Development of TransKinect: A Clinically Robust System for Transfer Assessment

不当的转移技术容易使轮椅使用者出现上臂疼痛和受伤。一 关于避免外力的适当转移方法的大量研究已经存在，但是这些技术 并没有很好地传播到诊所。在最近的一项研究中，我们发现高达63%的退伍军人使用 轮椅有许多转移技能的缺陷，包括不正确的手臂定位，使用手柄，和头部， Hips关系我们建议开发TransKinect，这是一种用于临床的自动转移评估系统， 可以帮助治疗师及其患者识别不正确的转移动作并提供指导的设置 如何提高他们的技术。该系统将基于转让评估工具（TAI） 这是一个有效和可靠的规模用于评估质量的转移技术，但需要新的治疗师 有相当多的时间来学习和判断适当的身体定位和力学。 TransKinect将消除对TAI背景知识和培训的需求， 使用便携式低成本无标记运动捕捉传感器测量身体和关节运动。于过往 在工作中，我们为TransKinect开发了机器学习分类器，可以区分正确和不正确 使用在传输过程中记录的传感器数据，该技术的平均准确度为94%。这些分类器 将被嵌入系统软件中，该软件被设计成“监视”转会，自动计算TAI分数， 实时呈现结果，并向治疗师及其同事提供教育和培训建议。 患者 具体目标是：1）迭代开发TransKinect软件平台， 专家小组，2）通过比较系统生成的TAI评分，验证TransKinect的临床应用 与专家治疗师进行对比，3）与一组新手治疗师一起测试TransKinect的可用性，4） 在VA临床环境中现场测试系统，以检查其实用性、可用性和有效性。TransKinect将成为 使用在研究的每个目标中收集的数据迭代地细化。 对于Aim 1，将开发TransKinect的原型，并与熟悉 泰对于目标2，三名专家治疗师和TransKinect将在观看30名退伍军人后对TAI进行评分 提供轮椅接送服务。我们的目标是两套评分之间至少达到80%的一致性 并会在有需要时，利用为此目的而收集的资料，改善有关软件。对于目标3，10名新手治疗师将 使用TransKinect评估模型患者，并提供他们对可用性和感知的反馈 系统的实用性。我们假设治疗师会发现TransKinect易于安装和使用， 它增加了他们对转移技术的认识和理解。在Aim 4中，TransKinect系统 将被引入到临床实践中克莱门特J. Zablocki VA医疗中心，我们将跟踪 系统的实用性、可用性和有效性。将通过比较患者的TAI评分来评价有效性 在诊所的初始和后续访问中进行评估。我们希望证明这个系统可以 成功融入标准实践，并对检测转移技术的改进敏感。

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.