# CNN-based Speed Detection Algorithm for Walking and Running using Wrist-worn Wearable Sensors

arXiv: https://arxiv.org/abs/2006.02348 · HTML: https://mlsanity.com/paper/2006.02348/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [CovidAlert -- A Wristwatch-based System to Alert Users from Face Touching](https://mlsanity.com/paper/2112.00131/) (2021)
- [Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach](https://mlsanity.com/paper/2407.04986/) (2024)
- [Convolutional Neural Networks for Accurate Measurement of Train Speed](https://mlsanity.com/paper/2508.17096/) (2025)

## Abstract (arXiv)

In recent years, there have been a surge in ubiquitous technologies such as smartwatches and fitness trackers that can track the human physical activities effortlessly. These devices have enabled common citizens to track their physical fitness and encourage them to lead a healthy lifestyle. Among various exercises, walking and running are the most common ones people do in everyday life, either through commute, exercise, or doing household chores. If done at the right intensity, walking and running are sufficient enough to help individual reach the fitness and weight-loss goals. Therefore, it is important to measure walking/ running speed to estimate the burned calories along with preventing them from the risk of soreness, injury, and burnout. Existing wearable technologies use GPS sensor to measure the speed which is highly energy inefficient and does not work well indoors. In this paper, we design, implement and evaluate a convolutional neural network based algorithm that leverages accelerometer and gyroscope sensory data from the wrist-worn device to detect the speed with high precision. Data from 15 participants were collected while they were walking/running at different speeds on a treadmill. Our speed detection algorithm achieved 4.2% and 9.8% MAPE (Mean Absolute Error Percentage) value using 70-15-15 train-test-evaluation split and leave-one-out cross-validation evaluation strategy respectively.