Arlindo Elias

An IMU-to-Body Alignment Method Applied to Human Gait Analysis

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.

Comparative muscle study fatigue with sEMG signals during the isotonic and isometric tasks for diagnostics purposes

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.

Smart Walkers: Advanced Robotic Human Walking-Aid Systems

In this book chapter, the authors present the Smart Walkers as robotic functional compensation devices for assisting mobility dysfunctions and empowering the human gait. First, general concepts of locomotion, mobility dysfunctions and assistive devices are presented. A special attention is given to the walkers, considering not only the large number of users, but mainly the rehabilitation and functional compensation potential of empowering the natural mobility. Following, robotic versions of wheeled-walkers for assisting locomotion dysfunctions are presented. In this context, the UFES Smart Walker is presented as an example of a robotic device focused on the user-machine multimodal interaction for obtaining a natural control strategy for the robotic device. Two developments are discussed: (i) an adaptive filtering strategy of the upper-body interaction forces is used in a Fuzzy-Logic based control system to generate navigation commands, and (ii) a robust inverse kinematics controller based on users-motion is presented as a new solution for controlling the Smart Walker motion. Finally, conclusions and future works in the field of walker-assisted gait is presented in the last section.

Robotic walkers from a clinical point of view: Feature-based classification and proposal of the UFES Walker

This paper presents a critical review of the state of the art of robotic walkers, emphasizing on the clinical applicability of the main features reported in such systems. A feature-based classification is proposed along with the presentation of the possible clinical scenarios, highlighting the potential of these devices for modern physical therapy practice. Based on the classification, the development of a novel robotic walker - the UFES Walker - is proposed with the most relevant features for controlled rehabilitation programs and, at the same time, suitable for functional compensation and domiciliary use.

Measurement of head relocation accuracy in the sagittal plane using static photogrammetry and inertial measurement unit

The high densities and complex arrays of joint mechanoreceptors and muscle spindles of the cervical spine suggest that disturbances of such structures may change the proprioceptive information to the neuromuscular system and thus, may play a key role in neck presentation. Errors in head positioning may arise in response to such proprioceptive disturbances and are considered to be a clinical sign of somatosensory impairment. The objective of this study is to compare the performance of a commercial 3D inertial measurement unit (IMU) with static photogrammetry in the head relocation accuracy test in the sagittal plane. Fourteen asymptomatic subjects were enrolled in the investigation. A paired observations experimental design was conducted and three error parameters (absolute, constant and variable) were calculated for each method. Results showed no significant difference between error parameter means obtained by both measurement methods. Bland and Altman plots were used to assess agreement levels between error estimates and showed good agreement levels among absolute and constant errors. The study concludes that the IMU is a valid method for head error position assessment in the sagittal plane, and future studies should focus on potential clinical applications involving different subgroups of neck pain patients.

Characterization and diagnosis of fibromyalgia based on fatigue analysis with sEMG signals

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%.