## 1 Introduction  
Aging leads to neuromuscular control difficulties while performing balance and walking needed for daily living [1]. With decreases in balance comes an increase in risk for falling leading to injury and even death; in the United States, one in four adults over 65 years old falls each year [2]. Aging is often accompanied by degeneration of nerve and muscle tissues [3] and of the sensory systems used for balance (i.e., visual, somatosensory, and vestibular), e.g., Ref. [4]. Moreover, aging is associated with major changes in the neuromotor system. In particular, these include reduction in muscle strength, power, and joint mobility along with an impaired sensorimotor integration [2]. Within the aging population, strokes are a prominent issue that can lead to disability and even death; over 60% of all strokes occur above the age of 65 years old [5]. Thus, effective rehabilitation is important towards facilitating balance maintenance in both older individuals that consider themselves healthy and survivors of stroke. Unfortunately, only 60 to 70 percent of survivors of stroke are able to walk again independently after rehabilitation [6]. In terms of quantifying balance and walking, common clinical measures have focused on qualitative tests, e.g., the commonly known Sit-to-Stand test and Timed Up and Go test (TUG), which can only offer crude, global, and/or descriptive information such as the time it takes to complete sitting and walking tasks, respectively. While kinematic measures (e.g., motion capture) can provide more detailed information in terms of movements, it is difficult to distinguish differences due to neural or musculoskeletal deficits. Further, forceplate methods (e.g., to measure center of pressure (COP)) may have limited applicability tied to movement due to the activation of different neural pathways because the same force and resulting movement can be achieved by many different muscle coordination patterns [7]. Muscle activation and muscle synergy analysis facilitate the characterization of neuromuscular control strategies for movement. Surface electromyography (sEMG) is a commonly used, non-invasive measure to determine muscle activation. Through post-processing analysis of the raw sEMG signals, muscle synergies then can be determined tied to the activity of the muscles; thus, muscle synergy analysis can facilitate the characterization of neuromuscular control strategies and functional deficits [1; 8; 9]. Via spatial and temporal activation examined from the recorded muscle activity, muscle synergies offer insight in terms of muscle coordination and activation patterns. Patients with impaired motor functions tend to show an adaptation of muscle synergies to typical characteristics of movement (such as type of movement, speed, compensation of asymmetry due to neurological damage) [10]. After a stroke, fewer synergies are used during walking and upper-extremity tasks compared with unimpaired adults, possibly reflecting a simplified control strategy [11; 12]; however, it will be interesting to determine if the same holds true for lower-extremity tasks such as walking. To the best of our knowledge, muscle synergy changes in older adults following several weeks of balance training have not been published, neither for healthy older adults nor survivors of stroke within the aged population. Previous research regarding muscle synergies in humans has evaluated sEMG responses for walking and during platform perturbations, e.g., Refs. [1; 12; 13; 14; 15; 16; 17]. The effects of directionality tied to muscle synergy have been examined (e.g., during perturbations for angles between 0 to 270 degrees), e.g., Refs. [15; 17]. While directional perturbation approaches provide insight into how the central nervous system affects muscle synergies via muscle tuning curves, these stimuli are not a direct comparison and atypical for movement one experiences in daily living. Task-specific muscle synergies have been examined (e.g., by performing straight forward walking, as well as right and left turning) [9].