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Many individuals with disabling conditions have difficulty with gait and balance control that may result in a fall. Exoskeletons are becoming an increasingly popular technology to aid in walking. Despite being a significant aid in increasing mobility, little attention has been paid to exoskeleton features to mitigate falls. To develop improved exoskeleton stability, quantitative information regarding how a user reacts to postural challenges while wearing the exoskeleton is needed. Assessing the unique responses of individuals to postural perturbations while wearing an exoskeleton provides critical information necessary to effectively accommodate a variety of individual response patterns. This report provides kinematic and neuromuscular data obtained from seven healthy, college-aged individuals during posterior support surface translations with and without wearing a lower limb exoskeleton. A 2-min, static baseline standing trial was also obtained. Outcome measures included a variety of 0 dimensional (OD) measures such as center of pressure (COP) RMS, peak amplitude, velocities, pathlength, and electromyographic (EMG) RMS, and peak amplitudes. These measures were obtained during epochs associated with the response to the perturbations: baseline, response, and recovery. T-tests were used to explore potential statistical differences between the exoskeleton and no exoskeleton conditions. Time series waveforms (1D) of the COP and EMG data were also analyzed. Statistical parametric mapping (SPM) was used to evaluate the 1D COP and EMG waveforms obtained during the epochs with and without wearing the exoskeleton. The results indicated that during quiet stance, COP velocity was increased while wearing the exoskeleton, but the magnitude of sway was unchanged. The OD COP measures revealed that wearing the exoskeleton significantly reduced the sway magnitude and velocity in response to the perturbations. There were no systematic effects of wearing the exoskeleton on EMG. SPM analysis revealed that there was a range of individual responses; both behaviorally (COP) and among neuromuscular activation patterns (EMG). Using both the OD and 1D measures provided a more comprehensive representation of how wearing the exoskeleton impacts the responses to posterior perturbations. This study supports a growing body of evidence that exoskeletons must be personalized to meet the specific capabilities and needs of each individual end-user. 

There have been significant advances in the technologies for robot-assisted lower-limb rehabilitation in the last decade. However, the development of similar systems for children has been slow despite the fact that children with conditions such as cerebral palsy (CP), spina bifida (SB) and spinal cord injury (SCI) can benefit greatly from these technologies. Robotic assisted gait therapy (RAGT) has emerged as a way to increase gait training duration and intensity while decreasing the risk of injury to therapists. Robotic walking devices can be coupled with motion sensing, electromyography (EMG), scalp electroencephalography (EEG) or other noninvasive methods of acquiring information about the user’s intent to design Brain-Computer Interfaces (BCI) for neuromuscular rehabilitation and control of powered exoskeletons. For users with SCI, BCIs could provide a method of overground mobility closer to the natural process of the brain controlling the body’s movement during walking than mobility by wheelchair. For adults there are currently four FDA approved lower-limb exoskeletons that could be incorporated into such a BCI system, but there are no similar devices specifically designed for children, who present additional physical, neurological and cognitive developmental challenges. The current state of the art for pediatric RAGT relies on large clinical devices with high costs that limit accessibility. This can reduce the amount of therapy a child receives and slow rehabilitation progress. In many cases, lack of gait training can result in a reduction in the mobility, independence and overall quality of life for children with lower-limb disabilities. Thus, it is imperative to facilitate and accelerate the development of pediatric technologies for gait rehabilitation, including their regulatory path. In this paper an overview of the U.S. Food and Drug Administration (FDA) clearance/approval process is presented. An example device has been used to navigate important questions facing device developers focused on providing lower limb rehabilitation to children in home-based or other settings beyond the clinic. 

Objective: Accurate implementation of real-time non-invasive Brain-Machine / Computer Interfaces (BMI / BCI) requires handling physiological and non-physiological artifacts associated with the measurement modalities. For example, scalp electroencephalographic (EEG) measurements are often considered prone to excessive motion artifacts and other types of artifacts that contaminate the EEG recordings. Although the magnitude of such artifacts heavily depends on the task and the setup, complete minimization or isolation of such artifacts is generally not possible. Approach: We present an adaptive de-noising framework with robustness properties, using a Volterra based non-linear mapping to characterize and handle the motion artifact contamination in EEG measurements. We asked healthy able-bodied subjects to walk on a treadmill at gait speeds of 1-to-4 mph, while we tracked the motion of select EEG electrodes with an infrared video-based motion tracking system. We also placed Inertial Measurement Unit (IMU) sensors on the forehead and feet of the subjects for assessing the overall head movement and segmenting the gait. Main Results: We discuss in detail the characteristics of the motion artifacts and propose a real-time compatible solution to filter them. We report the effective handling of both the fundamental frequency of contamination (synchronized to the walking speed) and its harmonics. Event-Related Spectral Perturbation (ERSP) analysis for walking shows that the gait dependency of artifact contamination is also eliminated on all target frequencies. Significance: The real-time compatibility and generalizability of our adaptive filtering framework allows for the effective use of non-invasive BMI/BCI systems and greatly expands the implementation type and application domains to other types of problems where signal denoising is desirable. Combined with our previous efforts of filtering ocular artifacts, the presented technique allows for a comprehensive adaptive filtering framework to increase the EEG Signal to Noise Ratio (SNR). We believe the implementation will benefit all non-invasive neural measurement modalities, including studies discussing neural correlates of movement and other internal states, not necessarily of BMI focus. 

Due to construction costs, the human effects of innovations in architectural design can be expensive to test. Post-occupancy studies provide valuable data about what did and did not work in the past, but they cannot provide direct feedback for new ideas that have not yet been attempted. This presents designers with something of a dilemma. How can we harness the best potential of new technology and design innovation, while avoiding costly and potentially harmful mistakes? The current research use virtual immersion and biometric data to provide a new form of extremely rigorous human-response testing prior to construction. The researchers’ hypothesis was that virtual test runs can help designers to identify potential problems and successes in their work prior to its being physically constructed. The pilot study aims to develop a digital pre-occupancy toolset to understand the impact of different interior design variables of learning environment (independent variables) on learning performance (dependent variable). This project provides a practical toolset to test the potential human impacts of architectural design innovations. The research responds to a growing call in the field for evidence-based design and for an inexpensive means of evaluating the potential human effects of new designs. Our research will address this challenge by developing a prototype mobile brain-body imaging interface that can be used in conjunction with virtual immersion.