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

SBIR 主题 425 第一阶段：通过机器学习进行姿势分析 (PATHML)

• 批准号: 10498198
• 负责人: VADIM KAGAN
• 金额: $ 39.99万
• 依托单位: SENTIMETRIX, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2022-06-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10498198
• 关键词: Activities of Daily Living; Architecture; Automated Annotation; Behavior; Categories; Clinical Trials; Computer Vision Systems; Computer software; Data; Data Collection; Data Set; Ethnography; Goals; Health; Health behavior; Home; Hour; Human; Human Activities; Image; Individual; Interview; Learning; Location; Machine Learning; Manuals; Methodology; Monitor; Outcome; Participant; Patients; Performance; Phase; Physical activity; Positioning Attribute; Posture; Process; Protocols documentation; Psychological Transfer; Rehabilitation therapy; Research; Research Personnel; Resolution; Resources; Scientist; Small Business Innovation Research Grant; Sports; Structure; Techniques; Technology; Time; Training; Walking; base; convolutional neural network; cost; functional status; human imaging; image processing; insight; interest; memory retention; neural network; open source; programs; prototype; recurrent neural network; walking speed; wearable device

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).

可穿戴技术的进步和低成本视频的可用性具有巨大的潜力，可以为身体行为与健康之间的关系提供新的见解，定义临床试验结果，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引人注目的方式连续记录，使参与者能够提供日常活动的详细记录，这在健康研究、记忆保留和民族志中有应用。（5-11）然而，在健康研究中使用图像处理