Effect of Motor-Assisted Elliptical Training Speed and Body Weight Support on Center of Pressure Movement Variability

Background: A motor-assisted elliptical trainer is being used clinically to help individuals with physical disabilities regain and/or retain walking ability and cardiorespiratory fitness. Unknown is how the device’s training parameters can be used to optimize movement variability and regularity. This study examined the effect of motor-assisted elliptical training speed as well as body weight support (BWS) on center of pressure (CoP) movement variability and regularity during training. Methods: CoP was recorded using in-shoe pressure insoles as participants motor-assisted elliptical trained at three speeds (20, 40, and 60 cycles per minute) each performed at four BWS levels (0%, 20%, 40%, and 60%). Separate two-way repeated measures ANOVAs (3 × 4) evaluated impact of training speed and BWS on linear variability (standard deviation) and nonlinear regularity (sample entropy) of CoP excursion (anterior-posterior, medial-lateral) for 10 dominant limb strides. Findings: Training speed and BWS did not significantly affect the linear variability of CoP in the anterior-posterior or medial-lateral directions. However, sample entropy in both directions revealed the main effect of training speed (p \u3c 0.0001), and a main effect of BWS was observed in the medial-lateral direction (p = 0.004). Faster training speeds and greater levels of BWS resulted in more irregular CoP patterns. Interpretation: The finding that speed and BWS can be used to manipulate CoP movement variability when using a motor-assisted elliptical has significant clinical implications for promoting/restoring walking capacity. Further research is required to determine the impact of motor-assisted elliptical speed and BWS manipulations on functional recovery of walking in individuals who have experienced a neurologic injury or illness

The effect of exoskeleton footwear on joint angular motion during walking in patients with peripheral artery disease

Gait, Lower Extremity, Peripheral Artery Disease, Joint Angle, Exoskeleton Footwear, Exoskeleton, Assistive Device, Walking

Optic flow improves step width and length in older adults while performing dual task

Background Dual-task paradigms are used to investigate gait and cognitive declines in older adults (OA). Optic-flow is a virtual reality environment where the scene flows past the subject while walking on a treadmill, mimicking real-life locomotion. Aims To investigate cost of environment (no optic-flow v. optic-flow) while completing single- and dual-task walking and dual-task costs (DTC; single- v. dual-task) in optic-flow and no optic-flow environments. Methods Twenty OA and seven younger adults (YA) walked on a self-paced treadmill in 3-min segments per task and both environments. Five task conditions included: no task, semantic fluency (category), phonemic fluency (letters), word reading, and serial-subtraction. Results OAs had a benefit of optic-flow compared to no optic-flow for step width (p = 0.015) and step length (p = 0.045) during letters compared to the YA. During letters, OA experienced improvement in step width DTC; whereas YA had a decrement in step width DTC from no optic-flow to optic-flow (p = 0.038). During serial-subtraction, OA had less step width DTC when compared to YA in both environments (p = 0.02). Discussion During letters, step width and step length improved in OA while walking in optic-flow. Also, step width DTC differed between the two groups. Sensory information from optic-flow appears to benefit OA. Letters relies more on verbal ability and word knowledge, which are preserved in aging. However, YA use a complex speech style during dual tasking, searching for complex words and an increased speed of speech. Conclusions OA can benefit from optic-flow by improving spatial gait parameters, specifically, step width, during dual-task walking

Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew’s Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew’s Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification