KINECAL

The field of human action recognition has made great strides in recent years, much helped by the availability of a wide variety of datasets that use Kinect to record human movement. Conversely, progress towards using Kinect in clinical practice has been hampered by the lack of appropriate data. In particular, datasets that contain clinically significant movements and appropriate metadata. This paper proposes a dataset to address this issue, namely KINECAL. It contains the recordings of 90 individuals carrying out 11 movements, commonly used in the clinical assessment of balance. The dataset contains relevant metadata, including clinical labelling, falls history and postural sway metrics. In addition, the recordings were made in both the lab and in informal settings.近年来，人体动作识别领域取得了长足进展，这在很大程度上得益于大量采用Kinect采集人体运动数据的数据集的问世。与之相对的是，Kinect在临床实践中的应用进展却因缺乏适配性数据而受阻，尤其是缺乏包含临床相关动作与规范元数据（metadata）的数据集。为此，本文提出了一款名为KINECAL的数据集以解决这一问题。该数据集收录了90名受试者完成11项动作的录制数据，这些动作均为临床平衡评估中的常用动作。数据集附带相关元数据，涵盖临床标注、跌倒史与姿势摆动（postural sway）指标。此外，所有录制数据均同时采集于实验室环境与非正式场景中。

搜集汇总

背景与挑战

背景概述

KINECAL是一个专注于临床平衡评估和跌倒风险预测的数据集，包含90名参与者执行11种常见临床动作（如静立、行走和坐站测试）的Kinect记录。其关键特点包括丰富的临床元数据（如跌倒历史、临床标签和姿势摇摆指标）以及多样化的记录环境（实验室和非正式设置），旨在为研究年龄相关变化和跌倒风险提供客观数据支持。