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

TL;DR

This paper presents a CNN-based algorithm that accurately detects walking and running speeds using wrist-worn accelerometer and gyroscope data, offering an energy-efficient alternative to GPS for indoor and outdoor activity monitoring.

Contribution

The study introduces a novel CNN approach leveraging wrist sensor data to estimate speed with high accuracy, improving indoor activity tracking over GPS-based methods.

Findings

Achieved 4.2% MAPE with train-test split.

Achieved 9.8% MAPE with leave-one-out validation.

Demonstrated high precision in speed detection using wrist sensors.

Abstract

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.