An Agent-Specific Stochastic Model of Generalized Reaching Task Difficulty

• Main Authors: Andrea Lucchese, Salvatore Digiesi, Kübra Akbaş, Carlotta Mummolo
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Applied Sciences
• Subjects: reaching motor task; trajectory complexity; Index of Difficulty; gait variability; young and elderly gait; affordance
• Online Access: https://www.mdpi.com/2076-3417/11/10/4330

The ability of an agent to accomplish a trajectory during a certain motor task depends on the fit between external (environment) and internal (agent) constraints, also known as affordance. A model of difficulty for a generalized reaching motor task is proposed as an affordance-related measure, as perceived by a specific agent for a given environment and task. By extending the information-based Index of Difficulty of a trajectory, a stochastic model of difficulty is formulated based on the observed variability of spatial trajectories executed by a given agent during a repetitive motor task. The model is tested on an experimental walking dataset available in the literature, where the repetitive stride movement of differently aged subjects (14 “old” subjects aged 50–73; 20 “young” subjects aged 21–37) at multiple speed conditions (comfortable, ~30% faster, ~30% slower) is analyzed. Reduced trajectory variability in older as compared to younger adults results in a higher Index of Difficulty (slower: +24%, <i>p</i> < 0.0125; faster: +38%, <i>p</i> < 0.002) which is interpreted in this context as reduced affordance. The model overcomes the limits of existing difficulty measures by capturing the stochastic dependency of task difficulty on a subject’s age and average speed. This model provides a benchmarking tool for motor performance in biomechanics and ergonomics applications.