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Posture Control

A project within the module "ubiqutous computing". The aim of the project is to create a system that controls the posture of a user when lifting heavy objects and gives suggestions for improvement. https://youtu.be/2RV6aqpohyI

Sub-Goals

  • Visualize back posture
  • Check if person is lifing something
  • Recognize a bad posture
  • Alert user by beeping or vibrating

Todo

  • Solder ESP8266 #1
  • Solder ESP8266 #2
  • Test FSR
  • Solder ESP8266 with FSR #3
  • make client.ino for fsr
  • Make client.ino work
  • Make server.ino work
  • Test with two clients
  • Test with three clients
  • Make server.ino work with webserver
  • Add status display for clients
  • Add beeper to foot https://www.arduino.cc/en/Tutorial/BuiltInExamples/toneMelody
  • Add beeper to back
  • Request status on boot for clients
  • Add function to send data to clients
  • Server.js should process data further
  • Calibrate MPU6050 Sensors
  • Calculate right angles from mpu data
  • Display the data on frontend
  • Look into neural networks for posture recognition
  • Improve webserver data handling
  • Smooth values from sensors
  • Use multiple axis
  • Find better method to time the training
  • Restrict training to 20 lines per file
  • Fixed null error in training
  • Visualize all raw data
  • Use visualisations to process data further

Architecture

Architecture

Folder Structure

  • ESP8266/Client -> The sketch for the ESP8266 which have the MPU6050 connected, connect to the AP and send the sensors data
  • ESP8266/Server -> The sketch for the ESP8266 which opens the wifi AP and listens for the websocket data
  • ESP8266/FSR -> The sketch for the ESP8266 which have the FSRs connected, connect to the AP and send the sensors data
  • Server -> The node.js server which get the data from the serial port connected to the Access Point ESP8266
  • src -> The parcel webserver files
  • data_analysis -> Everything about the data related stuff (mostly csv and jupyter notebooks)

Screenshot

Screenshot of the current version of the frontend Screenshot

Data Analysis

Data Recording

node server/record_data.js

Works best if you have a wireless keyboard/controller and bind a/b to one of the buttons.

  1. Connect all the sensors and servermcu to usb
  2. Wear the sensors
  3. Press 'a' or 'b' to start a recording
  4. Perform a motion
  5. Press 'a' or 'b' again to stop the recording

Data recording is done via the node js script. Mostly inspired by how charliegerad did it in her project: https://github.com/charliegerard/gestures-ml-js

Data Analysis

Main file is data_analysis/complete_set_analysis.ipynb. Takes all files from server/data, parse their names and reads them into a pandas dataframe.

Data analysis is heavily inspired by ATLVHEAD project and his detailed video about it: https://github.com/ATLTVHEAD/Atltvhead-Gesture-Recognition-Bracer