Objective Individual muscle activation patterns may be controlled by motor modules constructed by the central nervous system to simplify motor control. This study compared modular control of gait between persons with Parkinson’s disease (PD) and neurologically-healthy older adults (HOA) and investigated relationships between modular organization and gait parameters in persons with PD. Methods Fifteen persons with idiopathic PD and fourteen HOA participated. Electromyographic recordings were made from eight leg muscles bilaterally while participants walked at their preferred walking speed for ten minutes on an instrumented treadmill. Non-negative matrix factorization techniques decomposed the electromyographic signals, identifying the number and nature of modules accounting for 95% of variability in muscle activations during treadmill walking. Results Generally, fewer modules were required to reconstruct muscle activation patterns during treadmill walking in PD compared to HOA (p<.05). Control of knee flexor and ankle plantarflexor musculature was simplified in PD. Activation timing was altered in PD while muscle weightings were unaffected. Simplified neuromuscular control was related to decreased walking speed in PD. Conclusions Neuromuscular control of gait is simplified in PD and may contribute to gait deficits in this population. Significance Future studies of locomotor rehabilitation in PD should consider neuromuscular complexity to maximize intervention effectiveness.

Injury from blunt head impacts causes acute neurological deficits and may lead to chronic neurodegeneration. A head impact detection device can serve both as a research tool for studying head injury mechanisms and a clinical tool for real-time trauma screening. The simplest approach is an acceleration thresholding algorithm, which may falsely detect high-acceleration spurious events such as manual manipulation of the device. We designed a head impact detection system that distinguishes head impacts from nonimpacts through two subsystems. First, we use infrared proximity sensing to determine if the mouthguard is worn on the teeth to filter out all off-teeth events. Second, on-teeth, nonimpact events are rejected using a support vector machine classifier trained on frequency domain features of linear acceleration and rotational velocity. The remaining events are classified as head impacts. In a controlled laboratory evaluation, the present system performed substantially better than a 10-g acceleration threshold in head impact detection (98% sensitivity, 99.99% specificity, 99% accuracy, and 99.98% precision, compared to 92% sensitivity, 58% specificity, 65% accuracy, and 37% precision). Once adapted for field deployment by training and validation with field data, this system has the potential to effectively detect head trauma in sports, military service, and other high-risk activities.

Roughly 5% of all collegiate and high school American football players suffer a concussion each season [1]. Concussions and repetitive sub-concussive trauma can have measurable effects on brain function and neurophysiological changes [2]. Several studies have suggested that a combination of linear and angular kinematic measures may be predictive of concussion [3, 4]. Presently, laboratory testing and analysis of purely linear kinematics is used to design and assess the safety of protective headgear. However, it is not known how well existing laboratory tests recapitulate angular kinematics. In this study, we analyze combinations of linear and angular head kinematics experienced by players on the field. This study sought to answer the question: how well do the twin-wire drop test apparatus and a spring-driven linear impactor reproduce the combination of linear and angular head impact kinematics experienced in vivo by players of American football?