Josip Musić

Model based inertial sensing of human body motion kinematics in sit-to-stand movement

The paper presents the design and validation of a three-segment human body model. The model is aimed at the reconstruction of motion trajectories of the shank, thigh and HAT (Head-Arms-Trunk) segments in sit-to-stand-motion using low cost inertial sensors. For this purpose the Extended Kalman filter is applied for fusion of model data and data acquired through measurements. The simplifications, like motion constraint to sagittal plane, symmetry of movement and assumption of ideal joints are introduced in the model. Model validation was performed on simulated data and on measurements data acquired with the Optotrak optical motion analysis system. Obtained results are presented and discussed.

Nomadic Input on Mobile Devices: The Influence of Touch Input Technique and Walking Speed on Performance and Offset Modeling

In everyday life, people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this article, we analyze the influence of walking speed, gait pattern, and input techniques on commonly used performance parameters like error rate, accuracy, and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input, and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a trade-off between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods.

Modelling and correcting for the impact of the gait cycle on touch screen typing accuracy

Walking and typing on a smartphone is an extremely common interaction. Previous research has shown that error rates are higher when walking than when stationary. In this paper we analyse the acceleration data logged in an experiment in which users typed whilst walking, and extract the gait phase angle. We find statistically significant relationships between tapping time, error rate and gait phase angle. We then use the gait phase as an additional input to an offset model, and show that this allows more accurate touch interaction for walking users than a model which considers only the recorded tap position.

Performance of Compressive Sensing Image Reconstruction for Search and Rescue

In this letter, a system combining compressive sensing (CS)-based image reconstruction and object detection algorithm is introduced. The use of CS is a promising approach for search-and-rescue applications, since it highly reduces the amount of data that needs to be transmitted. However, the high-quality reconstruction of such images is a challenging task due to the complexity of structures and the number of tiny details, possibly being the objects of interest. Hence, the performance of image reconstruction is evaluated in terms of the missing data amount and the object detection quality. Object detection is performed by applying two-stage data segmentation algorithm based on mean shift clustering. The results quality is measured using structural similarity index and peak signal-to-noise ratio.

Is it possible to detect mobile phone user's attention based on accelerometer measurment of gait pattern?

Mobile phones have become ubiquitous in todays world. Their ever increasing computational power and sensing capabilities have made them well suited for number of tasks well beyond their original purpose of communication. But mobile phone usage while walking or driving can potentially be dangerous leading to serious injury or even death. In the paper we answer the question is it possible using only mobile phones embedded accelerometer to detect changes in gait pattern caused by changed attention level due to interaction with mobile device like reading on-screen text. Experimental measurements were conducted on 8 test subjects in indoor environment with each test subject performing 6 trials. Two different approaches based on gait phase and gait velocity were tested on recorded data in batch mode with more promising one implemented in real-time manner. Obtained results are presented and discussed and possible future research directions outlined.

Virtual hooping: teaching a phone about hula-hooping for fitness, fun and rehabilitation

The paper demonstrates the feasibility of using mobile phones for fitness and rehabilitation purposes by training them to recognise a users hula-hooping movements. It also proposes several parameters which can be used as a measure of rhythmic movement quality. Experimental measurements were achieved with two test subjects performing two sets of steady hula-hooping. The paper compares algorithm performance with accelerometer, gyroscope and magnetometer sensor readings. Analysis of the recorded data indicated that magnetometers had some advantages over accelerometers for reliable phase extraction. Hilbert transforms were used to extract the phase information, and a Dynamic Rhythmic Primitive Model was identified for the hula-hooping movement. Together these tools allow the creation of hula-hooping performance metrics which can be used in wellness, rehabilitation or entertainment applications for mobile devices. We outline open technical challenges and possible future research directions.

Gesture recognition system for real-time mobile robot control based on inertial sensors and motion strings

Abstract Navigating and controlling a mobile robot in an indoor or outdoor environment by using a range of body-worn sensors is becoming an increasingly interesting research area in the robotics community. In such scenarios, hand gestures offer some unique capabilities for human–robot interaction inherent to nonverbal communication with features and application scenarios not possible with the currently predominant vision-based systems. Therefore, in this paper, we propose and develop an effective inertial-sensor-based system, worn by the user, along with a microprocessor and wireless module for communication with the robot at distances of up to 250 m. Possible features describing hand-gesture dynamics are introduced and their feasibility is demonstrated in an off-line scenario by using several classification methods (e.g., random forests and artificial neural networks). Refined motion features are then used in K-means unsupervised clustering for motion primitive extraction, which forms the motion strings used for real-time classification. The system demonstrated an F 1 score of 90 . 05 % with the possibility of gesture spotting and null class classification (e.g., undefined gestures were discarded from the analysis). Finally, to demonstrate the feasibility of the proposed algorithm, it was implemented in an Arduino-based 8 -bit ATmega2560 microcontroller for control of a mobile, tracked robot platform.

Gradient Compressive Sensing for Image Data Reduction in UAV Based Search and Rescue in the Wild

Search and rescue operations usually require significant resources, personnel, equipment, and time. In order to optimize the resources and expenses and to increase the efficiency of operations, the use of unmanned aerial vehicles (UAVs) and aerial photography is considered for fast reconnaissance of large and unreachable terrains. The images are then transmitted to control center for automatic processing and pattern recognition. Furthermore, due to the limited transmission capacities and significant battery consumption for recording high resolution images, in this paper we consider the use of smart acquisition strategy with decreased amount of image pixels following the compressive sensing paradigm. The images are completely reconstructed in the control center prior to the application of image processing for suspicious objects detection. The efficiency of this combined approach depends on the amount of acquired data and also on the complexity of the scenery observed. The proposed approach is tested on various high resolution aerial images, while the achieved results are analyzed using different quality metrics and validation tests. Additionally, a user study is performed on the original images to provide the baseline object detection performance.

Comparison of Uncalibrated Model-Free Visual Servoing Methods for Small-Amplitude Movements: A Simulation Study

The paper compares the performance of several methods used for the estimation of an image Jacobian matrix in uncalibrated model-free visual servoing. This was achieved for an eye-in-hand configuration with small-amplitude movements with several sets of system parameters. The tested methods included the Broyden algorithm, Kalman and particle filters as well as the recently proposed population-based algorithm. The algorithms were tested in a simulation environment (Peter Corkes Robotic Toolbox for MATLAB) on a PUMA 560 robot. Several application scenarios were considered, including static point and dynamic trajectory tracking, with several characteristic shapes and three different speeds. Based on the obtained results, conclusions were drawn about the strengths and weaknesses of each method both for a particular setup and in general. Algorithm-switching was introduced and explored, since it might be expected to improve overall robot tracking performance with respect to the desired trajectory. Finally, possible future research directions are suggested.

Influence of human-computer interface elements on performance of teleoperated mobile robot

Mobile robots are becoming ubiquitous, with applications which usually include a degree of autonomy. However, due to uncertain and dynamic nature of operational environment, algorithms for autonomous operation might fail. In order to assist the robot, the human operator might need to take control over the robot from remote location. In order to efficiently and safely teleoperate the robot, the operator has to have high degree of situational awareness. This can be achieved with appropriate human-computer interface (HCI), so that the remote environment model constructed with sensor data is presented at appropriate time, and that robot commands can be issued intuitively and easily. In the research, influence of HCI elements on performance of teleoperated mobile robot was studied for several tasks and with several HCI setups. The user study was performed, in which accuracy and speed of completion of given tasks were measured on a real robot. Statistical analysis was performed in order to identify possible setup dependencies. It showed that, in majority of analysed cases and based on introduced metrics, there is no significant difference between the setups, and between the visual control and teleoperation. Finally, conclusions were drawn with emphasis on benefits of information technology in particular case.