## 1 Introduction  
Aging is associated with gradual changes in neuromuscular control [1; 2]. Eventually, these changes can have a major impact on fall risk, mobility and independence in older adults [3; 4; 5], which may be exacerbated in post-menopausal women due to accelerated muscle wasting [5]. One major challenge with assessing changes in neuromuscular control in healthy community-dwelling older adults is that, due to the gradual nature of age-related neuromuscular deterioration, pre-clinical changes in neuromuscular control of everyday tasks may go undetected [6]. In healthy older adults, changes in neuromuscular control can be revealed by increasing task challenge. For example, by increasing gait speed, cadence, step length, and step height [7; 8; 9]. Step ascent, a functional task in daily life, provides a challenge that can easily be modified by increasing step height and has been demonstrated to be an effective training stimulus to improve muscle volume and functional ability in older women [10; 11]. However, the effects of aging and task challenge on the neuromuscular control strategies behind step ascent have not yet been thoroughly explored. In a previous study conducted with older women, we found that peak activation of several major lower limb muscles occurred during the ascent phase of stepping and that there is a positive dose-response relationship between step height and peak muscle activation [10]. However, the increase in step height was also accompanied by increased between-subject variance of peak activation magnitudes [10]. This could be attributable to a tendency of older adults to modulate their motor strategies (e.g., shifting joint moment generation from the knee to the ankle), in order to operate within the limits of their physical capacity when ascending steps [12; 13]. Additionally, it is unknown if other observed age-related changes in neuromuscular control strategies, such as increased antagonistic co-contraction of quadriceps and hamstrings to help maintain postural control during dynamic tasks [14; 15; 16; 17], may interact with increased task challenge. One way to assess neuromuscular control is through muscle synergy analysis. Previous studies have found that low-dimensional sets of motor modules, also known as muscle synergies, can be used to reconstruct muscle activation patterns during various motor tasks [18; 19; 20; 21; 22]. These synergies are composed of groups of muscles that are assumed to be activated by a single neural command [23]. It is thought that the central nervous system employs this modular organization to reduce the large number of degrees of freedom inherent to the redundancy of the human musculoskeletal system [24], and to allow for flexible but accurate response selection during motor tasks [25]. However, some researchers have argued that modular recruitment of muscles might represent predetermined control strategies and could merely be an effect of task constraints or optimized performance criteria, rather than reflecting neural control strategies employed by the central nervous system [23; 26]. Regardless of the mechanisms underlying modular organization of muscle activation, extracting muscle synergies from electromyographic (EMG) signals can provide important insights about neuromuscular control strategies used to perform functional tasks [27]. In older adults with a history of falls, declines in neuromuscular control are reflected in a decreased number of extracted synergies from walking tasks that challenge dynamic balance [6]. A decreased number of synergies is indicative of decreased complexity of motor control or a decreased motor repertoire. These underlying changes in synergy complexity can be quantified using variance account for (VAF) by a given set of extracted synergies and defining the number of synergies required to adequately reconstruct the original EMG signals (indicated by a-priori or a-posteriori set thresholds for VAF) [28].