Intuitive Human-machine Interface Research Articles

An automated approach for hemocytometer cell counting based on image-processing method

Evaluating cell viability plays an important role in routine cell culture, toxicology, and drug development. Trypan blue stain is one of the simplest assays that are used to determine the number of viable cells in a cell suspension. The stained cell suspension can then be counted under a microscope using a hemocytometer. Hemocytometers have a fixed volume, making it possible to calculate the concentration of cells in the sample and measure viability. Most of the related studies are conducted using manual counting methods. However, manual counting is time-consuming and laborious. While various automated cell counting devices have been proposed, they tend to be too expensive for widespread deployment. Although low-cost automated cell counting methods presented in the literatures provide high accuracy and cost-effective alternatives, only cells in the field of view of microscope with unknown volume are counted. This will make the concentration inestimable. Accordingly, the present study proposes a low-cost but accurate method for hemocytometer cell counting using an image-processing approach. The accuracy of the proposed method is demonstrated by comparing the cell counting results obtained for human cervical carcinoma (HeLa) cells with the manual counts. It is shown that the proposed approach has a mean absolute percentage error of less than 6.26 % for samples containing a mixture of live and dead cells. Moreover, the proposed system is equipped with a simple and intuitive human machine interface such that the counting task can be performed by individuals with no prior experience in the image-processing field.

Optimum Real Time Estimation and Interfacing of Braking Points for Racing Car

To provide additional on-track support for racing drivers, a novel real-time braking point estimation system was developed and tested to improve overall lap time performance. The Formula-Student competition was chosen as testing environment because of direct access to a real car and, hence, to all the data needed for this study, which can be fully disclosed at the same time. To estimate the optimum braking point, a simulation of the track section ahead is performed using a multidimensional approach to model not only longitudinal and lateral acceleration, but also combined acceleration scenarios. A lap identification system as well as live track analysis tools were developed in order to obtain track data that cannot be measured directly. To evaluate the overall grip conditions, a tire model, using both a wheel load and a two-track model, is implemented, which includes a dynamic calibration feature designed to adapt the system to the changing conditions of the circuit. It can be seen that the simulation provides a detailed profile of the maximum permitted velocities for every point in front of the real vehicle, and that this can be used to create a reliable prediction of the braking points along the track. Furthermore, a concept based on physiological fundamentals was developed and investigated in terms of human reaction times in order to meet the requirements of an intuitive human-machine interface. Our results demonstrate that it can be successfully used to automatically compensate for driver delays, improve the trust in the system and ensure safe operation. We conclude that our new system can reduce lap times, depending on the length and number of curves, by a significant amount of time (tens of a second).

Design and development of a ball-plate balancing system with a smart phone human-machine interface

Technology is so ahead that sophisticated processes could be accessed and controlled by a smart phone through which human-machine interaction is made easy and taken to the next higher level. A classic example of a highly unstable non-linear mechatronic process is a ball-plate balancing system. This paper presents the mathematical model and design of a typical two axes ball-plate balancing system, where the roll and pitch of the plate is controlled by the actuation of two servo motors. An image processing algorithm acts on the video feed obtained by a vision system, to read the position of the ball on the plate in real time. A proportional-integral-derivative controller is implemented to position the ball on a desired location on the plate. A mobile application forming an intuitive human-machine interface is developed to interact, monitor and control the operation of the ball-plate balancing system. The developed system has an error of 1.29 % in positioning the ball with a settling time of about 26.4 seconds.

Design of a Charge-Sustaining Energy Management System for a Free-Floating Electric Shared Bicycle

This article proposes an energy management system (EMS) for shared electric bicycles. The objective is to guarantee electric assistance to the cyclist while avoiding discharging the battery. The basic working principle exploits the cycling efficiency gaps. The proposed multilayered EMS is specifically tailored to a free-floating bike-sharing setting. The innermost layer manages the assistance and energy harvesting with the objective of yielding an intuitive human–machine interface. The middle level modulates the level of assistance so to track a desired average battery power. This is an adaptive model-based controller designed on a control-oriented model of the cyclist and bicycle energy dynamics. A cyclist profiling mechanism enables the model adaptation. The outermost loop guarantees the long-term robustness by tracking a desired battery state-of-charge profile. Extensive simulations and experimental tests validate this approach in terms of usability and charge sustenance, proving that the cyclist profiling is of paramount importance.

Long exposure convolutional memory network for accurate estimation of finger kinematics from surface electromyographic signals

Objective. Estimation of finger kinematics is an important function of an intuitive human–machine interface, such as gesture recognition. Here, we propose a novel deep learning method, named long exposure convolutional memory network (LE-ConvMN), and use it to proportionally estimate finger joint angles through surface electromyographic (sEMG) signals. Approach. We use a convolution structure to replace the neuron structure of traditional long short-term memory (LSTM) networks, and use the long exposure data structure which retains the spatial and temporal information of the electrodes as input. The Ninapro database, which contains continuous finger gestures and corresponding sEMG signals was used to verify the efficiency of the proposed deep learning method. The proposed method was compared with LSTM and Sparse Pseudo-input Gaussian Process (SPGP) on this database to predict the ten main joint angles on the hand based on sEMG. The correlation coefficient (CC) was evaluated using the three methods on eight healthy subjects, and all the methods adopted the root mean square (RMS) features. Main results. The experimental results showed that the average CC, root mean square error, normalized root mean square error of the proposed LE-ConvMN method (0.82 ± 0.03,11.54 ± 1.89,0.12 ± 0.013) was significantly higher than SPGP (0.65 ± 0.05, p < 0.001; 15.51 ± 2.82, p < 0.001; 0.16 ± 0.01, p < 0.001) and LSTM (0.64 ± 0.06, p < 0.001; 14.77 ± 3.21, p < 0.001; 0.15 ± 0.02, p = < 0.001). Furthermore, the proposed real-time-estimation method has a computation cost of only approximately 82 ms to output one state of ten joints (average value of 10 tests on TitanV GPU). Significance. The proposed LE-ConvMN method could efficiently estimate the continuous movement of fingers with sEMG, and its performance is significantly superior to two established deep learning methods.

Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion.

Seamless integration of lower-limb assistive devices with the human body requires an intuitive human-machine interface, which would benefit from predicting the intent of individuals in advance of the upcoming motion. Ultrasound imaging was recently introduced as an intuitive sensing interface. The objective of the present study was to investigate the predictability of joint kinematics using ultrasound features of the rectus femoris muscle during a non-weight-bearing knee extension/flexion. Motion prediction accuracy was evaluated in 67ms increments, up to 600ms in time. Statistical analysis was used to evaluate the feasibility of motion prediction, and the linear mixed-effects model was used to determine a prediction time window where the joint angle prediction error is barely perceivable by the sample population, hence clinically reliable. Surprisingly, statistical tests revealed that the prediction accuracy of the joint angle was more sensitive to temporal shifts than the accuracy of the joint angular velocity prediction. Overall, predictability of the upcoming joint kinematics using ultrasound features of skeletal muscle was confirmed, and a time window for a statistically and clinically reliable prediction was found between 133 and 142ms. A reliable prediction of user intent may provide the time needed for processing, control planning, and actuation of the assistive devices at critical points during ambulation, contributing to the intuitive behavior of lower-limb assistive devices.

An online calibration tool for soft sensors: development and experimental tests in a semi-industrial boiler plant

Soft sensors with real time prediction capabilities appear as a profitable solution for hard-to-measure variables whenever hard sensors are difficult to apply or subjected to high operational costs. Nonetheless, the use of soft sensors within industrial applications is still not widespread because of the systematic accuracy issues that can be introduced with process plant deviations from nominal operation states. Soft sensor models need to be constantly updated to avoid degradation of their prediction potential. This study presents an innovative view on a well-known artificial neural network (ANN) calibration method by developing a generic online calibration tool that can be used in independent data-driven soft sensors based on ANN multi-layer perceptron (MLP) models. The maintenance framework has been fully tested in a semi-industrial boiler plant to predict real time pollutant emission levels, presenting recalibration time responses up to 1 min, overall r2 performance above 80% and an intuitive human–machine-interface.

A simple ERP method for quantitative analysis of cognitive workload in myoelectric prosthesis control and human-machine interaction.

Common goals in the development of human-machine interface (HMI) technology are to reduce cognitive workload and increase function. However, objective and quantitative outcome measures assessing cognitive workload have not been standardized for HMI research. The present study examines the efficacy of a simple event-related potential (ERP) measure of cortical effort during myoelectric control of a virtual limb for use as an outcome tool. Participants trained and tested on two methods of control, direct control (DC) and pattern recognition control (PRC), while electroencephalographic (EEG) activity was recorded. Eighteen healthy participants with intact limbs were tested using DC and PRC under three conditions: passive viewing, easy, and hard. Novel auditory probes were presented at random intervals during testing, and significant task-difficulty effects were observed in the P200, P300, and a late positive potential (LPP), supporting the efficacy of ERPs as a cognitive workload measure in HMI tasks. LPP amplitude distinguished DC from PRC in the hard condition with higher amplitude in PRC, consistent with lower cognitive workload in PRC relative to DC for complex movements. Participants completed trials faster in the easy condition using DC relative to PRC, but completed trials more slowly using DC relative to PRC in the hard condition. The results provide promising support for ERPs as an outcome measure for cognitive workload in HMI research such as prosthetics, exoskeletons, and other assistive devices, and can be used to evaluate and guide new technologies for more intuitive HMI control.

Design and Implementation of 8051 Single-Chip Microcontroller for Stationary 1.0 kW PEM Fuel Cell System

Proton exchange membrane fuel cells (PEMFCs) have attracted significant interest as a potential green energy source. However, if the performance of such systems is to be enhanced, appropriate control strategies must be applied. Accordingly, the present study proposes a sophisticated control system for a 1.0 kW PEMFC system comprising a fuel cell stack, an auxiliary power supply, a DC-DC buck converter, and a DC-AC inverter. The control system is implemented using an 8051 single-chip microcontroller and is designed to optimize the system performance and safety in both the startup phase and the long-term operation phase. The major features of the proposed control system are described and the circuit diagrams required for its implementation introduced. In addition, the touch-sensitive, intuitive human-machine interface is introduced and typical screens are presented. Finally, the electrical characteristics of the PEMFC system are briefly examined. Overall, the results confirm that the single-chip microcontroller presented in this study has significant potential for commercialization in the near future.

HUMAN AUDIO/VESTIBULAR SYSTEM: DATA INPUT CHANNELS FOR ROBOTIC FORCE AND MOMENT SENSOR MEASUREMENTS

The design goal was development of an intuitive human machine interface for force and moment data from space robotic operations. This paper defines overall requirements and goals. It describes experimental approaches used to evaluate our ‘nature’ inspired solution. The final portion of the paper discusses the design and prototyping of the segment of the problem which has lead to our first product. One of nature’s ways of presenting multiple degree of freedom (dof), vector data is through our audio and vestibular systems. This directional capability is being applied as a human machine interface (HMI) for robotic force sensing. Human audio direction ability is accurate except for sounds generated above and behind our heads. This inaccuracy has lead us to the development of the vestibulator. The vestibulator is a wireless device which applies low levels of current, to the human subject mastoid bones through surface mounted electrodes. These induce perceptions of tilt. The polarity of the signals provide directional stimulus.

2P2-F10 Motion Control of Three-Wheeled Omnidirectional Intelligent Cane Robot

A three-wheeled omnidirectional cane robot is designed for aiding the elderly walking. Possible walking modes are analyzed and a corresponding hybrid model is constructed to describe walking behavior. A concept called intentional direction is presented to denote the moving intention of a human. Based on experiments and some assumptions, dynamic model of intentional direction is obtained as well as its online estimation method. A new admittance control scheme is presented based on intentional direction, which provides natural and intuitive human machine interface. Experiment results show the effectiveness of the design.

An Adaptive Shared Control System for an Intelligent Mobility Aid for the Elderly

The control system for a personal aid for mobility and health monitoring (PAMM) for the elderly is presented. PAMM is intended to assist the elderly living independently or in senior assisted living facilities. It provides physical support and guidance, as well as monitoring basic vital signs for users that may have both limited physical and cognitive capabilities. This paper presents the design of a bi-level control system for PAMM. The first level is an admittance-based mobility controller that provides a natural and intuitive human machine interface. The second level is an adaptive shared controller that allocates control between the user and the computer based on metrics of the user's performance. Field trials at an eldercare facility show the effectiveness of the design.