In this paper, we propose a multi-model human motion tracking approach based on wearable IMU sensors to track the user’s movements in real time. According to the four states of feet in the daily activities of human body, four kinematic models are established, namely the left-foot-root (LFR) kinematics model, the right-foot-root (RFR) kinematics model, and the double-foot-root (DFR) kinematics model (the above three are collectively referred to as foot-root kinematics models) and the pelvis-root (PR) kinematics model. And then a model switching mechanism is established. These kinematics models can dynamically switch during tracking. In the foot-root kinematic model, the displacement of the human body can be obtained according to the attitude transformation of the leg. In the pelvis-root kinematic model, the quadratic integration of the acceleration of the sensor accelerometer output is used to calculate the displacement. The four models complement each other so that the human motion can be tracked well. Meanwhile this multimodel human motion tracking approach is versatile and provides greater flexibility in capturing outdoor sports.

A Multi-model Human Motion Tracking Approach with Wearable IMU Sensors

The vestibular system (VS) is a sensory system that has a vital function in human life by serving to maintain balance. In this study, multifractal detrended fluctuation analysis (MFDFA) is applied to insole pressure sensor data collected from subjects in order to extract features to identify diseases related to VS dysfunction. We use the multifractal spectrum width as the feature to distinguish between healthy and diseased people. It is observed that multifractal behavior is more dominant and thus the spectrum is wider for healthy subjects, where we explain the reason as the long-range correlations of the small and large fluctuations of the time series for this group. We directly process the instantaneous pressure values to extract features in contrast to studies in the literature where gait analysis is based on investigation of gait dynamics (stride time, stance time, etc.) requiring long walking time. Thus, as the main innovation of this work, we detrend the data to give meaningful information even for a relatively short walk. Extracted feature set was input to fundamental classification algorithms where the Support-Vector-Machine (SVM) performed best with an average accuracy of 98.2% for the binary classification as healthy or suffering. This study is a substantial part of a big project where we finally aim to identify the specific VS disease that causes balance disorder and also determine the stage of the disease, if any. Within this scope, the achieved performance gives high motivation to work more deeply on the issue. 

/pdf/multifractal-detrended-fluctuation-analysis-of-insole-1k3saaud.pdf

Multifractal detrended fluctuation analysis of insole pressure sensor data to diagnose vestibular system disorders

Transferable Condition Monitoring for Linear Guidance Systems Using Anomaly Detection

Discrimination of vestibular function based on inertial sensors

A Wireless Accelerometer-Based Body Posture Stability Detection System and Its Application for Meditation Practitioners

This study is a part of a project where we target determining discriminative features to define diseases that cause disorders in the human vestibular system. For this purpose we aim to analyze the gait of the person. Among a number of parameters used for gait analysis, some make use of the footand knee positions. Hence the exact determination of position is of great importance. Here we use inertial sensors (IMU) placed on foot and knee in order to calculate the displacement by double integrating the free acceleration output data of the sensor. Thus, the overall position accuracy is highly dependent on the accuracy of the acceleration data where the offset and drift play great role in its corruption. We propose a method to minimize the error due to sensor offset and drift by utilizing the fact that there are gait intervals where the foot rests. The results are promising that the calculated average error is low; though the standard deviation needs some further amendment.

IMU Acceleration Drift Compensation for Position Tracking in Ambulatory Gait Analysis.

Cite this article as: Kara E, İkizoğlu S, Şahin K, Çakar T, Ataş A. A New Algorithm for Dynamic Vestibular System Analysis with Wearable Pressure and Motion Sensors. J Acad Res Med 2021;11(2):149-56 A New Algorithm for Dynamic Vestibular System Analysis with Wearable Pressure and Motion Sensors Giyilebilir Basınç ve Hareket Sensörleri ile Dinamik Vestibüler Sistem Analiz Algoritması Geliştirilmesi

/pdf/a-new-algorithm-for-dynamic-vestibular-system-analysis-with-70670y2ntt.pdf

A New Algorithm for Dynamic Vestibular System Analysis with Wearable Pressure and Motion Sensors

This study is the first step gone to develop a Machine Learning (ML) algorithm to be applied to sensory information collected from people to identify Vestibular System (VS) disorders. Three ML methods, the Support Vector Machine (SVM), SVM with Gaussian Kernel and Decision Tree are compared to determine the one with the highest accuracy to use for VS analysis. These methods are applied to the data set collected from groups both of healthy and suffering from VS disorders. All three methods had computation time in tens of milliseconds providing the possibility of real time processing in the field of identification of diseases related to VS imperfections. The assessment of the algorithms was based on processing of 22 features extracted from the dataset. SVM with Gaussian Kernel performed best with 81.3% accuracy. Following this step, some addition and removal of features is made to observe their effect on the training model. We noticed that some features are discriminative that they have significant influence on the overall accuracy. Thus, as a next step, the objective of this work is to apply some feature selection methods to find the most discriminative features to decrease the algorithm complexity while increasing the system accuracy. The ultimate goal of our study is to develop a ML algorithm embedded in wearable devices in order to diagnose people with VS-problems in their daily life.

Performance comparison of ML methods applied to motion sensory information for identification of vestibular system disorders

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Papers

IMU Acceleration Drift Compensation for Position Tracking in Ambulatory Gait Analysis.

A New Algorithm for Dynamic Vestibular System Analysis with Wearable Pressure and Motion Sensors

Performance comparison of ML methods applied to motion sensory information for identification of vestibular system disorders