Effects of simulated reduced gravity and walking speed on ankle, knee, and hip quasi-stiffness in overground walking

Time-varying and speed-matched model for the evaluation of stroke-induced changes in ankle mechanics

The ankle mechanics (stiffness and moment) are modulated continuously when interacting with the environment during human walking. However, it remains unclear how ankle mechanics vary with walking speeds, and how they are affected by stroke. This study aimed to determine time-varying ankle stiffness and moment in stroke participants during walking, comparing them with healthy participants at matched speeds. A motion capture system, surface electromyography (EMG) system and force plates were used to measure biomechanics of seven healthy participants walking at 5 controlled speeds and ten patients with stroke at self-selected speeds. The ankle moment and stiffness during the stance phase were calculated using an EMG-driven musculoskeletal model. Surface equations of ankle moment and stiffness in healthy participants, with walking speed and stance phase as variables, were proposed based on polynomial fitting. Results showed that as walking speed increased, there was an increase in the ankle stiffness and moment of healthy participants during 77 %-89 % and 63 %-91 % of stance phase, respectively. Patients with stroke had lower ankle stiffness and moment at self-selected walking speed than healthy participants at 1.04 m/s walking speed during 52 %-87 % and 52 %-91 % of stance phase, respectively. At matched walking speed, the peak values of ankle stiffness and moment in patients with stroke were significantly less than those in healthy participants (p = 0.007; p = 0.028, respectively). This study proposes a novel approach to evaluate the ankle mechanics of patients with stroke using the speed-matched model of healthy participants and may provide insights into understanding speed-dependent movement mechanisms of human walking.

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

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.

Effects of Walking Speed and Added Mass on Hip Joint Quasi-Stiffness in Healthy Young and Middle-Aged Adults

Joint quasi-stiffness has been often used to inform exoskeleton design. Further understanding of hip quasi-stiffness is needed to design hip exoskeletons. Of interest are wearer responses to walking speed changes with added mass of the exoskeleton. This study analyzed hip quasi-stiffness at 3 walking speed levels and 9 added mass distributions among 13 young and 16 middle-aged adults during mid-stance hip extension and late-stance hip flexion. Compared to young adults, middle-aged adults maintained a higher quasi-stiffness with a smaller range. For a faster walking speed, both age groups increased extension and flexion quasi-stiffness. With mass evenly distributed on the pelvis and thighs or biased to the pelvis, both groups maintained or increased extension quasi-stiffness. With mass biased to the thighs, middle-aged adults maintained or decreased extension quasi-stiffness while young adults increased it. Young adults decreased flexion quasi-stiffness with added mass but not in any generalizable pattern with mass amounts or distributions. Conversely, middle-aged adults maintained or decreased flexion quasi-stiffness with even distribution on the pelvis and thighs or biased to the pelvis, while no change occurred if biased to the thighs. In conclusion, these results can guide the design of a hip exoskeleton's size and mass distribution according to the intended user's age.

EMG-informed neuromuscular model assesses the effects of varied bodyweight support on muscles during overground walking

Bodyweight supported walking is a common gait rehabilitation method that can be used as an experimental approach to better understand walking biomechanics. Neuromuscular modeling can provide an analytical means to gain insight into how muscles coordinate to produce walking and other movements. To better understand how muscle length and velocity affect muscle force during overground walking with bodyweight support, we used an electromyography (EMG)-informed neuromuscular model to investigate changes in muscle parameters (muscle force, activation and fiber length) at varying bodyweight support levels: 0%, 24%, 45% and 69% bodyweight. Coupled constant force springs provided a vertical support force while we collected biomechanical data (EMG, motion capture and ground reaction forces) from healthy, neurologically intact participants walking at 1.20 ± 0.06 m/s. The lateral and medial gastrocnemius demonstrated a significant decrease in muscle force (lateral: p = 0.002 and medial: p < 0.001) and activation (lateral: p = 0.007 and medial: p < 0.001) through push-off at higher levels of support. The soleus, in contrast, had no significant change in muscle activation through push-off (p = 0.652) regardless of bodyweight support level even though soleus muscle force decreased with increasing support (p < 0.001). During push-off, the soleus had shorter muscle fiber lengths and faster shortening velocities as bodyweight support levels increased. These results provide insight into how muscle force can be decoupled from effective bodyweight during bodyweight supported walking due to changes in muscle fiber dynamics. The findings contribute evidence that clinicians and biomechanists should not expect a reduction in muscle activation and force when using bodyweight support to assist gait during rehabilitation.