Use of a robotic device to measure age-related decline in finger proprioception

Abstract

Age-related changes in proprioception are known to affect postural stability, yet the extent to which such changes affect the finger joints is poorly understood despite the importance of finger proprioception in the control of skilled hand movement. We quantified age-related changes in finger proprioception in 37 healthy young, middle-aged, and older adults using two robot-based tasks wherein participants' index and middle fingers were moved by an exoskeletal robot. The first task assessed finger position sense by asking participants to indicate when their index and middle fingers were directly overlapped during a passive crisscross movement; the second task assessed finger movement detection by asking participants to indicate the onset of passive finger movement. When these tasks were completed without vision, finger position sense errors were 48 % larger in older adults compared to young participants (p < 0.05); proprioceptive reaction time was 78 % longer in older adults compared to young adults (p < 0.01). When visual feedback was provided in addition to proprioception, these age-related differences were no longer apparent. No difference between dominant and non-dominant hand performance was found for either proprioception task. These findings demonstrate that finger proprioception is impaired in older adults, and visual feedback can be used to compensate for this deficit. The findings also support the feasibility and utility of the FINGER robot as a sensitive tool for detecting age-related decline in proprioception.

Keywords: Aging; Finger function; Joint position sense; Proprioception; Robotic evaluation.

Fig. 1

FINGER robot with two 8-bar finger curling mechanisms and two actuators that allow for naturalistic grasping motions by controlling the angle and position of the proximal phalanx and the position of the middle phalanx. The index and middle fingers attach to the robot and are guided through crisscross finger movements during the proprioception tasks; movement stops and reverses directions when fingers are separated at 30% of range of motion (defined by bold lines)

Fig. 2

Example index and middle finger movement paths during proprioception tasks generated by the FINGER robot. FINGER moved participants’ index and middle fingers in opposing directions to create crisscross motions. One crossover event occurred during each crisscross movement wherein the index and middle fingers were directly overlapping. The position in space where the crossover event occurred varied for each crisscross movement; to create this effect, the fingers alternated between symmetric and asymmetric movements. Each crisscross movement occurred over 5 seconds, followed by a pseudo-random 0-3 second pause. During the pause, index and middle fingers were separated at 30% of the ROM by FINGER. Varying finger velocity profiles and pseudo-random pause times created non-periodic crisscross movements that participants could not predict using timing strategies

Fig. 3

(A) Average absolute error, in degrees about the MCP, made on the overlap task for the dominant and non-dominant hands, with and without vision. The older age group performed significantly worse than the young and middle-aged groups for both the dominant and non-dominant hands without vision. However, no difference existed between the age groups when participants were permitted visual feedback of their hand. Numbers in parentheses indicate the number of participants tested for each condition. Error bars are standard error. * = p<0.05. ** = p<0.01. (B) Histogram distribution of signed average errors made on the overlap task

Fig. 4

Mean time to detect finger movement onset during the movement onset task for the dominant and non-dominant hands, with and without vision. The older age group performed significantly worse than the young age group for both the dominant and non-dominant hands without vision. However, no difference existed among the groups when participants were permitted visual feedback while completing the task. Error bars are standard error. ** = p<0.01