Automated Mobility Context Detection with Inertial Signals

TL;DR

This paper investigates the use of wearable inertial sensors to classify indoor versus outdoor walking contexts, demonstrating that time series models outperform feature-based methods in accuracy and efficiency.

Contribution

It compares feature-based and end-to-end time series classification approaches for context detection using inertial signals, showing the superiority of time series models.

Findings

Time series classifiers achieve higher accuracy than feature-based models.

Inertial data reliably distinguishes indoor from outdoor walking.

Time series models are efficient and easy to implement.

Abstract

Remote monitoring of motor functions is a powerful approach for health assessment, especially among the elderly population or among subjects affected by pathologies that negatively impact their walking capabilities. This is further supported by the continuous development of wearable sensor devices, which are getting progressively smaller, cheaper, and more energy efficient. The external environment and mobility context have an impact on walking performance, hence one of the biggest challenges when remotely analysing gait episodes is the ability to detect the context within which those episodes occurred. The primary goal of this paper is the investigation of context detection for remote monitoring of daily motor functions. We aim to understand whether inertial signals sampled with wearable accelerometers, provide reliable information to classify gait-related activities as either indoor or outdoor. We explore two different approaches to this task: (1) using gait descriptors and features extracted from the input inertial signals sampled during walking episodes, together with classic machine learning algorithms, and (2) treating the input inertial signals as time series data and leveraging end-to-end state-of-the-art time series classifiers. We directly compare the two approaches through a set of experiments based on data collected from 9 healthy individuals. Our results indicate that the indoor/outdoor context can be successfully derived from inertial data streams. We also observe that time series classification models achieve better accuracy than any other feature-based models, while preserving efficiency and ease of use.