Many natural surfaces are capable of rapidly shedding water droplets—a phenomenon that has been attributed to the presence of low solid fraction textures (Φ_s ~ 0.01). However, recent observations revealed the presence of unusually high solid fraction nanoscale textures (Φ_s ~ 0.25 to 0.64) on water-repellent insect surfaces, which cannot be explained by existing wetting theories. Here, we show that the contact time of bouncing droplets on high solid fraction surfaces can be reduced by reducing the texture size to ~100 nm. We demonstrated that the texture size–dependent contact time reduction could be attributed to the dominance of line tension on nanotextures and that compact arrangement of nanotextures is essential to withstand the impact pressure of raindrops. Our findings illustrate a potential survival strategy of insects to rapidly shed impacting raindrops, and suggest a previously unidentified design principle to engineering robust water-repellent materials for applications including miniaturized drones.

Many patients suffer from declined motor abilities after a brain injury. To provide appropriate rehabilitation programs and encourage motor-impaired patients to participate further in rehabilitation, sufficient and easy evaluation methodologies are necessary. This study is focused on the sit-to-stand motion of post-stroke patients because it is an important daily activity. Our previous study utilized muscle synergies (synchronized muscle activation) to classify the degree of motor impairment in patients and proposed appropriate rehabilitation methodologies. However, in our previous study, the patient was required to attach electromyography sensors to his/her body; thus, it was difficult to evaluate motor ability in daily circumstances. Here, we developed a handrail-type sensor that can measure the force applied to it. Using temporal features of the force data, the relationship between the degree of motor impairment and temporal features was clarified, and a classification model was developed using a random forest model to determine the degree of motor impairment in hemiplegic patients. The results show that hemiplegic patients with severe motor impairments tend to apply greater force to the handrail and use the handrail for a longer period. It was also determined that patients with severe motor impairments did not move forward while standing up, but relied more on the handrail to pull their upper body upward as compared to patients with moderate impairments. Furthermore, based on the developed classification model, patients were successfully classified as having severe or moderate impairments. The developed classification model can also detect long-term patient recovery. The handrail-type sensor does not require additional sensors on the patient’s body and provides an easy evaluation methodology.

Post-stroke gait dysfunction is biomechanically heterogeneous, yet biomechanically informed classifications of functional walking remain underdeveloped. In particular, there is a lack of clinically accessible methods for classifying gait deficits that account for propulsion impairments—a historically laboratory-dependent gait parameter requiring measurement with force plate systems. This study examined whether propulsion impairment can be classified by combining a global measure of walking function (i.e., the 10 m walk test speed) with specific measures of dynamic walking ability derived from the Functional Gait Assessment (FGA). Forty participants >6 months post-stroke completed biomechanical evaluations quantifying propulsion during walking and clinical assessments including the FGA. Multivariable stepwise regression identified the FGA items most strongly associated with paretic propulsion. Models augmented with these FGA items explained 15% greater variance in the paretic propulsion peak and 7% greater variance in paretic propulsion impulse compared with models using Comfortable Walking Speed (CWS) alone. Incorporating FGA items also yielded the highest overall accuracy (72.5% vs. 60% with CWS alone) and best per-class performance in propulsion severity classification. These findings establish the co-assessment of walking speed and targeted FGA items as a clinically feasible approach to biomechanically informed classification of post-stroke gait dysfunction.

Measuring neuromotor control after stroke is crucial for identifying the mechanisms underlying asymmetrical walking and guiding rehabilitation. The lower extremity portion of the Fugl-Meyer (FM-LE) and the number of muscle synergies are commonly used measures, but have important limitations. The dynamic motor control index has emerged as a complementary metric, yet its relationship to established clinical measures (i.e., FM-LE), muscle synergy number, and gait biomechanics remains unclear. This study evaluated the ability of the dynamic motor control index to quantify post-stroke neuromotor impairment relative to FM-LE and muscle synergy number and examined its relationship with propulsion asymmetry. Electromyography data from 22 individuals post-stroke and 31 neurotypical controls were analyzed using non-negative matrix factorization. The dynamic motor control index and not the muscle synergy number differentiated paretic, non-paretic, and neurotypical limbs (χ2(2) = 27.57, p < .001). It also differed significantly between less and more impaired individuals classified by FM-LE ( p = .05) and demonstrated good discriminative performance between these groups (AUC: 0.777, p = .017). The index also moderated the relationship between FM-LE and propulsion asymmetry (ΔR ² = 0.223, p = .007). These findings support the dynamic motor control index as a clinically relevant msarker of post-stroke neuromotor impairment and recovery.

Background: Gait propulsion (anterior ground reaction force) is commonly impaired after stroke. Force plates and wearable sensors can quantify propulsion but are not routinely available in clinical settings. Walking speed alone does not adequately capture the heterogeneity of propulsion deficits. Objective: To determine whether standardized clinical measures can estimate post- stroke propulsion metrics and classify propulsion impairment. Methods: Forty individuals post-stroke participated: 33 for model development and cross-validation (11 with repeated observations for longitudinal validation) and 7 for independent-cohort validation. Clinical measures included the 6-Minute Walk Test (6MWT), comfortable walking speed (CWS), Functional Gait Assessment (FGA), Fugl- Meyer Assessment of the Lower Extremity (FMA-LE), Short Physical Performance Battery (SPPB), Timed Up and Go test (TUG), and stroke chronicity. Regression models were developed to estimate three propulsion metrics: paretic propulsion peak and impulse, and interlimb propulsion symmetry. Model performance was evaluated using R ² and RMSE. Propulsion impairment was classified from symmetry estimates using a prespecified cutoff of 31.5%, with classification accuracy reported. Results: Models using the 6MWT, SPPB, TUG, and chronicity estimated paretic propulsion peak (R ² = 0.77, RMSE = 3.00 %body weight) and impulse (R ² = 0.81, RMSE = 0.65 %body weight·%stance phase). FMA-LE, SPPB, TUG, and chronicity estimated propulsion symmetry (R ² = 0.75, RMSE = 8.56%) and classified propulsion impairment with 84.8% accuracy. Classification accuracy was 91.7% in longitudinal validation and 85.7% in independent-cohort validation. Conclusions: A multidomain clinical assessment framework may support practical and accurate estimation and classification of post-stroke propulsion impairment in clinical settings where specialized instrumentation is unavailable.