Accuracy of machine learning algorithms for the assessment of upper-limb motor impairments in patients with post-stroke hemiparesis: A systematic review and meta-analysis.

BACKGROUND: The assessment of motor function is vital in post-stroke rehabilitation protocols, and it is imperative to obtain an objective and quantitative measurement of motor function. There are some innovative machine learning algorithms that can be applied in order to automate the assessment of upper extremity motor function. OBJECTIVES: To perform a systematic review and meta-analysis of the efficacy of machine learning algorithms for assessing upper limb motor function in post-stroke patients and compare these algorithms to clinical assessment. MATERIAL AND METHODS: The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database. The review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. The search was performed using 6 electronic databases. The meta-analysis was performed with the data from the correlation coefficients using a random model. RESULTS: The initial search yielded 1626 records, but only 8 studies fully met the eligibility criteria. The studies reported strong and very strong correlations between the algorithms tested and clinical assessment. The meta-analysis revealed a lack of homogeneity (I2 = 85.29%, Q = 48.15), which is attributable to the heterogeneity of the included studies. CONCLUSION: Automated systems using machine learning algorithms could support therapists in assessing upper extremity motor function in post-stroke patients. However, to draw more robust conclusions, methodological designs that minimize the risk of bias and increase the quality of the methodology of future studies are required.

Fiber Optic Sensors for Vital Signs Monitoring. A Review of Its Practicality in the Health Field.

Vital signs not only reflect essential functions of the human body but also symptoms of a more serious problem within the anatomy; they are well used for physical monitoring, caloric expenditure, and performance before a possible symptom of a massive failure-a great variety of possibilities that together form a first line of basic diagnosis and follow-up on the health and general condition of a person. This review includes a brief theory about fiber optic sensors' operation and summarizes many research works carried out with them in which their operation and effectiveness are promoted to register some vital sign(s) as a possibility for their use in the medical, health care, and life support fields. The review presents methods and techniques to improve sensitivity in monitoring vital signs, such as the use of doping agents or coatings for optical fiber (OF) that provide stability and resistance to the external factors from which they must be protected in in vivo situations. It has been observed that most of these sensors work with single-mode optical fibers (SMF) in a spectral range of 1550 nm, while only some work in the visible spectrum (Vis); the vast majority, operate through fiber Bragg gratings (FBG), long-period fiber gratings (LPFG), and interferometers. These sensors have brought great advances to the measurement of vital signs, especially with regard to respiratory rate; however, many express the possibility of monitoring other vital signs through mathematical calculations, algorithms, or auxiliary devices. Their advantages due to miniaturization, immunity to electromagnetic interference, and the absence of a power source makes them truly desirable for everyday use at all times.