Self-Organizing Maps and Fuzzy C-Means Algorithms on Gait Analysis Based on Inertial Sensors Data. In: Madureira AM, Abraham A, Gamboa D, Novais P, editors. Intelligent Systems Design and Applications. Cham: Springer International Publishing; 2017. pp. 197-205. [DOI: 10.1007/978-3-319-53480-0_20]

A global view on how local muscular fatigue affects human performance

There is a growing interest in scientific literature on identifying how and to what extent interventions applied to a specific body region influence the responses and functions of other seemingly unrelated body regions. To investigate such a construct, it is necessary to have a global multivariate model that considers the interaction among several variables that are involved in a specific task and how a local and acute impairment affects the behavior of the output of such a model. We developed an artificial neural network (ANN)-based multivariate model by using parameters of motor skills obtained from kinematic, postural control, joint torque, and proprioception variables to assess the local fatigue effects of the abductor hip muscles on the functional profile during a single-leg drop landing and a squatting task. Findings suggest that hip abductor muscles' local fatigue produces a significant effect on a general functional profile, built on different control systems. We propose that expanded and global approaches, such as the one used in this study, have great applicability and have the potential to serve as a tool that guarantees ecological validity of future investigations.

Validation of two hybrid approaches for clustering age-related groups based on gait kinematics data

Age-associated changes in walking parameters are relevant to recognize functional capacity and physical performance. However, the sensible nuances of slightly different gait patterns are hardly noticeable by inexperienced observers. Due to the complexity of this evaluation, we aimed at verifying the efficiency of applied hybrid-adaptive algorithms to cluster groups with similar gait patterns. Based on self-organizing maps (SOM), k-means clustering (KM), and fuzzy c-means (FCM), we compared the hybrid algorithms to a conventional FCM approach to cluster accordingly age-related groups. Additionally, we performed a relevance analysis to identify the principal gait characteristics. Our experiments, based on inertial-sensors data, comprised a sample of 180 healthy subjects, divided into age-related groups. The outcomes suggest that our methods outperformed the FCM algorithm, demonstrating a high accuracy (88%) and consistent sensitivity also to distinguish groups that presented a significant difference (p < .05) only in one of the six observed gait features. The applied algorithms showed a compatible performance, but the SOM + KM required less computation cost and, therefore, was more efficient. Furthermore, the results indicate the overall importance of cadence, as a measurement of physical performance, especially when clustering subjects by their age. Such output provides valuable information to healthcare professionals, concerning the subject's physical performance related to his age, supporting and guiding the physical evaluation.

Clustering of Self-Organizing Maps as a means to support gait kinematics analysis and symmetry evaluation

Gait analysis is relevant for the functional diagnostic of several musculoskeletal disorders. Walking patterns can be analyzed using techniques such as video processing and inertial measurement units (IMU). In this work, a Self-Organizing Maps (SOM) algorithm is applied to reduce the complexity of kinematic features obtained by IMU sensors of a sample of 40 individuals. Our system provides a simpler data representation (2-D graphic) than conventional methods, which often applies statistical analysis. We have tested the proposed method to analyze typical and simulated limping gait pattern under well-controlled conditions. Based on kinematic parameters and symmetry-related features, SOM algorithm was able to organize the sample in groups of subjects with three different gait patterns, normal and limping with each lower limb. Moreover, our system may be used to evaluate the recovery of a patient, offering intuitive information of his walking pattern in an assessment report. However, further research with atypical-gait subjects is necessary before applying such method as a clinical tool.

Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System

There is a direct relationship between the prevalence of musculoskeletal disorders of the temporomandibular joint and orofacial disorders. A well-elaborated analysis of the jaw movements provides relevant information for healthcare professionals to conclude their diagnosis. Different approaches have been explored to track jaw movements such that the mastication analysis is getting less subjective; however, all methods are still highly subjective, and the quality of the assessments depends much on the experience of the health professional. In this paper, an accurate and non-invasive method based on a commercial low-cost inertial sensor (MPU6050) to measure jaw movements is proposed. The jaw-movement feature values are compared to the obtained with clinical analysis, showing no statistically significant difference between both methods. Moreover, We propose to use unsupervised paradigm approaches to cluster mastication patterns of healthy subjects and simulated patients with facial trauma. Two techniques were used in this paper to instantiate the method: Kohonen’s Self-Organizing Maps and K-Means Clustering. Both algorithms have excellent performances to process jaw-movements data, showing encouraging results and potential to bring a full assessment of the masticatory function. The proposed method can be applied in real-time providing relevant dynamic information for health-care professionals.

A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.