Britam Arom Gómez Arias

By Pablo Aqueveque, Paulina Ortega, Esteban Pino, Francisco Saavedra, Enrique Germany and Britam Gómez

This chapter presents a review of the rehabilitation technologies for people who have suffered a stroke, comparing and analyzing the impact that these technologies have on their recovery in the short and long term. The problematic is presented, and motor impairments for upper and lower limbs are characterized. The goal of this chapter is to show novel trends and research for the assistance and treatment of motor impairment caused by strokes.

Part of the book: Physical Disabilities

By Paulina Ortega-Bastidas, Britam Gómez, Karen Barriga, Francisco Saavedra and Pablo Aqueveque

This chapter aims to address the different impairments in the balance after stroke, beginning with an introduction on the main dysfunctions that can be observed, specifically in different transfers as sit-to-stand and gait. Also, a review of the main test and assessment scales most used in the clinical settings in this population. Finally, the application of new technologies and the technological advances used in clinical settings for human analysis focusing on balance are addressed. For example, the types of technologies used, their applications, and the combination with the existing clinical assessment tools. As a closure, we explain the importance of early detection and treatment of balance impairments in the post-stroke population to prevent falls.

Part of the book: Post-Stroke Rehabilitation

By Britam Arom Gómez Arias, Sebastián Gonzalo Chávez Orellana, Paulina Cecilia Ortega-Bastidas and Pablo Esteban Aqueveque Navarro

This chapter discusses the potential of wearable technologies in predicting fall risks among older adults, a demographic susceptible to falls due to age-related walking ability decline. We aimed to explore the feasibility of portable body sensors, mobile apps, and smartwatches for real-time gait analysis in non-clinical, everyday settings. We used classification models like Random Forest, Support Vector Machine with a radial basis function kernel, and Logistic Regression to predict fall risks based on gait parameters. Notably, both Random Forest and Support Vector Machine models demonstrated over 72% accuracy, underscoring the critical role of feature selection and model choice in fall risk prediction. These technologies can enhance older adults’ quality of life by predicting fall risks. However, future developments should focus on technologies adapted to non-clinical environments, predictivity, and high-risk group usability. The integration of these features may enable more efficient fall risk assessment systems.

Part of the book: Human Gait