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It remains unknown whether variation of scores on the Medical Research Council (MRC) scale for muscle strength is associated with operator-independent techniques: dynamometry and surface electromyography (sEMG). This study aimed to evaluate whether the scores of the MRC strength scale are associated with instrumented measures of torque and muscle activity in post-stroke survivors with severe hemiparesis both before and after an intervention. Patients affected by a first ischemic or hemorrhagic stroke within 6 months before enrollment and with complete paresis were included in the study. The pre- and post-treatment assessments included the MRC strength scale, sEMG, and dynamometry assessment of the triceps brachii (TB) and biceps brachii (BB) as measures of maximal elbow extension and flexion torque, respectively. Proprioceptive-based training was used as a treatment model, which consisted of multidirectional exercises with verbal feedback. Each treatment session lasted 1 h/day, 5 days a week for a total 15 sessions. Nineteen individuals with stroke participated in the study. A significant correlation between outcome measures for the BB (MRC and sEMG p = 0.0177, ρ = 0.601; MRC and torque p = 0.0001, ρ = 0.867) and TB (MRC and sEMG p = 0.0026, ρ = 0.717; MRC and torque p = 0.0001, ρ = 0.873) were observed post intervention. Regression models revealed a relationship between the MRC score and sEMG and torque measures for both the TB and BB. The results confirmed that variation on the MRC strength scale is associated with variation in sEMG and torque measures, especially post intervention. The regression model showed a causal relationship between MRC scale scores, sEMG, and torque assessments.

Recent studies have investigated muscle synergies as biomarkers for stroke, but it remains controversial if muscle synergies and clinical observation convey the same information on motor impairment. We aim to identify whether muscle synergies and clinical scales convey the same information or not. Post-stroke patients were administered an upper limb treatment. Before (T0) and after (T1) treatment, we assessed motor performance with clinical scales and motor output with EMG-derived muscle synergies. We implemented an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to identify the underlying relationships among all variables, at T0 and T1, and a general linear regression model to infer any relationships between the similarity between the affected and unaffected synergies (Median-sp) and clinical outcomes at T0. Clinical variables improved with rehabilitation whereas muscle-synergy parameters did not show any significant change. EFA and CFA showed that clinical variables and muscle-synergy parameters (except Median-sp) were grouped into different factors. Regression model showed that Median-sp could be well predicted by clinical scales. The information underlying clinical scales and muscle synergies are therefore different. However, clinical scales well predicted the similarity between the affected and unaffected synergies. Our results may have implications on personalizing rehabilitation protocols.

A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution.