SBIR TOPIC 425 PHASE I: POSTURE ANALYSIS THROUGH MACHINE LEARNING (PATHML)

可穿戴技术的进步和低成本视频的可获得性具有巨大的潜力，可以为身体行为如何与健康相关提供新的见解，定义临床试验结果，并评估日常生活患者在家中或康复环境中的功能状态和活动。(1-4)摄像机和/或视频可以以被动和不显眼的方式连续记录，使参与者能够提供日常活动的详细记录，这在健康研究、记忆保持和人种学方面具有应用价值。然而，在健康研究中，图像处理的使用 仍然负担沉重，费用高昂，往往需要训练有素的工作人员手动注释。为了自动标注图像和视频，近年来，科学家们一直在将新兴的机器学习技术应用于计算机视觉。借助多层专用神经网络(卷积神经网络，递归神经网络)，研究人员 能够根据静态图像和视频帧中所描述的内容准确地对它们进行分类，识别图像中感兴趣对象的位置，识别图像中的人，并跨视频的多个连续帧跟踪对象(车辆、人)。(12-18)到目前为止，这项技术已经应用于商业产品和运动表现，但还没有量化 健康研究的体力活动、表现或行为水平。该项目的长期目标是开发一个商业现成(COTS)软件程序，该软件程序可以准确地对身体活动(例如，“走”、“坐”或“站”)、有关行为的信息(例如，活动的位置和目的)以及性能(例如，行走速度和坐-站转换时间)进行分类。

Advances in wearable technology and the availability of low-cost video have tremendous potential to provide new insight into how physical behavior is associated with health, define clinical trial outcomes and assess functional status and activities of daily living patients within their home or a rehabilitation setting. (1-4) Cameras and/or videos can record continuously in a passive and unobtrusive manner, enabling participants to provide a detailed record of daily activity that has applications in health research, memory retention and ethnography. (5-11) However, in health research the use of image processing remains burdensome and cost prohibitive, often requiring manual annotations by trained staff. To automate annotation of images and video in recent years scientists have been using emerging machine learning technology applied to computer vision. With the help of multi-layered special purpose neural networks (Convolutional Neural Networks, Recurrent Neural Networks) researchers have been able to accurately classify still images and video frames based on what is depicted in them, recognize the position of objects of interest in an image, recognize humans in an image, and track objects (vehicles, humans) across multiple consecutive frames of a video. (12-18) To date, this technology has been applied to commercial products and sport performance, but not to quantify levels of physical activity, performance or behavior for health research. The long-term goal of this project is to develop a Commercial Off-The-Shelf (COTS) software program that can accurately classify physical activities (e.g. ’walking’, ‘sitting’ or ‘standing up”), information about behavior (e.g., location and purpose of the activity), and performance (e.g., walking speed and sit to stand transition times).