利用惯性信号的自动移动上下文检测
Automated Mobility Context Detection with Inertial Signals
摘要：
运动功能的远程监测是健康评估的有力方法，特别是在老年人群或受病理影响的受试者中，这些病理对他们的行走能力产生负面影响。可穿戴传感器设备的不断发展进一步支持了这一点，这些设备越来越小，越来越便宜，越来越节能。外部环境和移动环境对行走性能有影响，因此远程分析步态发作时的最大挑战之一是检测这些发作发生的环境的能力。本文的主要目标是调查上下文检测的日常运动功能的远程监控。我们的目标是了解是否惯性信号采样可穿戴加速度计，提供可靠的信息，分类步态相关的活动，无论是室内或室外。我们探索了两种不同的方法来完成这项任务：（1）使用步态描述符和从步行过程中采样的输入惯性信号中提取的特征，以及经典的机器学习算法，以及（2）将输入惯性信号视为时间序列数据并利用端到端的最先进的时间序列分类器。我们直接比较这两种方法，通过一组实验的基础上收集的数据，从9个健康的人。我们的结果表明，可以从惯性数据流成功推导出室内/室外背景。我们还观察到，时间序列分类模型比任何其他基于特征的模型都具有更好的准确性，同时保持了效率和易用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
