Education may play a key role in developing 'cognitive reserve' against neurodegenerative dementia. In this work, we investigate for the first time if handwriting dynamics can serve as a quantitative indicator of this reserve. We carried out an exploratory study involving a sample of mild cognitive impairment (MCI) subjects, with high and low education respectively, and a sample of healthy elder controls. We asked them to perform three complex handwriting tasks on a digitizing tablet: Drawing a clock; copying a check; writing a spontaneous sentence. Dynamic measures of the handwriting were then analyzed both with an unsupervised and a supervised machine learning approach. The results we obtained suggest that: (i) handwriting of MCI subjects with high reserve is quite similar to that of controls; (ii) handwriting of MCI subjects with lower reserve is easier to be distinguished from the other two. Dynamic handwriting analysis could provide a novel methodology to elucidate the still unknown mechanisms underlying brain resilience.
Angelillo, M. T., Impedovo, D., Pirlo, G., Sarcinella, L., Vessio, G., Handwriting dynamics as an indicator of cognitive reserve: an exploratory study, Abstract de <<2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019>>, (Bari, 06-09 October 2019 ), Institute of Electrical and Electronics Engineers Inc., Bari 2019:<<CONFERENCE PROCEEDINGS / IEEE INTERNATIONAL CONFERENCE ON SYSTEMS MAN AND CYBERNETICS>>,2019 835-840. 10.1109/SMC.2019.8914157 [http://hdl.handle.net/10807/149728]
Handwriting dynamics as an indicator of cognitive reserve: an exploratory study
Angelillo, Maria Teresa;
2019
Abstract
Education may play a key role in developing 'cognitive reserve' against neurodegenerative dementia. In this work, we investigate for the first time if handwriting dynamics can serve as a quantitative indicator of this reserve. We carried out an exploratory study involving a sample of mild cognitive impairment (MCI) subjects, with high and low education respectively, and a sample of healthy elder controls. We asked them to perform three complex handwriting tasks on a digitizing tablet: Drawing a clock; copying a check; writing a spontaneous sentence. Dynamic measures of the handwriting were then analyzed both with an unsupervised and a supervised machine learning approach. The results we obtained suggest that: (i) handwriting of MCI subjects with high reserve is quite similar to that of controls; (ii) handwriting of MCI subjects with lower reserve is easier to be distinguished from the other two. Dynamic handwriting analysis could provide a novel methodology to elucidate the still unknown mechanisms underlying brain resilience.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.