This paper explores the design, control, construction, and leading manufacturers of upper limb rehabilitation robots through a thorough literature review. Utilizing databases such as Scopus, IEEE Xplore, Science Direct, Springer Link, and the Clinical Trials database, the research adhered to a rigorous screening process in accordance with PRISMA guidelines. This included analyzing abstracts and conducting comprehensive reviews of full articles when necessary. A total of fourteen relevant papers were systematically selected for in-depth analysis. The study offers a detailed classification of robotic technologies along with their Technology Readiness Levels (TRLs), discusses the primary challenges hindering their adoption, and proposes strategic research directions to address these issues. In conclusion, while upper limb robotic devices exhibit significant potential, persistent technological and design challenges must be addressed, underscoring the need for ongoing research and multidisciplinary collaboration to facilitate broader and more effective adoption.

Hoy en día, la medición de la dinámica respiratoria está infravalorada en el ámbito clínico y en la vida diaria de un sujeto y sigue representando un reto desde el punto de vista técnico y médico. En este artículo proponemos un concepto para medir algunos de sus parámetros, como la frecuencia respiratoria (FR), utilizando cuatro sensores inerciales. Se realizaron dos experimentos diferentes para validar el concepto. Analizamos la colocación más adecuada de cada sensor para evaluar esas características y estudiamos la fiabilidad del sistema para medir parámetros anormales de la respiración (taquipnea, bradipnea y retención de la respiración). Por último, realizamos mediciones en pacientes post-COVID-19, algunos de ellos con alteraciones respiratorias después de más de un año del diagnóstico. Los resultados experimentales mostraron que el sistema propuesto podría utilizarse potencialmente para medir la dinámica respiratoria en el ámbito clínico. Además, mientras que la FR puede calcularse fácilmente con cualquier sensor, otros parámetros deben medirse con un sensor en una posición determinada.

This paper presents an approach integrating principal component and silhouette analysis with clustering algorithms for fault detection in hydraulic systems. The methodology was validated through a study in which vibration and pressure signals were collected under normal and fault conditions. These signals were then processed through filtering and normalization, followed by dimensionality reduction using principal component analysis. The resulting lower-dimensional feature vectors retained the critical characteristics of both normal and faulty conditions and were subsequently fed into a clustering algorithm. The quality of the resulting clusters was evaluated using silhouette analysis, which offers a reliable means of assessing cluster quality and visualising the outcomes of fault classification. The study demonstrates the effectiveness of this method in accurately representing the patterns of normal and malfunctioning hydraulic pump conditions, ultimately leading to successful diagnostic results.

Valve-plate wear in axial piston pumps reduces volumetric efficiency, and data-driven classifiers often report near-perfect accuracy in grading it. This paper asks how much of such accuracy reflects the fault rather than the recording. Outlet and precharge pressure, leakage flow and housing vibration at four points were recorded on a test bench for five induced wear states (estimated volumetric efficiency 94.9–69.5%) at an unloaded and a loaded (≈700 psi) operating point, in a single campaign. Statistical features were classified with support vector machines, random forests and shallow neural networks under protocols of increasing difficulty. Record-level cross-validation separates the five states with 94–100% accuracy from pressure or from any single accelerometer. Leave-one-condition-out validation shows that only a coarse decision generalises to a wear state absent from training: significant degradation (efficiency below about 80%, where the pump loses its precharge pressure) is detected by the pressure channels at both operating points and by three accelerometers at the loaded point, whereas the fine grading and a continuous efficiency estimate (mean absolute error 3.6–7.0 points) do not transfer, nor do models between operating points. The results are delimited to one campaign with one mounting per wear state; multi-session validation on naturally worn components is required.