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Mobile brain-body imaging (MoBI) seeks to understand human brain and body dynamics during movement and locomotor tasks such as walking with perturbations that challenge balance and lead to adaptation of walking behavior. In this dissertation, I evaluated the long-term electromyography (EMG) recording performance of dry epidermal electrodes for measuring electrical muscle activity. I also evaluated the relationships between the signals recorded from the two sides of dual-sided electroencephalography (EEG) electrodes, a recent advancement in EEG electrode design for measuring electrical brain activity. Last, I investigated adaptation of brain and body responses to small and frequent perturbations during treadmill walking while I recorded brain activity using a custom-built dual-layer EEG system and body kinematics using motion capture. Dry epidermal electrodes provided better Signal Quality Indices, a metric I developed that accounts for signal-to-noise and signal-to-motion contributions, during limited dynamic movements, indicating that high-quality EMG for long-term recording was possible but also limited. For the dual-sided EEG electrode evaluation, I quantified correlations between dual-sided EEG signals in a benchtop experiment. Signals recorded from two sides of a dual-sided EEG electrode were highly correlated during constrained movements but degraded in more realistic random movements. This information is critical for developing EEG cleaning algorithms based on dual-layer EEG systems. For the locomotor adaptation studies, I quantified gait stability using margin of stability and its components and performed source localization and time-frequency analyses to determine electrocortical processes during perturbed walking. Small and frequent treadmill perturbations disrupted gait stability and quickly induced direction-dependent gait stability adaptation. Anterior cingulate theta-band adaptation occurred and was more evident during belt deceleration perturbations compared to belt acceleration perturbations. These results add new knowledge about the characteristics of novel EMG and EEG electrodes and revealed the potential of modulating perturbation direction to tune gait stability strategy and activation of electrocortical dynamics.

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Habitually poor posture can lead to repetitive strain injuries that lower an individual's quality of life and productivity. Slouching over computer screens and smart phones, asymmetric weight distribution due to uneven leg loading, and improper loading posture are some of the common examples that lead to postural problems and health ramifications. To help cultivate good postural habits, researchers have proposed slouching, balance, and improper loading posture detection systems that alert users through traditional visual, auditory or vibro-tactile feedbacks when posture requires attention. However, such notifications are disruptive and can be easily ignored. We address these issues with a new physiological feedback system that uses sensors to detect these poor postures, and electrical muscle stimulation to automatically correct the poor posture. We compare our automatic approach against other alternative feedback systems and through different unique contexts. We find that our approach outperformed alternative traditional feedback systems by being faster and more accurate while delivering an equally comfortable user experience.

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On a daily basis, humans walk over a variety of terrains. Studies have shown that spatiotemporal gait parameters, such as stride length, stride frequency, stride variability, etc., change when humans walk down a decline and up an incline compared to level ground. However, these studies have been limited to using fixed speed treadmills or analyzing a small number of strides when conducted over ground. Thus, there is a need to investigate the fluctuations in spatiotemporal gait parameters of humans walking at their self-selected speed, which requires recording hundreds of strides. Here we hypothesized that subjects will walk with a slower speed and have greater stride variability on an incline or decline compared to level ground. We used a self-paced treadmill and had 7 young adults walk on three slopes (+9 degrees, incline; 0 degrees, level; -9 degrees, decline). A motion capture system was used to calculate spatiotemporal gait parameters. The results showed that subjects walked the fastest on level ground (1.15 +/- 0.17 m/s). Subjects walked more slowly during decline walking (1.06 +/- 0.14 m/s) and walked the slowest during incline walking (0.92 +/- 0.18 m/s). There was not a single steady-state speed that subjects used for all slopes. Instead, there were multiple periods when the subject was not at a steady state. Only ~60% of the strides could be classified as being at steady-state. When walking down a decline, subjects needed ~10 +/- 1 more strides to reach the first steady-state period. When walking on an incline and decline, stride length variability increased by ~1.6x (0.0014�2 ± 0.0008�2) and ~1.2x (0.0012�2 ± 0.0008�2 ) compared to level ground (0.0005 �2 ± 0.0003 �2). Stride width variability increased by ~20.6x (0.0108�2 ± 0.0121�2 ) and ~14.2x (0.0076�2 ± 0.0044�2 ) for incline and decline slopes compared to level ground (0.0005 �2 ± 0.0003 �2). These results provide greater insight on the fluctuations during self-selected walking speeds subjects use on different slopes. This could have implications on balance control and fall risk during walking.

This work is an experimental study and fabrication of design concepts to validate the feasibility of smart materials and their applications in bio-inspired robotics. Shape-Memory Alloy (SMA) springs are selected as the smart material actuators of interest to achieve locomotion in the proposed mobile robot. Based on a previous design of the robot, this work focuses on both implementing a new locomotion concept and reducing size and weight of the previous design, both using SMA based actuators. Objectives are met in consideration of the conceptual mechanics of circular robot locomotion. The first prototype is a variation of the original design. It consists of a soft, rubber outer shell with three intrinsically attached diametric SMA springs that deform the outer shell during contraction and relaxation. The springs were provided with electrical current in patterns to produce deformation needed to generate momentum and allow the robot to tumble and roll. This design was further improved to provide more stability while rolling. The second design concept is a modification of our previous design leading to reduction in size and weight while maintaining essentially the same mechanism of locomotion. In this case, the SMA springs were externally configured between the end of equi-spaced spokes and the circular core. Upon actuation, the spokes function as diametrically translating legs to generate locomotion. These design concepts are fabricated and experimented on, to determine their feasibility, i.e. whether rolling/tumbling motion is achieved. The scope of the project was limited to demonstration of basic locomotion, which was successful. Future work on this project will address the design of automatic control to generate motion using closed-loop sensor-based actuation.

Metabolic power and cost of transport (COT) are common quantifiers for effort when performing tasks including walking and running. Most studies focus on using a range of normal walking speeds over level ground or varied slopes. However, these studies use fixed-speed conditions. Fatigue, stability, metabolic expenditure, heart rate, and many other factors contribute to normal walking speed varying over time. This study aimed to show that allowing a subject to walk with a self-paced speed should correlate to a minimum COT at a given slope. This study also aimed to determine if a preferred slope exists based on minimizing metabolic expenditure or maximizing stability. In this study, subjects walked at four different speed conditions including three fixed speeds (0.75 m/s, 1.0 m/s, 1.25 m/s) and their self-paced speed at five different slopes (-6°, -3°, 0°, 3°, 6°) while metabolic energy expenditure and motion were recorded. The minimum COT occurred at a 3° decline. At this slope, some subjects preferred to walk at a faster speed compared to level ground, whereas other subjects walked with a slower speed compared to level ground. Thus, there was a greater range of self-paced speeds, from 0.745 m/s-2.045 m/s. In comparison, at a 6° incline, the range of self-paced speeds was much smaller, from 0.767 m/s-1.434 m/s. The variance among self-paced speeds and slope conditions between subjects suggests that COT, alone, does not explain walking decisions; stability might play a greater role than initially believed. These results provide greater insight into why humans choose to walk at a certain speed over a range of slopes and terrains.