Context-Aware Human Activity Recognition (CAHAR) Dataset

This dataset consists of inertial, force, color, and LiDAR data collected from a novel sensor system. The system comprises three Inertial Measurement Units (IMUs) positioned on the waist and atop each foot, a color sensor on each outer foot, a LiDAR on the back of each shank, and a custom Force-Sensing Resistor (FSR) insole featuring 13 FSRs in each shoe. 20 participants wore this sensor system whilst performing 38 combinations of 11 activities on 9 different terrains, totaling over 7.8 hours of data. In addition, participants 1 and 3 performed additional activity-terrain combinations which can be used as a test set, or for additional training data. The intention with this dataset is to enable researchers to develop classification techniques capable of identifying both the performed activity, and the terrain on which it was performed. These classification techniques will be instrumental in the adoption of remote, real-environment gait analysis with increased accuracy, reproducibility, and scope for diagnosis.

本数据集由一套创新传感器系统收集的惯性、力、颜色和激光雷达数据构成。该系统包括三个固定在腰部及每只脚跟处的惯性测量单元（IMU）、每只外侧脚上的颜色传感器、每只小腿后侧的激光雷达以及每只鞋内的13个力感应电阻（FSR）组成的定制力感鞋垫。20名参与者佩戴该传感器系统，在9种不同地形上执行了11种活动的38种组合，累计数据时长超过7.8小时。此外，参与者1和3还进行了额外的活动-地形组合，这些组合可用于测试集或作为额外训练数据。本数据集的目的是为了使研究人员能够开发出能够识别所执行的活动及其所进行地形的分类技术。这些分类技术在提高远程、真实环境步态分析准确性、可重复性和诊断范围方面将发挥至关重要的作用。