# “Speed”: A Dataset for Human Speed Estimation

## TL;DR

This paper introduces a new dataset for estimating human walking and running speeds using wearable sensors and benchmarks a deep learning model that outperforms existing methods.

## Contribution

The paper introduces the 'Speed' dataset and proposes a deep CNN (SpeedNet) that achieves state-of-the-art accuracy and real-time performance for speed estimation.

## Key findings

- SpeedNet achieves an average RMSE of 0.4819 km/h in subject-independent testing.
- After adaptation with subject-specific data, SpeedNet's RMSE drops to 0.1747 km/h.
- SpeedNet outperforms existing methods in both accuracy and computational efficiency.

## Abstract

Over the years, researchers have developed several speed estimation techniques using wearable inertial measurement units (IMUs). In this paper, we introduce a medium-scale dataset, containing measurements of walking/running at speeds ranging from 4.0 km/h (1.11 m/s) to 9.5 km/h (2.64 m/s) in increments of 0.5 km/h (0.14 m/s) from 33 healthy subjects wearing IMUs. We name it the “Speed” dataset. In summary, we present accelerometer and gyroscope data from 12 speeds and 22 subject-independent sets with the full range of 12 speeds. The data in each set consists of overlapping sections of 250 time samples (corresponding to 2.5 s, sampled at 100 Hz), and six dimensions (corresponding to the three axes of the accelerometer and three axes of the gyroscope). Each speed set contains 1775 examples. We benchmark the existing approaches used in the literature for the purpose of speed estimation on this dataset. These include support vector regression, Gaussian Process Regression, and shallow neural networks. We then design a deep Convolutional Neural Network (CNN), SpeedNet, for baseline results. The proposed SpeedNet yields an average Root Mean Square Error (RMSE) of 0.4819 km/h (0.13 m/s), following a subject-independent approach. Then, the SpeedNet obtained from the subject-independent approach are adapted using a portion of subject-specific data. The average RMSE for the remainder of the data for all subjects then drops down to 0.1747 km/h (0.05 m/s). The suggested SpeedNet yields a lower RMSE in comparison to the other approaches. In addition, we also compare the proposed method to others in terms of the average testing time, to give an idea of computational complexity. The proposed SpeedNet, despite being more accurate, yields real-time performance.