How to Create Fitness Tracker Assignments Using MATLAB Tools
Creating your own fitness tracker might sound like something only tech giants or developers with years of experience can achieve. However, with the powerful tools offered by MATLAB Online and MATLAB Mobile, this kind of project becomes surprisingly accessible—even for students and beginners. A smartphone today contains a wealth of sensors, including GPS, accelerometers, gyroscopes, and more. When you combine these sensors with MATLAB’s data analysis and visualization capabilities, you can build your very own customized fitness tracker.
This guide walks you through everything from setting up your environment and recording sensor data, to building a model that calculates metrics like steps taken and distance traveled. Whether you’re doing this as part of a university project, looking for help with MATLAB assignment, or simply trying to deepen your understanding of applied mathematics and programming, the skills you gain here can form the foundation of future work in mobile computing, wearable tech, or data science.
Getting Started with MATLAB Mobile and MATLAB Online
To begin developing your fitness tracker, you’ll need two platforms: MATLAB Online and MATLAB Mobile. MATLAB Online allows you to run MATLAB directly in your web browser, eliminating the need for desktop installation and making your files accessible from anywhere via MATLAB Drive. On the other hand, MATLAB Mobile connects your smartphone’s sensors to your MATLAB environment, streaming real-world data into your scripts.
Once you’ve installed MATLAB Mobile from the app store, open the application and log in using your MathWorks credentials. Then, make sure to connect the app to the MathWorks Cloud. This enables seamless integration between the data collected on your phone and your MATLAB Online workspace.
After the connection is established, you can access your smartphone’s various sensors and begin collecting meaningful activity data.
Working with Smartphone Sensors in MATLAB
One of the most exciting aspects of this project is getting to work with the rich array of sensor data available on most smartphones. These sensors can include:
- GPS: Provides latitude, longitude, altitude, speed, and heading.
- Accelerometer: Measures changes in movement or velocity.
- Gyroscope: Detects orientation and angular movement.
- Orientation: Shows device rotation in degrees.
- Course and Speed: Useful for tracking direction and pace.
Understanding what each sensor provides and selecting the right ones is crucial. For example, if you want to measure steps and distance, GPS data (latitude and longitude) along with time can be immensely helpful. Meanwhile, acceleration data may help classify the type of movement (walking, running, etc.).
MATLAB Mobile makes accessing these sensors easy. By navigating to the “Sensors” panel and setting the data stream to “Log,” you ensure the information is recorded and stored as .mat files inside a folder called MobileSensorData in your MATLAB Drive.
Once your sensors are configured, start logging data and engage in a physical activity. You can walk around, jog, or perform different types of workouts. To make your model more comprehensive and reliable, record a wide range of movements so your algorithm can generalize better.
Accessing and Understanding Sensor Data in MATLAB Online
Once your activity is complete and the data is logged, go to MATLAB Online and open your MATLAB Drive. Inside the MobileSensorData folder, you will find the .mat files containing the sensor logs. You can load a specific file into your MATLAB workspace using the command:
load('filename.mat'); % replace filename with the name of your file
These logs are imported as timetables, which associate a timestamp with each row of data. This is a convenient format when working with time-series sensor data, allowing you to reference changes in location or acceleration over time.
To inspect the structure of the imported timetable, you can use:
Position.Properties
This command helps you understand what variables are included, such as latitude, longitude, and timestamp. You can preview a subset of your data like so:
first6 = Position(1:6,:);
If you need to extract numeric data without metadata (like timestamps), use:
positionTime = Position.Timestamp;
To work with elapsed time instead of absolute timestamps, consider converting the Timestamp array using a helper function like timeElapsed. If it is saved in your current folder or path, execute:
positionTime = timeElapsed(positionTime);
This makes it easier to plot changes in location or movement over time
Developing a Model to Track Steps from GPS Data
Let’s now design a simple but effective fitness model. The goal is to estimate the number of steps taken using GPS data. While this is only one method (and not the most accurate in all situations), it’s a good starting point for understanding how to process real-world sensor data into useful metrics.
The idea is straightforward: calculate the total distance traveled and divide it by an average stride length.
Step Calculation Formula
Number of Steps = Total Distance (ft) / Stride Length (ft/step)
For most people, a stride length of 2.5 feet is a reasonable approximation. But first, we need to compute the total distance. Since GPS logs latitude and longitude in degrees, we must convert these degrees into feet.
We use the Earth’s circumference (approximately 24,901 miles, or 131,477,280 feet) to convert degrees into linear distance:
DistanceTravelled(ft) = (DegreesTravelled / 360) * Earth's Circumference(ft)
MATLAB Code to Estimate Distance and Steps
Here’s how you can implement this model in MATLAB:
% Initialize VariablesearthCirc = 24901 * 5280; % Convert miles to feettotaldis = 0;stride = 2.5; % Average stride in feet% Extract latitude and longitudelat = Position{:, 'Latitude'};lon = Position{:, 'Longitude'};% Loop through all GPS pointsfor i = 1:(length(lat) - 1) lat1 = lat(i); lon1 = lon(i); lat2 = lat(i+1); lon2 = lon(i+1); % Calculate distance between two points diff = distance(lat1, lon1, lat2, lon2); dis = (diff / 360) * earthCirc; totaldis = totaldis + dis;end% Compute stepssteps = totaldis / stride;
In this loop, we calculate the distance between every pair of consecutive latitude and longitude values. Adding these small distances gives a much more accurate estimate of total movement, especially when the path isn't a straight line.
Visualizing Movement and Step Count
One of MATLAB’s biggest strengths is its ability to visualize data clearly and effectively. Once you’ve computed the number of steps and total distance, it's a good idea to present this information using a clean and informative plot.
Here’s a sample plotting routine that maps your route and shows a summary of your activity:
plot(lat, lon, '-r', lat(1), lon(1), '*g', lat(end), lon(end), '*b', 'LineWidth', 3, 'MarkerSize', 10);hold on;legend('Route', 'Start Point', 'End Point');xlabel('Latitude');ylabel('Longitude');title(sprintf('Workout Summary: You took %0.0f steps and moved %0.3f miles', steps, totaldis / 5280));hold off;
This plot shows the route in red, marks the start point in green, and the endpoint in blue. The legend and title provide quick context, making your fitness tracker more user-friendly.
Expanding the Model: Altitude, Calories, and Activity Type
Once the basic model is working, there’s no limit to how far you can take it. You could start by including altitude data to count flights of stairs climbed. Accelerometer and gyroscope data can help detect movement patterns, making it possible to differentiate between walking, running, or cycling.
Another valuable addition is the estimation of calories burned, which can be calculated using activity classification models and MET (Metabolic Equivalent of Task) values. You can even explore integrating machine learning techniques to build a more intelligent and responsive tracker.
If you’re comfortable with MATLAB App Designer, you can also build a user interface that lets users input their height, weight, stride length, and activity type to customize their experience further.
Final Thoughts
This project isn't just about creating a cool gadget—it’s about learning how to handle real-world data, apply mathematical modeling, and deliver insights in a meaningful way. With nothing more than a smartphone and MATLAB, you can build a fully functional fitness tracker that visualizes routes, counts steps, and opens the door to further data exploration.
Whether you're working on an academic assignment, exploring MATLAB’s capabilities, or developing a prototype for a future startup idea, this project allows you to gain practical experience in signal processing, sensor data fusion, and model development.
Use this opportunity to showcase your skills and creativity. Add more features, share your results, and explore what’s possible when MATLAB meets mobile technology.