Unravelling socio-motor biomarkers in schizophrenia
di Bernardo, M
Nature Publishing Group
We present novel, low-cost and non-invasive potential diagnostic biomarkers of schizophrenia. They are based on the “mirror-game”, a coordination task in which two partners are asked to mimic each other’s hand movements. In particular, we use the patient’s solo movement, recorded in the absence of a partner, and motion recorded during interaction with an artificial agent, a computer avatar or a humanoid robot. In order to discriminate between patients and controls we employ statistical learning techniques, which we apply to nonverbal synchrony and neuromotor features derived from the participants’ movement data. The proposed classifier has 93% accuracy and 100% specificity. Our results provide evidence that statistical learning techniques, nonverbal movement coordination and neuromotor characteristics could form the foundation of decision support tools aiding clinicians in cases of diagnostic uncertainty.
This work was funded by the European Project AlterEgo FP7 ICT 2.9 – Cognitive Sciences and Robotics, Grant Number 600610. The research of K.T-A was supported by grants EP/L000296/1 and EP/N014391/1 of the Engineering and Physical Sciences Research Council (EPSRC).
This is the author accepted manuscript. The final version is available from Nature Publishing Group via the DOI in this record.
Vol. 3, Art. No. 8