SHB: Small: Cell Phone-Based Activity Tracking for Telehealth

SHB：小号：现代智能手机包含一个三轴加速度计，这使得有可能在所有三个空间维度上测量智能手机用户的加速度。现代数据挖掘方法允许从该加速度数据构建活动识别分类器，使得用户的身体活动（例如，步行、慢跑、站立等）可以从加速度计值自动推断。这个研究项目将建立一个活动识别系统，可以部署为一个可下载的手机应用程序。然后，用户将能够通过Web界面访问其活动的描述，并可以使用此信息来监控和改变其行为。因此，这项研究可以用来解决许多与健康有关的问题，导致身体活动不足。它还可以通过检测福尔斯跌倒并向护理人员提供自动通知来帮助解决其他健康相关问题，例如老年人跌倒。通过手机活动识别系统生成的数据还将使对活动水平和健康的大规模流行病学研究成为可能，而这在以前是不可能的，因为成本过高。建立一个成功的活动识别系统需要解决许多技术挑战。这项研究将需要改进的特征构造方法，用于将时间序列数据转换为适合于面向实例的分类算法的表示。它还需要评估用于执行大规模实时数据挖掘的替代架构，以确定应该在客户端上执行多少活动识别工作（即，智能电话）与集中式服务器。智能蜂窝电话用于执行可靠的活动识别的充分性也将在给定许多约束（例如，有限的电池寿命）。

SHB: Small: Cell Phone-Based Activity Monitoring for TelehealthAbstractContemporary smart cell phones contain a tri-axial accelerometer, which makes it possible to measure the acceleration of a smart phone user in all three spatial dimensions. Modern data mining methods permit the construction of an activity recognition classifier from this acceleration data, so that a user's physical activity (e.g., walking, jogging, standing, etc.) can be automatically inferred from the accelerometer values. This research project will build an activity recognition system that can be deployed as a downloadable cell phone application. Users will then be able to access a description of their activities via a web interface and can use this information to monitor and change their behavior. Thus this research can be used to address the many health-related problems that result from physical inactivity. It can also assist with other health-related problem, such as falling in the elderly, by detecting falls and providing automatic notification to caregivers. The data generated via the cell phone activity recognition system will also enable large scale epidemiological studies of activity levels and health that, due to prohibitive costs, were not previously possible.Building a successful activity recognition system will require addressing many technical challenges. This research will require improved feature construction methods for transforming time-series data into representations suitable for example-oriented classification algorithms. It will also require the evaluation of alternate architectures for performing wide-scale real-time data mining, in order to determine how much of the activity recognition work should be performed on the client (i.e., smart phone) versus a centralized server. The adequacy of smart cell phones for performing reliable activity recognition will also be evaluated given the many constraints (e.g., limited battery life) imposed by these devices.