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A multi-objective optimal control approach to motor strategy changes in older people with mild cognitive impairment during obstacle crossing
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 200 (2024)
Abstract
Background
Mild cognitive impairment (MCI) may lead to difficulty maintaining postural stability and balance during locomotion. This heightened susceptibility to falls is particularly evident during tasks such as obstacle negotiation, which demands efficient motor planning and reallocation of attentional resources. This study proposed a multi-objective optimal control (MOOC) technique to assess the changes in motor control strategies during obstacle negotiation in older people affected by amnestic MCI.
Methods
Motion data from 12 older adults with MCI and 12 controls when crossing obstacles were measured using a motion capture system, and used to obtain the control strategy of obstacle-crossing as the best compromise between the conflicting objectives of the MOOC problem, i.e., minimising mechanical energy expenditure and maximising foot-obstacle clearance. Comparisons of the weighting sets between groups and obstacle heights were performed using a two-way analysis of variance with a significance level of 0.05.
Results
Compared to the controls, the MCI group showed significantly lower best-compromise weightings for mechanical energy expenditure but greater best-compromise weightings for both heel- and toe-obstacle clearances. This altered strategy involved a trade-off, prioritising maximising foot-obstacle clearance at the expense of increased mechanical energy expenditure. The MCI group could successfully navigate obstacles with a normal foot-obstacle clearance but at the cost of higher mechanical energy expenditure.
Conclusions
MCI alters the best-compromise strategy between minimising mechanical energy expenditure and maximising foot-obstacle clearances for obstacle-crossing in older people. These findings provide valuable insights into how MCI impacts motor tasks and offer potential strategies for mitigating fall risks in individuals with MCI. Moreover, this approach could serve as an assessment tool for early diagnosis and a more precise evaluation of disease progression. It may also have applications for individuals with impairments in other cognitive domains.
Background
Mild cognitive impairment (MCI) is an intermediary stage in the continuum from normal cognitive ageing to early dementia. Individuals diagnosed with MCI exhibit an annual risk of dementia ranging from 12 to 15%, compared to about 1–2% in the general older population [1,2,3]. The incidence of MCI escalates with age, increasing from 8.4% among people aged 65–69 years to 25.2% in the 80–84 bracket [4]. Cognitive decline in older people with MCI causes memory, executive function, or attention impairments, although they can still maintain their independence of function in daily activities [5]. People affected by MCI were found to have aberrant postural control or balance abnormalities during gait, as well as a higher risk of falling [6,7,8], particularly when navigating obstacles [9]. Obstacle negotiation requires more attentional resources than unobstructed walking in motor planning [10], processes regulated by the central nervous system (CNS) [11]. In individuals with MCI, the deterioration of cognitive faculties can impede the capacity to effectively strategise and coordinate movements. Among the various subtypes of MCI, amnestic MCI (aMCI), characterised mainly by a memory deficit, is prevalent and recognised as a precursor to Alzheimer’s disease [12]. Identifying the overall control strategy used by aMCI for obstacle-crossing will aid in gaining a better understanding of the disease’s influence on the motor task and devising methods to reduce fall risks in this population.
The current understanding of obstacle-crossing mechanics and control is mostly gained from quantifying peripheral mechanical changes, namely angles and moments at lower limb joints [13,14,15,16,17,18,19]. Several studies have used single-objective optimal control techniques to identify the minimisation of mechanical energy expenditure as the motor control principle underlying normal human gait [20,21,22,23]. However, motions of obstacle-crossing predicted by minimising mechanical energy expenditure alone differed significantly from those experimentally observed [24]. In contrast to level walking, obstacle-crossing requires greater mechanical energy to lift the swing limb to clear the obstacle and carry out the associated body’s postural adjustments for maintaining balance [16]. Increasing toe-obstacle clearance may effectively reduce the odds of the crossing foot colliding with the obstacle, but the associated body posture changes may inevitably increase the overall mechanical energy expenditure [25].
Multi-objective optimisation techniques have been used in group decision-making scenarios involving multiple conflicting objectives, contributing significantly to the fields of quantitative psychology and mathematical game theory [26, 27]. These methods are effective in describing the trade-off mechanisms inherent in multiple conflicting objectives, offering valid mathematical frameworks to represent the preferences of individual decision-makers [28]. In MOOC problems with conflicting objectives, the optimal solutions are termed non-dominated or Pareto solutions. All non-dominate solutions are regarded as equally good, as any attempt to improve the value of one objective function would inevitably result in the degrading of the value of other objective functions [26, 27, 29]. In practice, decision-makers can select a single non-dominated solution as the best compromise solution according to personal preferences [30, 31], e.g., represented by the weightings assigned to each objective in the weighting method [27, 28, 31]. To study the central control strategy for obstacle-crossing, Lu et al. [24] proposed a multi-objective optimal control (MOOC) framework and showed that the strategy in healthy young adults was the best compromise between minimising mechanical energy expenditure and maximising clearance of the swing foot. This best compromise solution accurately predicts the swing ankle trajectories and lower limb joint angles and moments independent of obstacle heights, indicative of being a central control strategy chosen by the CNS [24].
Self-awareness and self-regulation enable individuals to assess impending environmental hazards, thereby facilitating the adjustments of control strategies in advance to prevent falls. In normal ageing, compared to the young, older people adopted a control strategy that emphasised less mechanical energy expenditure to trade for maximising foot-obstacle clearance to reduce tripping risks [32]. Being a degenerative disease in the central nervous system, it is known that MCI leads to gait changes during unobstructed walking. It is quite likely that cognitive decline in older adults with MCI may affect the assessment of environmental risks, leading to inappropriate anticipatory postural adjustments for obstacle negotiation [33, 34]. Although the relationship between observed joint level changes and age, pathology, or interventions has been established [35], it remains unclear to what extent aMCI affects the central control strategies during obstacle-crossing. Identifying and quantifying the alterations in the MOOC crossing strategy in older people with MCI may be helpful for an accurate diagnosis and assessment of the progression of the disease and provide a guideline for assessing an individual’s risk of falling.
The current study proposed using the MOOC approach to identify and assess the effects of aMCI on the motor control strategy for obstacle negotiation in older people. It was hypothesised that the best compromise control strategy adopted by older people with MCI remained to be a non-dominate solution between minimisation of mechanical energy expenditure and maximisation of foot-obstacle clearances but traded off the mechanical energy expenditure for a normal foot-obstacle clearance when compared to healthy controls. It was hoped that the current study would provide more insights into the control of walking while negotiating obstacles in aMCI with a new set of parameters for quantifying the overall control strategy and that the findings would be helpful for the design and improvement of the current clinical management programs.
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