IEEE J Biomed Health Inform - Component-Level Tuning of Kinematic Features from Composite Therapist Impressions of Movement Quality.

Tópicos

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Resumo

In this paper, we propose a general framework for tuning component-level kinematic features using therapists' overall impressions of movement quality, in the context of a Home-based Adaptive Mixed Reality Rehabilitation (HAMRR) system. We propose a linear combination of non-linear kinematic features to model wrist movement, and propose an approach to learn feature thresholds and weights using high-level labels of overall movement quality provided by a therapist. The kinematic features are chosen such that they correlate with the quality of wrist movements to clinical assessment scores. Further, the proposed features are designed to be reliably extracted from an inexpensive and portable motion capture system using a single reflective marker on the wrist. Using a dataset collected from ten stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment in HAMRR systems. The system is currently being deployed for largescale evaluations, and will represent an increasingly important application area of motion capture and activity analysis.

Resumo Limpo

paper propos general framework tune componentlevel kinemat featur use therapist overal impress movement qualiti context homebas adapt mix realiti rehabilit hamrr system propos linear combin nonlinear kinemat featur model wrist movement propos approach learn featur threshold weight use highlevel label overal movement qualiti provid therapist kinemat featur chosen correl qualiti wrist movement clinic assess score propos featur design reliabl extract inexpens portabl motion captur system use singl reflect marker wrist use dataset collect ten stroke survivor demonstr framework can reliabl use movement qualiti assess hamrr system system current deploy largescal evalu will repres increas import applic area motion captur activ analysi

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