Classification of Human Movements with Motion Capture Data in a Motor Rehabilitation Context

• Luis Guilherme Silva Rodrigues Universidade do Estado de São Paulo - UNESP
• Diego Dias Universidade Federal de São João del-Rei - UFSJ
• Marcelo de Paiva Guimaraes Universidade Federal de São Paulo - UNIFESP
• Alexandre Fonseca Brandao Universidade de Campinas - UNICAMP
• Leonardo Rocha Universidade Federal de São João del-Rei - UFSJ
• Rogerio L. Iope Universidade do Estado de São Paulo - UNESP
• Jose Remo Ferreira Brega Universidade do Estado de São Paulo - UNESP

Resumo

Augmented and virtual reality can be used in motor or neuromotor rehabilitation clinics to make patients become more motivated and engaged with the treatment. The interaction with the applications stimulates the patient to exercise the impaired limb while enjoying the experience. This work takes the real-time tracking data generated from optical and wearable motion capture devices and uses it to feed machine learning algorithms. The data processing makes the movements with different durations consistent and enables the convergence of the models. Also, the data format is independent of the camera position and user. One of the experiments presented recognizes eight movements being executed in the system.

Palavras-chave: motion capture, supervised machine learning, Computer vision, augmented reality