Despite medical treatment focused on addressing walking disability, many millions of people with neurological conditions, like cerebral palsy (CP), struggle to maintain independent mobility. Lower limb exoskeletons and exosuits may hold potential for augmenting walking ability. However, it remains unknown whether these wearable robots are safe and beneficial for use outside of highly controlled laboratory environments, the demonstration of which is necessary for clinical translation. Here, we show that a lightweight, portable, ankle exoskeleton with an adaptable one-size-works-for-all assistance controller can improve energy efficiency and walking speed for individuals with CP spanning a wide spectrum of lower limb impairment in a multi-terrain real-world environment. Tested on an outdoor walking route with level, sloped, and stair terrain, robotic assistance resulted in a 15–18% (p ${=}$ 0.013–0.026) reduction in estimated energy cost and a 7–8% (p ${=}$ 0.001–0.004) increase in average walking speed across “shorter” 6-minute and “longer” 20-minute walking durations relative to unassisted walking. This study provides evidence that wearable robots may soon improve mobility in neighborhood, school, and community settings for individuals with CP.

Although the field of wearable robotic exoskeletons is rapidly expanding, there are several barriers to entry that discourage many from pursuing research in this area, ultimately hindering growth. Chief among these is the lengthy and costly development process to get an exoskeleton from conception to implementation and the necessity for a broad set of expertise. In addition, many exoskeletons are designed for a specific utility and are confined to the laboratory environment, limiting the flexibility of the designed system to adapt to answer new questions and explore new domains. To address these barriers, we present OpenExo, an open-source modular untethered exoskeleton framework that provides access to all aspects of the design process, including software, electronics, hardware, and control schemes. To demonstrate the utility of this exoskeleton framework, we performed benchtop and experimental validation testing with the system across multiple configurations, including hip-only incline assistance, ankle-only indoor and outdoor assistance, hip-and-ankle load carriage assistance, and elbow-only weightlifting assistance. All aspects of the software architecture, electrical components, hip and Bowden-cable transmission designs, and control schemes are freely available for other researchers to access, use, and modify when looking to address research questions in the field of wearable exoskeletons. Our hope is that OpenExo will accelerate the development and testing of new exoskeleton designs and control schemes while simultaneously encouraging others, including those who would have been turned away from entering the field, to explore new and unique research questions.

The clinical efficacy of robotic rehabilitation interventions hinges on appropriate neuromuscular recruitment from the patient. The first purpose of this study was to evaluate the use of supervised machine learning techniques to predict neuromuscular recruitment of the ankle plantar flexors during walking with ankle exoskeleton resistance in individuals with cerebral palsy (CP). The second goal of this study was to utilize the predictive models of plantar flexor recruitment in the design of a personalized biofeedback framework intended to improve (i.e., increase) user engagement when walking with resistance. First, we developed and trained multilayer perceptrons (MLPs), a type of artificial neural network (ANN), utilizing features extracted exclusively from the exoskeleton's onboard sensors, and demonstrated 85–87% accuracy, on average, in predicting muscle recruitment from electromyography measurements. Next, our participants completed a gait training session while receiving audio-visual biofeedback of their personalized real-time planar flexor recruitment predictions from the online MLP. We found that adding biofeedback to resistance elevated plantar flexor recruitment by 24 ± 16% compared to resistance alone. This study highlights the potential for online machine learning frameworks to improve the effectiveness and delivery of robotic rehabilitation systems in clinical populations.