RESUMEN
The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence.
Asunto(s)
Marcha , Realidad Virtual , Humanos , Fenómenos Biomecánicos/fisiología , Marcha/fisiología , Masculino , Femenino , Anciano , Dispositivo Exoesqueleto , Locomoción/fisiología , Caminata/fisiología , Andadores , Robótica/métodosRESUMEN
This paper presents a novel calibration procedure as a simple, yet powerful, method to place and align inertial sensors with body segments. The calibration can be easily replicated without the need of any additional tools. The proposed method is validated in three different applications: a computer mathematical simulation; a simplified joint composed of two semi-spheres interconnected by a universal goniometer; and a real gait test with five able-bodied subjects. Simulation results demonstrate that, after the calibration method is applied, the joint angles are correctly measured independently of previous sensor placement on the joint, thus validating the proposed procedure. In the cases of a simplified joint and a real gait test with human volunteers, the method also performs correctly, although secondary plane errors appear when compared with the simulation results. We believe that such errors are caused by limitations of the current inertial measurement unit (IMU) technology and fusion algorithms. In conclusion, the presented calibration procedure is an interesting option to solve the alignment problem when using IMUs for gait analysis.
Asunto(s)
Marcha/fisiología , Fisiología/métodos , Algoritmos , Artrometría Articular , Fenómenos Biomecánicos , Calibración , Simulación por Computador , Humanos , Articulaciones/fisiología , Pierna/fisiología , Reproducibilidad de los Resultados , RotaciónRESUMEN
INTRODUCTION:This study investigates a gait research protocol to assess the impact of a walker model with forearm supports on the kinematic parameters of the lower limb during locomotion. METHODS: Thirteen healthy participants without any history of gait dysfunction were enrolled in the experimental procedure. Spatiotemporal and kinematic gait parameters were calculated by using wireless inertial sensors and analyzed with Principal Component Analysis (PCA). The PCA method was selected to achieve dimension reduction and evaluate the main effects in gait performance during walker-assisted gait. Additionally, the interaction among the variables included in each Principal Component (PCs) derived from PCA is exposed to expand the understanding of the main differences between walker-assisted and unassisted gait conditions. RESULTS:The results of the statistical analysis identified four PCs that retained 65% of the data variability. These components were associated with spatiotemporal information, knee joint, hip joint and ankle joint motion, respectively. CONCLUSION: Assisted gait by a walker model with forearm supports was characterized by slower gait, shorter steps, larger double support phase and lower body vertical acceleration when compared with normal, unassisted walking.
RESUMEN
The study of fatigue is an important tool for diagnostics of disease, sports, ergonomics and robotics areas. This work deals with the analysis of sEMG most important fatigue muscle indicators with use of signal processing in isometric and isotonic tasks with the propose of standardizing fatigue protocol to select the data acquisition and processing with diagnostic proposes. As a result, the slope of the RMS, ARV and MNF indicators were successful to describe the fatigue behavior expected. Whereas that, MDF and AIF indicators failed in the description of fatigue. Similarly, the use of a constant load for sEMG data acquisition was the best strategy in both tasks.