Development of TransKinect: A Clinically Robust System for Transfer Assessment

TransKinect 的开发：用于转移评估的临床稳健系统

• 批准号: 10377345
• 负责人: Alicia M. Koontz
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10377345
• 关键词: 3-Dimensional; Activities of Daily Living; Agreement; Area; Arm Injuries; Arm Pain; Assessment tool; Awareness; Clinic; Clinical; Cohort Studies; Communities; Computer software; Cumulative Trauma Disorders; Data; Development; Documentation; Education; Educational process of instructing; Effectiveness; Evaluation; Feedback; Freedom; Goals; Head; Healthcare; Heart; Hip region structure; Home; Injury; Joints; Knowledge; Learning; Measurement; Measures; Medical center; Modeling; Motion; Outcome; Pain; Patient Transfer; Patients; Performance; Physical Medicine; Positioning Attribute; Psychological Transfer; Quality of life; Recommendation; Reporting; Research; Software Design; Surface; Surveys; System; Techniques; Testing; Textbooks; Time; Training; Training and Education; Transferable Skills; Upper Extremity; Upper arm; Veterans; Visit; Wheelchairs; Work; active lifestyle; arm; base; body mechanics; body position; clinical practice; commercialization; cost; design; evidence base; experience; field study; follow-up; graphical user interface; healthy lifestyle; improved; interactive therapy; machine learning classifier; portability; prototype; sensor; skills; software systems; tool; usability

Improper transfer technique predisposes wheelchair users to developing upper arm pain and injuries. A large body of research on the proper ways to transfer to avoid extraneous forces exists but these techniques have not been well disseminated into clinics. In a recent study we found that up to 63% of Veterans who use wheelchairs have many transfer skill deficits including improper arm positioning, use of handgrips, and head- hips relationship. We propose to develop TransKinect, an automated transfer assessment system for clinical settings that can help therapists and their patients to identify improper transfer motions and provide guidance on how to improve their technique. The system will be based on the Transfer Assessment Instrument (TAI) which is a valid and reliable scale used to assess the quality of transfer technique but requires new therapists considerable time to learn and to make judgements on appropriate body positioning and mechanics. TransKinect will eliminate the need for background knowledge and training on the TAI and provide objective measurement of body and joint motions using a portable, low-cost markerless motion capture sensor. In prior work, we developed machine learning classifiers for TransKinect that can differentiate proper from improper techniques with an average accuracy of 94% using the sensor data recorded during a transfer. These classifiers will be embedded in system software designed to `watch' a transfer, automatically compute the TAI score, present the results in real-time and provide education and training recommendations to therapists and their patients. The specific aims are: 1) to iteratively develop the TransKinect software platform involving input from an expert panel, 2) to validate TransKinect for clinical use by comparing the TAI scores generated by the system against those of expert therapists 3) to test the usability of TransKinect with a novice group of therapists and 4) field test the system in a VA clinical setting to examine its utility, usability and effectiveness. TransKinect will be iteratively refined using the data that is collected in each aim of the study. For Aim 1, a prototype of TransKinect will be developed and tested with field experts familiar with the TAI. For Aim 2 three of the expert therapists and TransKinect will score the TAI after watching 30 Veterans perform wheelchair transfers. We aim to achieve at least 80% of agreement between the two sets of scores and will refine the software using data collected in this aim if necessary. For Aim 3, 10 novice therapists wlll use TransKinect to assess a model patient and provide their feedback on the usability and perceived usefulness of the system. We hypothesize that the therapists will find TransKinect easy to setup and use and that it increases their awareness and understanding about transfer technique. In Aim 4 the TransKinect system will be introduced into clinical practice at the Clement J. Zablocki VA Medical Center where we will track the system's utility, usability and effectiveness. Effectiveness will be evaluated by comparing TAI scores on patients who were assessed at an initial and followup visit to the clinic. We hope to show that the system can be successfully integrated into standard practice and is sensitive to detecting improvements in transfer technique.

不当转移技术使轮椅使用者易于发展上臂的疼痛和伤害。一个 关于转移的正确方法以避免外部力量存在的大量研究，但是这些技术 还没有很好地传播到诊所。在最近的一项研究中，我们发现多达63％的退伍军人使用 轮椅有许多转移技能缺陷，包括手臂定位不当，使用手夹和头部 - 臀部关系。我们建议开发Transkinect，这是一种自动转移评估系统的临床转移评估系统 可以帮助治疗师及其患者确定不当转移动议并提供指导的设置 关于如何改进他们的技术。该系统将基于转移评估工具（TAI） 这是用于评估转移技术质量的有效且可靠的规模，但需要新的治疗师 大量的学习和对适当的身体定位和机制做出判断。 Transinect将消除对TAI上背景知识和培训的需求，并提供目标 使用便携式，低成本无标记运动捕获传感器对身体和关节运动的测量。在先验 工作，我们开发了用于跨线的机器学习分类器，可以区分不当 使用传输过程中记录的传感器数据，平均准确度的平均精度为94％。这些分类器 将嵌入到系统软件中，旨在“观察”传输，自动计算TAI评分， 实时介绍结果，并向治疗师及其提供教育和培训建议 患者。 具体目的是：1）迭代开发涉及来自 专家小组，2）通过比较系统产生的TAI分数来验证临床使用的Trainkinect 反对专家治疗师3）测试与新手治疗师的跨金属可用性和4） 现场测试该系统在VA临床环境中检查其效用，可用性和有效性。 Transinect将是 使用在研究的每个目标中收集的数据进行迭代完善。 对于AIM 1，将与熟悉的现场专家开发和测试过跨线的原型 太极。对于AIM 2，三个专家治疗师和Transinect在观看30名退伍军人后将得分 执行轮椅转移。我们的目标是达到两组分数之间的至少80％的协议 并将在必要时使用此目标中收集的数据来完善软件。对于目标3，10名新手治疗师WLLL 使用Transinect评估模型患者，并提供有关可用性和可用性的反馈 系统的有用性。我们假设治疗师会找到易于设置和使用的跨线，并且 它提高了他们对转移技术的认识和理解。在AIM 4中，脱系系统 将在Clement J. Zablocki VA医疗中心引入临床实践 系统的实用性，可用性和有效性。将通过比较对患者的TAI评分来评估有效性 在对诊所的初步和后续访问中进行了评估。我们希望证明该系统可以 成功整合到标准实践中，对检测转移技术的改进很敏感。