  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.
