Sharif Digital Repository

Localization-capable inertial motion capture algorithms rely on zero-velocity updates (ZUPT), usually as measurements in a Kalman filtering scheme, for position and attitude error control. As ZUPTs are only applicable during the static phases a link goes through, estimation errors grow during dynamic ones. This error growth may somewhat be mitigated by imposing biomechanical constraints in multi-sensor systems. Error reduction is also possible by optimization-based methods that incorporate the dynamic and static constraints governing the system behavior over a period of time (e.g. the dynamic network algorithm); when this period includes multiple static phases for a link, its estimation... 

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the... 

Human motion tracking has many applications in biomedical and industrial services. Low-cost inertial/magnetic sensors are widely used in human motion capture systems to obtain the orientation of the human body segments. In this paper, we have presented a quaternion-based unscented Kalman filter algorithm to fuse inertial/magnetic sensors measurements for tracking human arm movements. In order to have a better estimation of the orientation of the forearm and the upper arm, a constraint equation was developed based on the relative velocity of the elbow joint with respect to the inertial sensors attached to the forearm and the upper arm. Also to compensate for fast body motions, we adapted the... 

Motion capture systems are used to gauge the kinematic features of the motion in numerous fields of research. Despite superb accuracy performance, the commercial systems are costly and difficult to use. To solve these issues, Kinect has been proposed as a low-priced markerless motion capture sensor, and its accuracy has been assessed using previous motion capture systems. However, in many of these studies, the anatomical joint angles captured using the Kinect are compared to the 3D rotation angles reported by the gold standard motion capture systems. These incompatibilities in the determination of the human joint angles can lead to higher error estimation. To accomplish a valid accuracy... 

Human motion tracking has many applications in biomedical and industrial services. Low-cost inertial/magnetic sensors are widely used in human motion capture systems to obtain the orientation of the human body segments. In this paper, we have presented a quaternion-based unscented Kalman filter algorithm to fuse inertial/magnetic sensors measurements for tracking human arm movements. In order to have a better estimation of the orientation of the forearm and the upper arm, a constraint equation was developed based on the relative velocity of the elbow joint with respect to the inertial sensors attached to the forearm and the upper arm. Also to compensate for fast body motions, we adapted the... 

Inspired by algorithms utilized in inertial navigation, an inertial motion capturing algorithm capable of position and heading estimation is introduced. The fusion algorithm is capable of real-time link geometry estimation, which allows for the imposition of biomechanical constraints without a priori knowledge regarding sensor placements. Furthermore, the algorithm estimates gyroscope and accelerometer bias, scaling, and non-orthogonality parameters in real-time. The stationary phases of the links, during which pseudo-measurements such as zero velocity or heading stabilization updates are applied, are detected using optically trained neural networks with buffered accelerometer and gyroscope... 

Evaluation of workers’ body posture in workstations is a prerequisite to estimate spinal loads and assess risk of injury for the subsequent design of preventive interventions. The Microsoft Kinect™ sensor is, in this regard, advantageous over the traditional skin-marker-based optical motion capture systems for being marker-less, portable, cost-effective, and easy-to-use in real workplaces. While several studies have demonstrated the validity/reliability of the Kinect for posture measurements especially during gait trials, its capability to adequately drive a detailed spine musculoskeletal model for injury risk assessments remains to be investigated. Lumbosacral (L5-S1) load predictions of a... 

Purpose: There is a scarcity of studies evaluating the variability of couplings between proximal segments (trunk, pelvis, and thigh) in individuals with patellofemoral pain (PFP) while emerging evidence has suggested that aberrant motions of trunk and pelvis can have a contributory role in the etiology of PFP. The purpose of this study was, therefore, to evaluate the trunk, pelvis, and thigh intersegmental coordination variability in PFP compared with healthy individuals. Methods: Thirty-four participants (17 with PFP and 17 healthy controls) walked (at preferred speed) and ran (at preferred and fixed speed) on a treadmill each trial for 30 seconds. Three-dimensional kinematics were recorded... 

To date various commercial systems have been used in the GAIT analysis. These systems have some difficulties for clinical use, such as interfering with normal movement and high prices. The possibility of utilization of Kinect as a sensor for GAIT analysis has been studied in this research. The accuracy of Kinect in calculation of GAIT parameters such as lower limb joint angles, stride time, and stride length were computed during normal walking. The effects of the sensor's position and direction relative to the walkway were also investigated. The Kinect sensor was installed at different positions toward the motion path. In each position the data was recorded by both Kinect and a commercial... 

Sign language is a non-verbal communication tool used by the deaf. A robust sign language recognition framework is needed to develop Human–Robot Interaction (HRI) platforms that are able to interact with humans via sign language. Iranian sign language (ISL) is composed of both static postures and dynamic gestures of the hand and fingers. In this paper, we present a robust framework using a Deep Neural Network (DNN) to recognize dynamic ISL gestures captured by motion capture gloves in Real-Time. To this end, first, a dataset of fifteen ISL classes was collected in time series; then, this dataset was virtually augmented and pre-processed using the “state-image” method to produce a unique...