Raspberry Pi tracks post-Covid activity levels

Covid affected all of us in different ways, but one thing we all shared was a complete disruption of our normal routines. The effects of these changes are still felt by many of us in terms of our mental health, sleep patterns, and physical activity.

post COVID activity monitoring
This is what the sensors can pick up

A team from the National Research Council of Italy noticed that the elderly population has been particularly affected by these changes, and wanted to devise something to monitor them. Maintaining routines which keep you active is especially important as you get older, and not being able to do so can have important consequences for health.

post COVID activity monitoring
Here’s how everything works together

The team created ambient and wearable sensors that detect posture and movement. They can recognise when a user is standing, sitting, bending and lying down, walking, or doing other physical activity. Data is sent via USB and Bluetooth to a Raspberry Pi 4, which processes it to quantify energy expenditure using Python scripts.

post COVID activity monitoring
This is the ambient sensor that works with the wearable sensor

The combined system comprises four types of sensors:

  • Tri-axial accelerometer: measures human movement based on vibrations across three axis points
  • Tri-axial gyroscope: works in tandem with the tri-axial accelerometer to sense what the wearer is doing
  • Magnetometer: a body-mounted magnetic field sensor that detects movement
  • Pressure and temperature sensor: does what it says on the tin
post COVID activity monitoring
This is the wearable sensor that works with the ambient sensor

Mood-monitoring upgrades

It’s also possible to evaluated heart-rate and breathing in order to indicate the mood and stress level of the sensor wearer. The team believes these factors are of paramount importance in the overall health of older people, because they significantly affect lifestyle. They plan to upgrade their creation with these capabilities in order to better assess the likelihood of onset of disorders or diseases.

post COVID activity monitoring
This is the Raspberry Pi 4 setup used to parse all the data

This research was conducted by Alessandro Leone, Gabriele Rescio, Giovanni Diraco, Andrea Manni, Pietro Siciliano, and Andrea Caroppo from the National Research Council of Italy. Their full research paper has been made available for free.

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