BACKGROUND: Most of the patients who survive stroke, spinal cord or others nervous system injuries, must face different challenges for a complete recovery of physical functional impairment. An accurate and recurrent assessment of the patient rehabilitation progress is very important. So far, wearable sensors (e.g. accelerometers, gyroscopes) and depth cameras have been used in medical rehabilitation for the automation of traditional motor assessments. Combined with machine learning techniques, these sensors are leading to novel metric systems for upper limb mobility assessment. OBJECTIVE: Review current research for objective and quantitative assessments of the upper limb movement, analyzing sensors used, health issues examined, and data processes applied such as: selected features, feature engineering approach, learning models and data processing techniques. METHOD: A systematic review conducted according to the PRISMA guidelines. EBSCOHOST discovery service was queried for relevant articles published from January 2014 to December 2018 with English language and scholarly peer reviewed journals limits. RESULTS: Of the 568 articles identified, 75 were assessed for eligibility and 43 were finally included and weighed for an in-depth analysis according to their ponderation. The reviewed studies show a wide use of sensors to capture raw data for subsequent motion analysis. CONCLUSION: As the volume of the data captured via these sensors increase, it makes sense to extract useful information about them such as prediction of performance scores, detection of movement impairments and measured progression of recovery.

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La inclusión de la población adulta en los procesos educativos universitarios, deriva un compromiso institucional de la Corporación Universitaria Americana que va en la línea de la calidad en la educación, porque se identifican las necesidades específicas en el proceso de formación de los estudiantes que están matriculados entre los 40 y 60 años de edad, y se construye, a partir del análisis contextual-experiencial, soluciones innovadoras a través de la incorporación de tecnologías cómo la visión por computador, reconocimiento de voz, aprendizaje de máquina, detección de movimientos, entre otros. Estas herramientas integradas a estudios de andragogía, se usan en el contexto educativo de la Corporación Universitaria Americana, y permiten la inclusión digital al aprovechar las competencias que han desarrollado los adultos en su experiencia de vida y los acercan a las tecnologías desde sus fortalezas, para crear prácticas significativas que les permiten acceder a nuevas oportunidades y desafíos en su entorno escolar, disminuyendo la curva de aprendizaje requerida para interactuar con las TIC.AbstractThe inclusion of the adult population in university educational processes, derived an institutional commitment to the American University Corporation that goes on the line of quality in education, because identify specific needs in the training process of the enrolled students between 40 and 60 years old, and it builds from contextual-experiential analysis, innovative solutions through the incorporation of technologies such computer vision, speech recognition, machine learning, motion tracking, among others. These tools integrated to andragogy studies, are used in the educational context of the American University Corporation, and enable the digital inclusion by leveraging the skills they have developed in their life experience and approach them to technologies from their strengths, to create significant practices that allow them to gain access to new opportunities and challenges in their school environment, reducing the learning curve required to interact with ICT.