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Abstract：
Machine learning models are being utilized to provide wearable sensor-based exercise biofeedback to patients undertaking physical therapy. However, most systems are validated at a technical level using lab-based cross validation approaches. These results do not necessarily reflect the performance levels that patients and clinicians can expect in the real-world environment. This study aimed to conduct a thorough evaluation of an example wearable exercise biofeedback system from laboratory testing through to clinical validation in the target setting, illustrating the importance of context when validating such systems. Each of the various components of the system were evaluated independently, and then in combination as the system is designed to be deployed. The results show a reduction in overall system accuracy between lab-based cross validation (>94%), testing on healthy participants (n = 10) in the target setting (>75%), through to test data collected from the clinical cohort (n = 11) (>59%). This study illustrates that the reliance on lab-based validation approaches may be misleading key stakeholders in the inertial sensor-based exercise biofeedback sector, makes recommendations for clinicians, developers and researchers, and discusses factors that may influence system performance at each stage of evaluation.
摘要：
机器学习模型被用于向进行物理治疗的患者提供可穿戴的基于传感器的运动生物反馈。然而,大多数系统在技术层面使用基于实验室的交叉验证方法进行验证。这些结果不一定反映患者和临床医生在现实环境中可以期望的性能水平。本研究旨在对从实验室测试到目标设置的临床验证的示例可穿戴运动生物反馈系统进行全面评估。说明验证此类系统时上下文的重要性。独立评估系统的各个组件，然后在系统设计部署时进行组合。结果显示，基于实验室的交叉验证之间的整体系统准确性降低(>94%)，在目标设置(>75%)中对健康参与者(n=10)进行测试，通过从临床队列收集的测试数据(n=11)(>59%)。这项研究表明，对基于实验室的验证方法的依赖可能会误导基于惯性传感器的运动生物反馈部门的关键利益相关者，为临床医生提出建议，开发人员和研究人员，并讨论了在每个评估阶段可能影响系统性能的因素。
