Stroke is one of the most common adult injuries, with 6.5 million stroke survivors in the US alone. We use a novel motion capture system together with machine learning tools to evaluate the standard stroke rehabilitation scale, the Fugl-Meyer Assessment (FMA). FMA involves the patient performing specific motor actions. A medical professional rates the performance and provides an FMA score. We have developed a multi depth-camera system using off the shelf consumer depth cameras. Its novelty is in its ability to perform synchronization, data integration and most importantly, calibration on the fly automatically without the need of a professional operator. The camera system tracks the subject's body and outputs a stream of skeleton representations, which allows to evaluate the patients's motor performance. Using a multi camera system rather than a single camera allows capturing motion on all sides of the patient body, as required by the FMA. The system was evaluated in a pilot study at a major hospital. Applying machine learning techniques on the skeleton streams, the system was able to correctly asses FMA scores on 2 of the standard motions with close to 100% success rate. This serves as a proof of concept for the feasibility of creating a full FMA home based assessment tool.
|Title of host publication||2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 10 Oct 2018|
|Event||2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil|
Duration: 8 Jul 2018 → 13 Jul 2018
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Conference||2018 International Joint Conference on Neural Networks, IJCNN 2018|
|City||Rio de Janeiro|
|Period||8/07/18 → 13/07/18|
Bibliographical notePublisher Copyright:
© 2018 IEEE.
ASJC Scopus subject areas
- Artificial Intelligence