoosterPack

  • Variables: X, Y, Z acceleration (variables: 3)

  • Frame size: 256 samples per frame

  • Stride: 0.25 (64-sample step between frames)

  • Normalization: Range normalization per frame

7.8.20.7. Feature Extraction

The example uses raw time-domain features (no FFT). Range normalization captures gesture amplitude and shape directly.

7.8.20.8. Available Models

Model

Parameters

Description

CLS_55k_NPU

~55,000

Default — CNN optimized for NPU, best accuracy

7.8.20.9. Expected Results

Accuracy: ~94.46% on test data
Device: MSPM0G5187 NPU

7.8.20.10. System Components

Hardware

Software

  • Code Composer Studio 12.x or later

  • MSPM0 SDK 2.10.04 or later

7.8.20.11. Next Steps