# A Novel Dataset for Gait Activity Recognition in Real-World Environments

## TL;DR

This paper introduces a new dataset for gait analysis in real-world settings to improve fall risk assessment and wearable sensor technology.

## Contribution

The paper presents the first dataset combining activity and terrain labels for indoor and outdoor environments.

## Key findings

- The CAHAR dataset includes data from IMUs, FSR insoles, color sensors, and LiDARs.
- The dataset supports both human activity recognition and terrain classification.
- It aims to advance remote gait analysis through wearable sensors.

## Abstract

Falls are a prominent issue in society and the second leading cause of unintentional death globally. Traditional gait analysis is a process that can aid in identifying factors that increase a person’s risk of falling through determining their gait parameters in a controlled environment. Advances in wearable sensor technology and analytical methods such as deep learning can enable remote gait analysis, increasing the quality of the collected data, standardizing the process between centers, and automating aspects of the analysis. Real-world gait analysis requires two problems to be solved: high-accuracy Human Activity Recognition (HAR) and high-accuracy terrain classification. High accuracy HAR has been achieved through the application of powerful novel classification techniques to various HAR datasets; however, terrain classification cannot be approached in this way due to a lack of suitable datasets. In this study, we present the Context-Aware Human Activity Recognition (CAHAR) dataset: the first activity- and terrain-labeled dataset that targets a full range of indoor and outdoor terrains, along with the common gait activities associated with them. Data were captured using Inertial Measurement Units (IMUs), Force-Sensing Resistor (FSR) insoles, color sensors, and LiDARs from 20 healthy participants. With this dataset, researchers can develop new classification models that are capable of both HAR and terrain identification to progress the capabilities of wearable sensors towards remote gait analysis.