Ivo Stancic

New Kinematic Parameters for Quantifying Irregularities in the Human and Humanoid Robot Gait

Gait patterns of humans and humanoid robots are often described by analysing changes in angular rotation of hip, knee and ankle joints during one gait cycle. Each joint displays specific behaviour and irregularities of the gait pattern could be detected by measuring displacements from the normal rotation curve, while small deviations of individual gait characteristics are usually not easily detected. In this paper, an advanced gait analysis method is proposed, which incorporates analysis of angular data and its derivations of hip, knee, and ankle joints, presented in the phase plane. The gait kinematics was measured using a system based on active markers and fast digital cameras. The experiment included measurements on thirty healthy, barefoot humans while walking on a treadmill. We also simulated types of irregular gait, by measurements on subjects wearing knee constraints. The new kinematic parameters which are introduced clearly indicated the discrepancy between normal, healthy gait trials and irregular gait trials. The proposed gait factor parameter is a valuable measure for the detection of irregularities in gait patterns of humans and humanoid robots.

Multichannel Protocols for User-Friendly and Scalable Initialization of Sensor Networks

We consider the classical problem of establishing initial secu- rity associations in wireless sensor networks. More specifically, we focus on pre-deployment phase in which sensor nodes have not yet been loaded with shared secrets or other forms of authentic information. In this paper, we propose two novel multichannel protocols for ini- tialization of large scale wireless sensor networks. The first protocol uses only secret key cryptography and is suitable for CPU-constrained sensor nodes. The second protocol is based on public key cryptography. Both protocols involve communication over a bidirectional radio channel and an unidirectional out-of-band visible light channel. A notable feature of the proposed public key-based key deployment protocol is that it is designed to be secure in a very strong attacker model, where an attacker can eavesdrop, jam and modify transmitted messages by adding his own message to both a radio and a visible light channel; the attacker however cannot disable the visible light communication channel. We show that many existing protocols that rely on the visible light channel are insecure in this strong adversary model. We implemented the proposed protocols on the Meshnetics wireless sensor platform. The proposed protocols are cheap to implement, secure in the very strong attacker model, easy to use and scalable. We also de- signed and tested a simple random number generator suitable for sensor platforms.

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.

Wake-on-a-Schedule: Energy-aware Communication in Wi-Fi Networks

1 Abstract—Excessive energy consumption of mobile device Wi-Fi (IEEE 802.11x) interface is limiting its operational time on batteries, and impacts total energy consumption of electronic devices. In recent years research community has invested great effort in better efficiency of energy consumption. However, there is still a space for improvement. Wi-Fi devices connected to the single AP (Access Point) compete for the medium during data exchange. However, due to the performance anomaly in 802.11 networks, a low data rate device will force all other devices connected to the AP to communicate at low rate, which will increase the total energy consumption of these devices. Wake-on-a-Schedule algorithm is proposed reducing the energy consumption of devices placed in the area with the weaker signal by scheduling the data packets for each client on the server side which will not allow clients to compete for the Wi-Fi medium. Through extensive measurements we show that our algorithm can save up to 60% of energy consumption on the client side.

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.

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.

Active IR System for Projectile Detection and Tracking

Positioning systems based on location fingerprinting have become an area of intense research, mainly with the aim of providing indoor localization. Many challenges arise when trying to ...

Compressive sensing for reconstruction of 3D point clouds in smart systems

Performing an accurate 3D surface scan of everyday objects is sometimes difficult to achieve. Using the 3D scanner as a main sensor in a fast-moving mobile robot emphasizes this issue even further. When small robots with limited payload are considered, the professional Lidar systems are not likely to be embedded due to their weight, dimensions and/or high cost. Introduction of simple structured-light scanners makes possible fast scanning, effective robot detection and evasion of obstacles. Nevertheless, some obstacles may still be difficult to detect and recognize, primarily due to limitations of scanners hardware which results in a low number of reconstructed surface points. In this paper a compressed sensing technique, primarily used for the reconstruction of 2D images, is utilized to enhance the quality of 3D scan, by increasing the number of reconstructed 3D points to the scanners theoretical maximum. Obtained results demonstrated the feasibility of the approach in terms of mean square error.

Learning from depth sensor data using inductive logic programming

The problem of detecting objects and their movements in sensor data is of crucial importance in providing safe navigation through both indoor and outdoor environments for the visually impaired. In our setting we use depth-sensor data obtained from a simulator and use inductive logic programming (ILP), a subfield of machine learning that deals with learning concept descriptions, to learn how to detect borders, find the border that is nearest to some point of interest, and border correspondence through time. We demonstrate how ILP can be used to tackle this problem in an incremental manner by using previously learned predicates to construct more complex ones. The learned concept descriptions show high (> 90%) accuracy and their natural language interpretation closely matches an intuitive understanding of their meaning.