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.

Investigations of the biomechanical parameters of robotic walker assisted gait are needed to allow modern rehabilitation strategies and to improve further technological developments. In this study, spatio-temporal gait parameters were assessed during normal and assisted ambulation with the Simbiosis walker model, a robotic walker with forearm supports. Six infra-red video cameras, integrated in a movement analysis system, were used for three-dimensional reconstruction of body segments and measurement of biomechanical variables during assisted ambulation. Results showed that walker-assisted gait was marked by an overall reduction of spatio-temporal parameters, especially gait speed, without modification in cadence-speed and stride length-speed relationships. Future work investigating such modality of assisted gait in clinical conditions are warranted and may contribute for a better understanding of user-device interaction forces and its impact over gait biomechanics.

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.

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Fibromyalgia (FM) is a chronic musculoskeletal disturbance that poses major challenges for diagnostic procedures. It is marked by a constellation of symptoms, such as widespread pain, chronic fatigue, sleep disturbances, osteoarthritis, among others. This work is a pilot study that aims to validate a diagnostic tool by analyzing sEMG signals of specific muscles during the performance of isotonic tasks. This work is a pilot study that aims to validate a diagnostic protocol with people affected by fibromyalgia. The objective was to indentify the behavior of five fatigue indicators: RMS, ARV, MNF, MDF and IAF, at 30%, 60% and 80% of MCV with a cutoff parameter k of 60%.

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 identifi ed 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.