Kinematics of arm movements in elderly humans

Cooke, J. D.; Brown, Susan H.; Cunningham, D. A.

doi:10.1016/0197-4580(89)90025-0

Cited by 136 publications

(75 citation statements)

References 27 publications

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“…diculties in controlling muscle force and limb position precisely (Kinoshita and Francis 1996). For example, a larger force variation when trying to keep force constant (Galganski et al 1993), as well as a larger variability between repeated movements, especially when the movements are of small amplitude (Cooke et al 1989) have been found in elderly compared to younger subjects. Such age-related changes could be of signi®cance in performance (Smith et al 1999) and muscle load when carrying out computer tasks.…”

Section: Introductionmentioning

confidence: 98%

Performance and muscle activity during computer mouse tasks in young and elderly adults

Laursen¹,

Jensen²,

Ratkevičius³

2001

Eur J Appl Physiol

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The influence of age on performance and muscle activity was studied during computer mouse tasks designed to induce high demands on motor control. Eight young (mean age 25 years) and nine elderly (mean age 63 years) women participated. When the speed was self-determined, the elderly subjects performed 13%-18% slower than did the young. When speed was predefined, the error rate was higher in the elderly subjects than in the young ones (medium precision 7.8% compared to 2.5%, high precision 16.5% compared to 7.9%, respectively). The highest error rate was found for double-clicking (32.9% compared to 13.5%, respectively). The reduced performance in the elderly subjects was hypothesised to be a combined effect of deteriorated proprioception, increased motor unit size, and changes in the central nervous system. Electrical activity (EMG) was recorded from the forearm, shoulder and neck muscles. Higher levels of EMG activity were found in the elderly compared to the young. A likely explanation is that the impaired motor control necessitated an increased muscle activity. The highest levels of EMG activity and lack of EMG gaps were found for the forearm extensor muscles, especially the extensor digitorum muscle (mean EMG activity 10.4% compared to 8.1% of maximal electrical activity, EMGmax) whereas lower EMG activity levels were found for the shoulder region (e.g. right trapezius muscle mean EMG 2.8% compared to 1.1% EMGmax, respectively). The latter was possibly due to a relieving effect of the forearm support. Differences in muscle activity among the tasks were found, however they were minor for the shoulder and neck muscles. Consideration of the demands on motor control when designing user interfaces is recommended, to the benefit of both the young and the elderly.

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Section: Introductionmentioning

confidence: 98%

Performance and muscle activity during computer mouse tasks in young and elderly adults

Laursen¹,

Jensen²,

Ratkevičius³

2001

Eur J Appl Physiol

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“…The majority of the additional time to complete accuracy-constrained aiming movements is associated with the time after peak velocity, and typically the time between peak deceleration and the end of the movement (Cooke, Brown, & Cunningham, 1989;Darling, Cooke, & Brown, 1989;Goggin & Stelmach, 1990;Welsh, Higgins, & Elliott, 2007). This latter portion of the trajectory is usually associated with target control (Carlton, 1992;Chua & Elliott, 1993).…”

Section: Normal Agingmentioning

confidence: 99%

Goal-directed aiming: Two components but multiple processes.

Elliott

Hansen²,

Grierson

et al. 2010

Psychological Bulletin

354

312

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This article reviews the behavioral literature on the control of goal-directed aiming and presents a multiple-process model of limb control. The model builds on recent variants of Woodworth's (1899) two-component model of speed-accuracy relations in voluntary movement and incorporates ideas about dynamic online limb control based on prior expectations about the efferent and afferent consequences of a planned movement. The model considers the relationship between movement speed and accuracy, and how performers adjust their trial-to-trial aiming behavior to find a safe, but fast, zone for movement execution. The model also outlines how the energy and safety costs associated with different movement outcomes contribute to movement planning processes and the control of aiming trajectories. Our theoretical position highlights the importance of advance knowledge about the sensory information that will be available for online control and the need to develop a robust internal representation of expected sensory consequences. We outline how early practice contributes to optimizing strategic planning to avoid worst-case outcomes associated with inherent neural-motor variability. Our model considers the role of both motor development and motor learning in refining feed-forward and online control. The model reconciles procedural and representational accounts of the specificity-of-learning phenomenon. Finally, we examine the breakdown of perceptual-motor precision in several special populations (i.e., Down syndrome, Williams syndrome, autism spectrum disorder, normal aging) within the framework of a multiple-process approach to goal-directed aiming.

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“…Although there is some debate in the literature, it is generally accepted that in aiming or prehension tasks requiring a degree of accuracy and precision older adults take longer to complete their movements than younger adults, display a more asymmetric velocity profile; usually characterised by a lengthened deceleration phase, are more variable, and make more corrective movements at the end (Cooke, Brown and Cunningham 1989;Goggin and Stelmach 1990;Pratt et al 1994;Weir et al 1998). It is also evident that these differences between older and younger adults are exacerbated when task difficulty is increased e.g.…”

Section: Introductionmentioning

confidence: 99%

Eye and hand movement strategies in older adults during a complex reaching task

et al. 2015

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The kinematics of upper limb movements and the coordination of eye and hand movements are affected by aging. These age differences are exacerbated when task difficulty is increased, but the exact nature of these differences remains to be established.We examined the performance of 12 older adults (mean age = 74) and 11 younger adults (mean age = 20) on a multi-phase prehension task. Participants had to reach for a target ball with their preferred hand, pick it up and place it in a tray, then reach for a second target ball and place that in the same tray. On half the trials (stabilising condition) participants were required to hold the tray just above the surface of the table with their non-preferred hand and keep it as still as possible. Hand and eye movements were recorded. Older adults took longer to complete their movements and reached lower peak velocities than the younger adults. Group differences were most apparent in the stabilising condition, suggesting that the added complexity had a greater effect on the performance of the older adults than the young. During pick-up, older adults preferred to make an eye movement to the next target as soon as possible, but spent longer fixating the current target during placement, when accuracy requirements were higher. These latter observations suggest that older adults employed a task-dependent eye movement strategy; looking quickly to the next target to allow more time for planning and execution when possible, but fixating on their hand and successful placement of the ball when necessary.

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Kinematics of arm movements in elderly humans

Cited by 136 publications

References 27 publications

Performance and muscle activity during computer mouse tasks in young and elderly adults

Performance and muscle activity during computer mouse tasks in young and elderly adults

Goal-directed aiming: Two components but multiple processes.

Eye and hand movement strategies in older adults during a complex reaching task

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