■物理和职业治疗师为手动轮椅使用者提供常规护理,并负责培训和评估转移质量。如果使用不正确的坐姿枢轴技术,这些转移可能会在上肢关节上产生很大的负载。评估转让质量的方法包括转让评估工具,从定量生物力学特征衍生的临床验证的工具;然而,该工具的采用是低由于复杂的使用要求和典型的传输速度。
■本研究的目的是开发和验证计算机视觉和机器学习解决方案,以在临床环境中更好地实施转移评估仪器。
■原型系统,TransKinect,由红外深度传感器和自定义软件应用程序组成;可用性测试是由15名治疗师进行的,他们使用TransKinect进行了两次转移评估。熟练使用功能,可用性,通过经过验证的调查分析了可接受性和满意度,并从定性反馈中提取了主题。
■治疗师能够以86.7±5.4%的熟练程度成功完成转移质量评估。系统可用性量表(77.6±14.7%)和用户界面满意度问卷(83.5±8.7%)的总分表明该系统可用且令人满意。定性反馈表明,TransKinect是用户友好的,容易学习,而且潜力很大.
■结果支持TransKinect作为治疗师的潜在临床决策支持系统,用于全面评估独立转移技术。需要未来的研究来研究TransKinect在实际临床环境中的实用性和接受度。对康复的影响机器学习和计算机视觉可用于分析转移技术TransKinect是治疗师自动化分析的一种可用且用户友好的方法摘要报告和转移视频显示了临床使用的巨大潜力采用TransKinect可以提高手动轮椅使用者的护理质量。
UNASSIGNED: Physical and occupational therapists provide routine care for manual wheelchair users and are responsible for training and assessing the quality of transfers. These transfers can produce large loads on the upper extremity joints if improper sitting-pivot-technique is used. Methods to assess quality of transfers include the Transfer Assessment Instrument, a clinically validated tool derived from quantitative biomechanical features; however, adoption of this tool is low due to the complex usage requirements and speed of typical transfers.
UNASSIGNED: The objective of this study is to develop and validate a computer vison and machine learning solution to better implement the Transfer Assessment Instrument in clinical settings.
UNASSIGNED: The prototype system, TransKinect, consists of an infrared depth sensor and a custom software application; usability testing was carried out with fifteen therapists who performed two transfer assessments with the TransKinect. Proficiency in using features, usability, acceptability and satisfaction were analysed with validated surveys and themes were extracted from the qualitative feedback.
UNASSIGNED: The therapists were able to successfully complete the transfer quality assessments with 86.7 ± 5.4% proficiency. Total scores for System Usability Scale (77.6 ± 14.7%) and Questionnaire for User Interface Satisfaction (83.5 ± 8.7%) indicated that the system was usable and satisfactory. Qualitative feedback indicated that TransKinect was user-friendly, easy to learn, and had high potential.
UNASSIGNED: The results support TransKinect as a potential clinical decision support system for therapists for the comprehensive assessment of independent transfer technique. Future research is needed to investigate the utility and acceptance of TransKinect in real clinical environments. Implications for RehabilitationMachine learning and computer vision can be used to analyze transfer techniqueTransKinect is a usable and user-friendly means for therapists to automate analysisSummary reports and videos of transfers show high potential for clinical useAdoption of TransKinect can increase quality of care for manual wheelchair users.