Subject-Independent Continuous Locomotion Mode Classification for Robotic Hip Exoskeleton Applications.

Autonomous lower-limb exoskeletons must modulate assistance based on locomotion mode (e.g., ramp or stair ascent) to adapt to the corresponding changes in human biological joint dynamics. However, current mode classification strategies for exoskeletons often require user-specific tuning, have a slow update rate, and rely on additional sensors outside of the exoskeleton sensor suite. In this study, we introduce a deep convolutional neural network-based locomotion mode classifier for hip exoskeleton applications using an open-source gait biomechanics dataset with various wearable sensors. Our approach removed the limitations of previous systems as it is 1) subject-independent (i.e., no user-specific data), 2) capable of continuously classifying for smooth and seamless mode transitions, and 3) only utilizes minimal wearable sensors native to a conventional hip exoskeleton. We optimized our model, based on several important factors contributing to overall performance, such as transition label timing, model architecture, and sensor placement, which provides a holistic understanding of mode classifier design. Our optimized DL model showed a 3.13% classification error (steady-state: 0.80 ± 0.38% and transitional: 6.49 ± 1.42%), outperforming other machine learning-based benchmarks commonly practiced in the field (p<0.05). Furthermore, our multi-modal analysis indicated that our model can maintain high performance in different settings such as unseen slopes on stairs or ramps. Thus, our study presents a novel locomotion mode framework, capable of advancing robotic exoskeleton applications toward assisting community ambulation.

Effect of Assistance Timing in Knee Extensor Muscle Activation During Sit-to-Stand Using a Bilateral Robotic Knee Exoskeleton.

The population of older adults experiences a significant degradation in musculoskeletal structure, which hinders daily physical activities. Standing up from a seated position is difficult for mobility-challenged individuals since a significant amount of knee extensor moment is required to lift the body's center of mass. One solution to reduce the required muscle work during sit-to-stand is to utilize a powered exoskeleton system that can provide relevant knee extension assistance. However, the optimal exoskeleton assistance strategy for maximal biomechanical benefit is unknown for sit-to-stand tasks. To answer this, we explored the effect of assistance timing using a bilateral robotic exoskeleton on the user's knee extensor muscle activation. Assistance was provided at both knee joints from 0% to 65% of the sit-to-stand movement, with a maximum torque occurring at four different timings (10%, 25%, 40%, and 55%). Our experiment with five able-bodied subjects showed that the maximal benefit in knee extensor activation, 19.3% reduction, occurred when the assistance timing was delayed relative to the user's biological joint moment. Among four assistance conditions, two conditions with each peak occurring at 25% and 40% significantly reduced the muscle activation relative to the no assistance condition (p < 0.05). Additionally, our study results showed a U-shaped trend (R2= 0.93) in the user's muscle activation where the global optimum occurred between 25% and 40% peak timing conditions, indicating that there is an optimal level of assistance timing in maximizing the exoskeleton benefit.

Wearable Sensor-Based Step Length Estimation During Overground Locomotion Using a Deep Convolutional Neural Network.

Step length is a critical gait parameter that allows a quantitative assessment of gait asymmetry. Gait asymmetry can lead to many potential health threats such as joint degeneration, difficult balance control, and gait inefficiency. Therefore, accurate step length estimation is essential to understand gait asymmetry and provide appropriate clinical interventions or gait training programs. The conventional method for step length measurement relies on using foot-mounted inertial measurement units (IMUs). However, this may not be suitable for real-world applications due to sensor signal drift and the potential obtrusiveness of using distal sensors. To overcome this challenge, we propose a deep convolutional neural network-based step length estimation using only proximal wearable sensors (hip goniometer, trunk IMU, and thigh IMU) capable of generalizing to various walking speeds. To evaluate this approach, we utilized treadmill data collected from sixteen able-bodied subjects at different walking speeds. We tested our optimized model on the overground walking data. Our CNN model estimated the step length with an average mean absolute error of 2.89 ± 0.89 cm across all subjects and walking speeds. Since wearable sensors and CNN models are easily deployable in real-time, our study findings can provide personalized real-time step length monitoring in wearable assistive devices and gait training programs.

Continuous locomotion mode classification using a robotic hip exoskeleton.

Human augmentation through robotic exoskeleton technology can enhance the user's mobility for a wide range of ambulation tasks. This is done by providing assistance that is in line with the user's movement during different locomotion modes (e.g., ramps and stairs). Several machine learning techniques have been applied to classify such tasks on lower limb prostheses, but these strategies have not been applied extensively to exoskeleton systems which often rely on similar control inputs. Additionally, conventional methods often identify modes at a discrete time during the gait cycle which can delay the corresponding assistance to the user and potentially reduce overall exoskeleton benefit. We developed a gait phase-based Bayesian classifier that can classify five ambulation modes continuously throughout the gait cycle using only mechanical sensors on the device. From our five able-bodied subject experiment with a robotic hip exoskeleton, we found that implementing multiple models within the gait cycle can reduce the classification error rate by 35% compared to using a single model (p < 0.05). Furthermore, we found that utilizing bilateral sensor information can reduce the error by 43% compared to using a unilateral information (p < 0.05). Our study findings provide valuable information for future exoskeleton developers to utilize different on-board mechanical sensors to enhance mode classification for a faster update rate in the controller and provide more natural and seamless exoskeleton assistance between locomotion modes.