Novice Users' Brain Activity and Biomechanics Change After Practicing Walking With an Ankle Exoskeleton.

Walking with an exoskeleton represents a sophisticated interplay between human physiology and mechanical augmentation, yet understanding of cortical responses in this context remains limited. To address this gap, this study aimed to explore cortical responses during walking with an ankle exoskeleton, examining how these responses evolve with familiarity to the augmentation. Healthy participants without prior exoskeleton experience underwent EEG, EMG, and motion capture analysis while walking with exoskeleton assistance at 1.2m/s. Initially, exoskeleton-assisted walking induced significant biomechanical changes accompanied by corresponding cortical alterations, leading to increased cortical involvement. In addition, after a brief familiarization period, significant increases in alpha band cortical power were observed, indicating decreased cortical engagement. These findings hold significance for elucidating the cortical mechanisms involved in exoskeleton-assisted walking and may contribute to the development of more seamlessly integrated augmentation devices.

Characterizing Bodyweight-Supported Treadmill Walking on Land and Underwater Using Foot-Worn Inertial Measurement Units and Machine Learning for Gait Event Detection.

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95-96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.

Mobile Electroencephalography for Studying Neural Control of Human Locomotion.

Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.

Characteristics of physiological data during asymmetric gait of healthy subjects using a two-belt treadmill.

BACKGROUND: Mobile rehabilitation systems for patients with gait disorder are being developed. Safety functions to prevent patients from falling are considered during product development; however, few studies have been conducted on systems that have been prevalidated for healthy adults prior to application to patients. OBJECTIVE: This study analyzed the characteristics of lower extremity muscles and foot pressure in healthy adults during unbalanced walking with differences in the speed of left and right speed using a two-belt treadmill. METHODS: Twenty subjects performed gait motions with a difference in the weight support conditions (0% and 30%) and the left and right lower limb speeds (1-3 km/h). Each subject's muscular activities and foot pressure signals were collected. The gait patterns of the faster side exhibit similar characteristics to the paralyzed leg, and the slower side is similar to the non-paralyzed leg. RESULTS: Weight-supporting healthy subjects showed asymmetric gait patterns, similar to hemiplegic patients, because of the difference in the speed of the left and right side. CONCLUSIONS: The quantitative results can be used to develop a training protocol for two-belt treadmills with differently controlled left and right speeds for gait rehabilitation in hemiplegic patients.