https://github.com/utensor/adl_demo

https://github.com/utensor/adl_demo

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/utensor/adl_demo
• Owner: uTensor
• License: apache-2.0
• Created: 2018-05-03T15:10:57.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-09-13T01:47:38.000Z (over 7 years ago)
• Last Synced: 2025-04-19T15:21:29.199Z (11 months ago)
• Language: C++
• Size: 1.6 MB
• Stars: 9
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ADL

  uTensor enables motion recognition on microcontrollers. The model is trained with a modified Activity of Daily dataset recognizing 5 classes: 

  

  - Walking

  - Climbing

  - Activities

  - Descending

  - Resting

  

  The project is also a reference implementation of sequential data processing with Mbed and uTensor.

  

  ![Board and grove shield and Accelerometer](/docs/images/boardResting.jpg)

For sensor setup, please refer to [Train/HMP_Dataset/MANUAL.txt](https://github.com/neil-tan/ADL_demo/blob/master/Train/HMP_Dataset/MANUAL.txt). The grove sensor is place flat on the back of user's right hand, with the connector socket oriented furthest away from the wrist.

## Hardware requirement:

  - Mbed F413ZH board

  - Grove Sheild

  - Grove 3D digital accelerometer

## Build Instruction

- Recommend [cloud9 environment](https://github.com/uTensor/cloud9-installer)

- Run:

```

$ mbed import https://github.com/uTensor/ADL_demo

$ cd ADL_demo

$ mbed compile -m DISCO_F413ZH -t GCC_ARM --profile=uTensor/build_profile/release.json

```

- Ensure the Grove sensor is connected

- Locate the binary path from the terminal output, and flash it onto the board

## Training

For Training Instruction, please see [Train/README.md](https://github.com/neil-tan/ADL_demo/blob/master/Train/README.md)