Abstract
Given its virtually algorithmic process, the Fugl-Meyer Assessment (FMA) of motor recovery is prone to automatization reducing subjectivity, alleviating therapists’ burden and collaterally reducing costs. Several attempts have been recently reported to achieve such automatization of the FMA. However, a cost-effective solution matching expert criteria is still unfulfilled, perhaps because these attempts are sensor-specific representation of the limb or have thus far rely on a trial and error strategy for building the underpinning computational model. Here, we propose a sensor abstracted representation. In particular, we improve previously reported results in the automatization of FMA by classifying a manifold embedded representation capitalizing on quaternions, and explore a wider range of classifiers. By enhancing the modeling, overall classification accuracy is boosted to 87% (mean: 82% ± 4.53:) well over the maximum reported in literature thus far 51.03% (mean: 48.72 ± std: 2.10). The improved model brings automatic FMA closer to practical usage with implications for rehabilitation programs both in ward and at home.
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Notes
- 1.
We are currently in the process of collecting further clinical data.
- 2.
Full abstraction from the sensing geometry is beyond the scope of this work. For instance, we do not aim at being capable of achieving our goal of automatic motor assessment from say thermal sensors.
- 3.
The definition of what is an interesting view of the dataset correspond to the domain demands.
- 4.
Previously reported values were well below these figures.
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The leading author has received a scholarship No. 339981 from CONACYT.
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Heyer, P., Orihuela-Espina, F., Castrejón, L.R., Hernández-Franco, J., Sucar, L.E. (2017). Sensor Abstracted Extremity Representation for Automatic Fugl-Meyer Assessment. In: Sucar, E., Mayora, O., Munoz de Cote, E. (eds) Applications for Future Internet. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-319-49622-1_17
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