Objective and reliable assessment of motor functions, such as dexterity, is a key point for evaluating worker’s abilities. In this context, the proposed work presents a tool for objective automatic assessment of the Minnesota Dexterity Test using cameras with depth sensors. Typical performance measurements (i.e., total time and associated percentiles) were estimated using custom algorithms. In addition, the possibility to identify the qualifiers for the code d440 of the International Classification of Functioning, Disability and Health was implemented in the developed algorithms. The proposed tool can also identify the mistakes most frequently committed by the subjects. To prove the capabilities of the proposed method, a series of experimental trials was conducted with 10 healthy young volunteers. Results showed that the developed tool helps clinicians to obtain performance feedback and evaluate patients’ dexterity quickly without bias.
Automatic Outcomes in Minnesota Dexterity Test Using a System of Multiple Depth Cameras / Caporaso, T.; Sanseverino, G.; Krumm, D.; Grazioso, S.; D'Angelo, R.; Di Gironimo, G.; Odenwald, S.; Lanzotti, A.. - (2023), pp. 286-293. (Intervento presentato al convegno International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing, JCM 2022 tenutosi a ita nel 2022) [10.1007/978-3-031-15928-2_25].
Automatic Outcomes in Minnesota Dexterity Test Using a System of Multiple Depth Cameras
Caporaso T.
Primo
;Grazioso S.;Di Gironimo G.;Lanzotti A.Ultimo
2023
Abstract
Objective and reliable assessment of motor functions, such as dexterity, is a key point for evaluating worker’s abilities. In this context, the proposed work presents a tool for objective automatic assessment of the Minnesota Dexterity Test using cameras with depth sensors. Typical performance measurements (i.e., total time and associated percentiles) were estimated using custom algorithms. In addition, the possibility to identify the qualifiers for the code d440 of the International Classification of Functioning, Disability and Health was implemented in the developed algorithms. The proposed tool can also identify the mistakes most frequently committed by the subjects. To prove the capabilities of the proposed method, a series of experimental trials was conducted with 10 healthy young volunteers. Results showed that the developed tool helps clinicians to obtain performance feedback and evaluate patients’ dexterity quickly without bias.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
