Head Gesture Recognition using a 6DOF Inertial IMU

Sensors, 2010

Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb's orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital.

2007

Purpose -This paper seeks to present an inertial motion tracking system for monitoring movements of human upper limbs in order to support a homebased rehabilitation scheme in which the recovery of stroke patients' motor function through repetitive exercises needs to be continuously monitored and appropriately evaluated. Design/methodology/approach -Two inertial sensors are placed on the upper and lower arms in order to obtain acceleration and turning rates. Then the position of the upper limbs can be deduced by using the kinematical model of the upper limbs that was designed in the previous paper. The tracking system starts from inertial data acquisition and pre-filtering, followed by a number of processes such as transformation of coordinate systems of sensor data, and kinematical modelling and optimization of position estimation. Findings -The motion detector using the proposed kinematic model only has drifts in the measurements. Fusion of acceleration and orientation data can effectively solve the drift problem without the involvement of a Kalman filter.

EasyChair Preprints, 2019

Powered wheelchairs provide the only means of mobility for many people with severe motor disabilities. When the lower and upper limbs are affected, available interfaces may be impossible or very difficult to use, or not very efficient. In this paper we propose an egocentric interface based on inertial sensors placed on the user’s head. This interface is based on movements of the head, admitting different null-positions of the head depending on user’s posture, and allows continuous direction and speed commands to drive the wheelchair. Though, the development of an inertial interface for driving a wheelchair presents several challenges, namely, it must deal with: 1) the simultaneous movements of the head and wheelchair, each one with its own coordinate system; and 2) the unconstrained free-movements of the head. Therefore, the two coordinate systems need to be combined and several safety procedures are required to ensure only admissible commands. In this paper we describe the overall ...

PLOS ONE

To facilitate hand gesture recognition, we investigated the use of acoustic signals with an accelerometer and gyroscope at the human wrist. As a proof-of-concept, the prototype consisted of 10 microphone units in contact with the skin placed around the wrist along with an inertial measurement unit (IMU). The gesture recognition performance was evaluated through the identification of 13 gestures used in daily life. The optimal area for acoustic sensor placement at the wrist was examined using the minimum redundancy and maximum relevance feature selection algorithm. We recruited 10 subjects to perform over 10 trials for each set of hand gestures. The accuracy was 75% for a general model with the top 25 features selected, and the intra-subject average classification accuracy was over 80% with the same features using one microphone unit at the mid-anterior wrist and an IMU. These results indicate that acoustic signatures from the human wrist can aid IMU sensing for hand gesture recognition, and the selection of a few common features for all subjects could help with building a general model. The proposed multimodal framework helps address the single IMU sensing bottleneck for hand gestures during arm movement and/or locomotion.

Proceedings of the International Conference on Biomedical Electronics and Devices, 2009

The scope of this paper is the presentation of experiments which involve measurements and identification of human movements by using the inertial sensors. We describe the purpose, design and obtained results of two experiments, as well as our future plans which include the exploration of the forces acting at spine segments by measurements with inertial sensors. The first experiment implemented the method for measuring the range of motion (RoM) of head in transverse plane . It was done in the Laboratory of Biomechanics and Automatic Control -LaBACS, University of Split. In the second experiment we analyzed the standing -up movement and we used the robot assistive device for the support of human while performing the standing -up task. Measurements for purposes of this experiment were done in the Laboratory of Biomedical Engineering and Robotics, University of Ljubljana. We have proposed the new method which uses the Extended Kalman filtering for combining the data acquired from inertial sensor measurements of standing -up movement with data from the dynamic human body model . Our plans regarding the next experiment are focused on the identification of the spinal load during sitting and standing, by using the inertial sensors measurement system.

2010 IEEE International Conference on Robotics and Biomimetics, 2010

Movement & Sport Sciences - Science & Motricité, 2016

The aim of this study was to evaluate the reproducibility of a protocol using inertial sensors in order to characterize primary and associated movements of the head. Twenty-two subjects were evaluated twice in the same experimental conditions (3 days interval). Two inertial sensors allowed the evaluation of the range of movement of the head. Three complete cycles of movements were realised in each plane. A patient suffering from cervical dystonia was also evaluated with the same protocol before and after self-training program. Results show a good reproducibility for all ranges of movement except for the associated movements realised in the sagittal plane. Thereafter, our protocol allowed us to notice an improvement of the kinematics of the head after the self-training program followed by the patient. These results show the reproducibility of the inertial sensors as evaluation tools for head movements, a change in the proposed method will allow a generalization to cervical movements.

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018

Dyscalculia is a learning difficulty hindering fundamental arithmetical competence. Children with dyscalculia often have difficulties in engaging in lessons taught with traditional teaching methods. In contrast, an educational game is an attractive alternative. Recent educational studies have shown that gestures could have a positive impact in learning. With the recent development of low cost wearable sensors, a gesture based educational game could be used as a tool to improve the learning outcomes particularly for children with dyscalculia. In this paper, two generic gesture recognition methods are proposed for developing an interactive educational game with wearable inertial sensors. The first method is a multilayered perceptron classifier based on the accelerometer and gyroscope readings to recognize hand gestures. As gyroscope is more power demanding and not all low-cost wearable device has a gyroscope, we have simplified the method using a nearest centroid classifier for classifying hand gestures with only the accelerometer readings. The method has been integrated into open-source educational games. Experimental results based on 5 subjects have demonstrated the accuracy of inertial sensor based hand gesture recognitions. The results have shown that both methods can recognize 15 different hand gestures with the accuracy over 93%.

Sensors

Relative orientation estimation between the hand and its fingers is important in many applications, such as virtual reality (VR), augmented reality (AR) and rehabilitation. It is still quite a big challenge to do the estimation by only exploiting inertial measurement units (IMUs) because of the integration drift that occurs in most approaches. When the hand is functionally used, there are many instances in which hand and finger tips move together, experiencing almost the same angular velocities, and in some of these cases, almost the same accelerations are measured in different 3D coordinate systems. Therefore, we hypothesize that relative orientations between the hand and the finger tips can be adequately estimated using 3D IMUs during such designated events (DEs) and in between these events. We fused this extra information from the DEs and IMU data with an extended Kalman filter (EKF). Our results show that errors in relative orientation can be smaller than five degrees if DEs are...

2021

The automation of human gestures is gaining increasing importance in manufacturing. Indeed, robots support operators by simplifying their tasks in a shared workspace. However, human-robot collaboration can be improved by identifying human actions and then developing adaptive control algorithms for the robot. Accordingly, the aim of this study was to classify industrial tasks based on accelerations signals of human upper limbs. Two magnetic inertial measurement units (MIMUs) on the upper limb of ten healthy young subjects acquired pick and place gestures at three different heights. Peaks were detected from MIMUs accelerations and were adopted to classify gestures through a Linear Discriminant Analysis. The method was applied firstly including two MIMUs and then one at a time. Results demonstrated that the placement of at least one MIMU on the upper arm or forearm is suitable to achieve good recognition performances. Overall, features extracted from MIMUs signals can be used to define...