Stroke-induced hemiparetic gait is characteristically slow and metabolically expensive. Passive assistive devices such as ankle-foot orthoses are often prescribed to increase function and independence after stroke; however, walking remains highly impaired despite-and perhaps because of-their use. We sought to determine whether a soft wearable robot (exosuit) designed to supplement the paretic limb's residual ability to generate both forward propulsion and ground clearance could facilitate more normal walking after stroke. Exosuits transmit mechanical power generated by actuators to a wearer through the interaction of garment-like, functional textile anchors and cable-based transmissions. We evaluated the immediate effects of an exosuit actively assisting the paretic limb of individuals in the chronic phase of stroke recovery during treadmill and overground walking. Using controlled, treadmill-based biomechanical investigation, we demonstrate that exosuits can function in synchrony with a wearer's paretic limb to facilitate an immediate 5.33 ± 0.91°increase in the paretic ankle's swing phase dorsiflexion and 11 ± 3% increase in the paretic limb's generation of forward propulsion (P < 0.05). These improvements in paretic limb function contributed to a 20 ± 4% reduction in forward propulsion interlimb asymmetry and a 10 ± 3% reduction in the energy cost of walking, which is equivalent to a 32 ± 9% reduction in the metabolic burden associated with poststroke walking. Relatively low assistance (~12% of biological torques) delivered with a lightweight and nonrestrictive exosuit was sufficient to facilitate more normal walking in ambulatory individuals after stroke. Future work will focus on understanding how exosuit-induced improvements in walking performance may be leveraged to improve mobility after stroke.

Background A higher energy cost of walking poststroke has been linked to reduced walking performance and reduced participation in the community. Objective To determine the contribution of post-intervention improvements in walking speed and spatiotemporal gait asymmetry to the reduction of the energy cost of walking after stroke. Methods Forty-two subjects with chronic hemiparesis (> 6 months poststroke) were recruited to participate in 12 weeks of walking rehabilitation. The energy cost of walking, walking speed, and step length, swing time, and stance time asymmetries were calculated pre- and posttraining. Sequential regression analyses tested the cross-sectional (ie, pretraining) and longitudinal (ie, posttraining changes) relationships between the energy cost of walking versus speed and each measure of asymmetry. Results Pretraining walking speed (β = −.506) and swing time asymmetry (β = .403) predicted pretraining energy costs (adjR2 = .713, F(3,37) = 34.05, p < .001). In contrast, change in walking speed (β = .340) and change in step length asymmetry (β = .934) predicted change in energy costs with a significant interaction between these independent predictors (adjR2 = .699, F(4,31) = 21.326, p < .001). Moderation by the direction or the magnitude of pretraining asymmetry was not found. Conclusions For persons in the chronic phase of stroke recovery, faster and more symmetric walking after intervention appears to be more energetically advantageous than merely walking faster or more symmetric. This finding has important functional implications given the relationship between the energy cost of walking and community walking participation.

Recent technological advancements have enabled the creation of portable, low-cost, and unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation. The application of wearable sensors to movement tracking has emerged as a promising paradigm to enhance the care provided to patients with neurological or musculoskeletal conditions. These sensors enable quantification of motor behavior across disparate patient populations and emerging research shows their potential for identifying motor biomarkers, differentiating between restitution and compensation motor recovery mechanisms, remote monitoring, tele-rehabilitation, and robotics. Moreover, the big data recorded across these applications serve as a pathway to personalized and precision medicine. This paper presents state-of-the-art and next generation wearable movement sensors, ranging from inertial measurement units to soft sensors. An overview of clinical applications is presented across a wide spectrum of conditions that have potential to benefit from wearable sensors, including stroke, movement disorders, knee osteoarthritis, and running injuries. Complementary applications enabled by next-generation sensors that will enable point-of-care monitoring of neural activity and muscle dynamics during movement are also discussed.