Real-time Performance Assessment Research Articles

Examining the Integration of a GPS-based Staff Performance Monitoring and Assessment System within Academic Settings

Research evidence of manipulations of appraisal systems of various institutions in Nigeria abound, and little is known about the application of a Geographical positioning system (GPS) enabled system in this context. To cover this gap, this study investigates the integration of a real-time staff performance monitoring and assessment system with GPS technology, within academic settings. With the increasing emphasis on quality education and efficient resource utilization, academic institutions are exploring innovative approaches to monitor staff performance and student outcomes. The integration of real-time performance systems offers a promising avenue to achieve these goals by providing timely feedback, fostering accountability, and facilitating data-driven decision-making processes. This study outlines the key components of such a system, including its technological infrastructure, data collection methods, and performance evaluation metrics. Additionally, it explores the potential benefits and challenges associated with its adoption, considering factors such as staff engagement, privacy concerns, and institutional culture. Through a comprehensive examination of existing literature and case studies, this research aims to provide valuable insights and recommendations for academic institutions seeking to leverage real-time staff performance monitoring for institutional enhancement and educational excellence. While this study highlights the significance of stakeholder collaboration and continuous improvement mechanisms in ensuring the successful integration and sustained effectiveness of the monitoring system, it introduces a nuanced novel GPS technology and highlights some design implications which will guide future human-computer interaction researchers.

O160: Novel Monitoring of General Surgeons' Intraoperative Cognitive Load following Surgical Sabermetrics Principles

Abstract Introduction Surgeons strive to continually enhance performance. Currently, assessment relies on subjective, retrospective methods-hindering real-time improvement and error prevention. Learning from sports, surgeons can utilise surgical sabermetrics for data-driven analysis. Cognitive load(CogL) impacts performance, with overload impairing skills and judgement. Sabermetrics can be leveraged to measure CogL objectively(e.g.Electrodermal activity(EDA)) and subjectively. This study aimed to combine these measures and explore their relationship with surgeon factors and patient outcomes. Methods Fourteen surgeons conducting 58 benign surgery cases participated. Surgeons’ intraoperative EDA was captured using a digital sensor. Subjective data collected included (i)Surgery Task-Load-Index(SURG-TLX)), (ii)mood, (iii)sleep-disturbance and (iv)risk-aversion. EDA underwent decomposition into (a)tonic (background autonomic arousal) and (b) phasic(response to events) components. Patient outcomes included (i)30-day morbidity and (ii)case difficulty. Results Overall, mean phasic-EDA was 0.038μS and mean tonic-EDA was 0.483μS(ranges 0-1:1 indicating highest CogL). Mean SURG-TLX was 47.128(/120, where 120 is the highest workload). Negative mood related to changes in tonic-EDA(p<0.05,F=11.84). EDA climbed throughout each case (Tonic:p<0.05,F=2.035;Phasic:p<0.05,F=1.626). SURG-TLX were weakly correlated with sleep(p<0.05,ϱ=-0.273) and moderately correlated with case duration(p<0.05, ϱ=0.598), but not EDA(p>0.50). Discussion This study is the first in the UK to report combined CogL measures gathered during live surgery. Awareness of factors that affect CogL crucially allows for the management of factors to prevent overload and to inform targeted interventions for performance improvement. Using digital sensors to measure CogL provides opportunity for automated, real-time performance assessment, advanced feedback, and behavioural modifications.

A case study in eastern Indonesia for the development of integrated real-time performance assessment on science practicum in universities

The purpose of this study was to collect preliminary data for the design of real-time and integrated performance assessments in science practicum at two public universities and two private universities in Eastern Indonesia. The case study design and quantitative and qualitative approaches were adopted in this study. Cluster random sampling technique was used to select four universities, 376 participants respectively from university students and lecturers, four vice deans, four heads of study programs, four heads of laboratories, and four teaching assistants. Data were gathered through questionnaires, in-depth interviews, and observations. The chi-square test was used to analyze the data descriptively. Findings indicated that a performance assessment was not carried out since standard measurement instruments that provided real-time and integrated feedback had not been developed. There was no significant difference (p > 0.05) between public and private universities on the science practicum assessment, implying that both public and private universities still used paper-based practicum assessments. There was also no significant difference between the responses of university students and lecturers on practicum performance assessments (p > 0.05). University students’ experience in assessing science practicums was similar to those of lecturers’, which suggests that practicum of performance assessments was not real-time and integrated with feedback. This study recommends developing a real-time and integrated performance assessment with automated feedback on the science practicum, in order to provide continuous improvement of students and monitoring their learning progress.

Discrete-time multi-model preview control: Application to a real semi-active automotive suspension system

Road roughness is one of reasons of vehicle vibration. Passengers feel uncomfortable or goods are damaged with the serious vibration caused by the irregular road surface. This paper focus on the road adaptation for vehicle semi-active suspension system using preview robust control approach. This approach aims to improve the performance of control systems by using future measurements or predictions of road disturbances. The objectives are to minimize a quadratic criterion on the system output and at the same time to achieve a good magnitude of the control signal. The proposed solution combines a robust feedback controller together with a preview feedforward filter. This new method is developed to synthesize the preview feedforward filter in order to limit the control force demand of an electro-rheological (ER) damper in an automotive suspension system, and is implemented on the INOVE testbench from GIPSA-lab (1/5-scaled real vehicle) for real-time performance assessment. Both simulation and experimental results demonstrate the effectiveness of the proposed controller to determinate the damper force in real-time face to measurement noises and road disturbances.

3D Autonomous Surgeon’s Hand Movement Assessment Using a Cascaded Fuzzy Supervisor in Multi-Thread Video Processing

The purpose of the Fundamentals of Laparoscopic Surgery (FLS) training is to develop laparoscopic surgery skills by using simulation experiences. Several advanced training methods based on simulation have been created to enable training in a non-patient environment. Laparoscopic box trainers-cheap, portable devices-have been deployed for a while to offer training opportunities, competence evaluations, and performance reviews. However, the trainees must be under the supervision of medical experts who can evaluate their abilities, which is an expensive and time-consuming operation. Thus, a high level of surgical skill, determined by assessment, is necessary to prevent any intraoperative issues and malfunctions during a real laparoscopic procedure and during human intervention. To guarantee that the use of laparoscopic surgical training methods results in surgical skill improvement, it is necessary to measure and assess surgeons' skills during tests. We used our intelligent box-trainer system (IBTS) as a platform for skill training. The main aim of this study was to monitor the surgeon's hands' movement within a predefined field of interest. To evaluate the surgeons' hands' movement in 3D space, an autonomous evaluation system using two cameras and multi-thread video processing is proposed. This method works by detecting laparoscopic instruments and using a cascaded fuzzy logic assessment system. It is composed of two fuzzy logic systems executing in parallel. The first level assesses the left and right-hand movements simultaneously. Its outputs are cascaded by the final fuzzy logic assessment at the second level. This algorithm is completely autonomous and removes the need for any human monitoring or intervention. The experimental work included nine physicians (surgeons and residents) from the surgery and obstetrics/gynecology (OB/GYN) residency programs at WMU Homer Stryker MD School of Medicine (WMed) with different levels of laparoscopic skills and experience. They were recruited to participate in the peg-transfer task. The participants' performances were assessed, and the videos were recorded throughout the exercises. The results were delivered autonomously about 10 s after the experiments were concluded. In the future, we plan to increase the computing power of the IBTS to achieve real-time performance assessment.

AI-Based Human Resource Management Tools and Techniques; A Systematic Literature Review

In the context of rapid advancements in artificial intelligence (AI), its pervasive impact across industries extends to Human Resource Management (HRM), redefining workforce management paradigms. This systematic literature review addresses the intersection of AI and HRM, elucidating AI-driven tools and techniques that optimize recruitment, performance management, and employee engagement. Rooted in the recognition that conventional HRM approaches can be time-intensive and biased, AI's integration promises to enhance decision-making efficiency and accuracy. This review, guided by research questions, explores prevalent AI applications, evaluates statistical evidence of their impact, and engenders discussions on implications. Through methodological rigor, this review critically synthesizes diverse scholarly perspectives. It provides insights into AI's multifaceted role, en-compassing candidate screening, personalized training, real-time performance assessment, and engagement enhancement. Next, it furnishes statistically derived trends from selected studies, revealing adoption rates, prevalent techniques, and sector-specific implementations.

Multifrequency Microwave Radiometry for Characterizing the Internal Temperature of Biological Tissues

The analysis of near-field radiometry is described for characterizing the internal temperature of biological tissues, for which a system based on multifrequency pseudo-correlation-type radiometers is proposed. The approach consists of a new topology with multiple output devices that enables real-time calibration and performance assessment, recalibrating the receiver through simultaneous measurable outputs. Experimental characterization of the prototypes includes a well-defined calibration procedure, which is described and demonstrated, as well as DC conversion from the microwave input power. Regarding performance, high sensitivity is provided in all the bands with noise temperatures around 100 K, reducing the impact of the receiver on the measurements and improving its sensitivity. Calibrated temperature retrievals exhibit outstanding results for several noise sources, for which temperature deviations are lower than 0.1% with regard to the expected temperature. Furthermore, a temperature recovery test for biological tissues, such as a human forearm, provides temperature values on the order of 310 K. In summary, the radiometers design, calibration method and temperature retrieval demonstrated significant results in all bands, validating their use for biomedical applications.

Toward Asphalt Pavement Health Monitoring With Built-In Sensors: A Novel Application to Real-Time Modulus Evaluation

Modulus is a critical parameter for evaluating the bearing capacity and remaining life of pavements. The prevalent modulus back-calculation method by using the pavement surface deflections captured from falling weight deflectometer (FWD) could characterize the overall bearing capacity of pavements. However, a non-uniqueness issue may occur when identifying the modulus of each layer from the perspective of mathematics. Alternatively, this paper presents a novel application to real-time modulus evaluation based on asphalt pavement health monitoring with built-in sensors. First, the sensor layout is optimized on the basis of the theoretical relationship between modulus and mechanical response inside the pavement. The proposed sensor layout, including input (random loads), output (mechanical responses), and environmental sensing modules, is illustrated in detail. Then, the procedure for real-time modulus evaluation is proposed, and the convergence and uniqueness of modulus evaluation results are investigated. Lastly, a case study of the realistic asphalt pavement health-monitoring system is conducted to demonstrate the effectiveness of the application to real-time modulus evaluation. The results indicate that the proposed modulus evaluation method has a potential to identify the modulus of each pavement structural layer in real time with a convergent and unique solution under a realistic traffic load. The evaluation process has the advantage of not interfering with traffic compared with the prevalent FWD-based modulus evaluation method. The pavement health monitoring with built-in sensors is recommended for newly built roads to facilitate long-term real-time performance assessment and maintenance decision making.

A review of research on design theory and engineering practice of high-speed railway turnout

Abstract This paper systematically reviews the research progress, problems, specific countermeasures, and development trends of dynamic design theory as applied to high-speed railway turnouts. This includes wheel–rail contact solving, high-speed vehicle–turnout dynamic interaction simulation, analysis of long-term turnout performance deterioration, safety assessment of trains passing through the turnout, and the maintenance and management of turnout serviceability. High-speed turnouts still face severe technical challenges with regard to their acclimation to the future development of rail transit technology. Some of these challenges include the suitability of next-generation higher-speed turnouts to a complex environment, lifecycle design, optimization of wheel–rail matching and vehicle–turnout dynamic performance, real-time status capture and performance assessment, health management and damage prediction. It is now necessary to deepen the basic theoretical study of high-speed railway turnouts, and integrate cutting-edge techniques, such as advanced materials and manufacturing, intelligence and automation, big data and cloud computing, in an effort to enhance China’s capabilities for original innovation in high-speed railway turnout technology. By analysing the present situation in a problem-orientated manner, this paper aims to provide a new perspective, as well as some basic data for academic research into technological innovations for railway engineering.

Real-time performance assessment of open-top downdraft biomass gasifier system

Biomass gasification is a promising technology for electricity generation, thermal application, and synthesis of chemicals. Present investigation was carried out to obtain temperature profile, evolutionary characteristics of producer gas composition, calorific value at the different flow rate, and morphological characteristics of eucalypts and their residue. Result show that producer gas so obtained has a reasonably high calorific value of 3.709 MJ/Nm3, with carbon monoxide (13.3%) and hydrogen (10.8%). The lowest CH4 and CO2 values are 0.9 and 9.2% at the moisture content of 8.5% and equivalence ratio of 0.309 (optimal equivalence ratio for the best performance of the downdraft gasifier). Morphological analysis showed the transformation of all volatiles give way to pores and tube-like structures, seen in the eucalyptus residue indicating enhanced porosity. The absence of an aliphatic C–H peak (3000–2500 cm-1) in the residue indicates that the aliphatic structure was disturbed during the thermochemical conversion. The crystallinity of the residue decreases as it degrades throughout the gasification process. The TG-DTG analysis gives information of thermochemical properties of biomasses before gasification.

Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device.

The side effects and complications of traditional treatments for treating pathological tremor have led to a growing research interest in wearable tremor suppression devices (WTSDs) as an alternative approach. Similar to how the human brain coordinates the function of the human system, a tremor estimator determines how a WTSD functions. Although many tremor estimation algorithms have been developed and validated, whether they can be implemented on a cost-effective embedded system has not been studied; furthermore, their effectiveness on tremor signals with multiple harmonics has not been investigated. Therefore, in this study, four tremor estimators were implemented, evaluated, and compared: Weighted-frequency Fourier Linear Combiner (WFLC), WFLC-based Kalman Filter (WFLC-KF), Band-limited Multiple FLC, and enhanced High-order WFLC-KF (eHWFLC-KF). This study aimed to evaluate the performance of each algorithm on a bench-top tremor suppression system with 18 recorded tremor motion datasets; and compare the performance of each estimator. The experimental evaluation showed that the eHWFLC-KF-based WTSD achieved the best performance when suppressing tremor with an average of 89.3% reduction in tremor power, and an average error when tracking voluntary motion of 6.6°/s. Statistical analysis indicated that the eHWFLC-KF-based WTSD is able to reduce the power of tremor better than the WFLC and WFLC-KF, and the BMFLC-based WTSD is better than the WFLC. The performance when tracking voluntary motion is similar among all systems. This study has proven the feasibility of implementing various tremor estimators in a cost-effective embedded system, and provided a real-time performance assessment of four tremor estimators.

Application of Hybrid Approach in Banking System: An Undesirable Operational Performance Modelling

Non-performing loans (NPLs) is a critical constituent that impacts the operational performance of banks. Rising level of risk leads to poor operational performance, especially when it is beyond the bank’s capabilities to control the increasing bad assets. This calls for real-time performance assessment coupled with futuristic decision making to support banking managers. This observation motivates the authors of this article to develop a two-stage performance prediction assessment model. Accordingly, a hybrid approach combining data envelopment analysis (DEA) and artificial neural network (ANN) is developed to measure and predict the operational efficiency scores of banks. DEA effectively explores the operational performance as well as improvable areas of inefficient banks. The training of ANN model is dependent on estimated operational DEA efficiency scores with the objective to estimate the efficiency scores. Domain for the validation of this study includes dataset derived from Indian banks. The validation result shows that trained ANN model has the prediction capacity with minimum error and maximum accuracy. Finally, the outcome of this study is significantly directed towards business managers who can rely on predictions based on empirical findings of this proposed hybrid modelling.

Real-Time Experimental Performance Assessment of a Photovoltaic Thermal System Cascaded With Flat Plate and Heat Pipe Evacuated Tube Collector

Abstract In response to the global quest for a sustainable and environmentally friendly source of energy, most scientists’ discretion is solar energy, especially solar thermal. However, successful deployment of solar thermal technologies such as solar-assisted process heating (SAPH) systems in medium- to large-scale industries is still in quandary due to their inefficacy in raising ample temperatures. Cascaded SAPH system, which is essentially a series combination of two same or different types of thermal collectors, may provide a worthwhile solution to this problem. In this article, performance assessment and comparison of two cascaded SAPH systems have been presented: photovoltaic thermal (PVT) cascaded with flat-plate collector (PVT-FPC) and PVT coupled with heat pipe evacuated tube collector (PVT-HPETC). Simulation models have been presented for individual FPC, HPETC, and PVT as well as PVT cascaded with FPC and HPETC systems in TRNSYS and validated through outdoor experimentation. Both the first and the second laws of thermodynamics have been employed to reveal veritable performance of the systems. Results show that PVT-HPETC delivers better performance with 1625 W thermal energy, 81.59% energy efficiency, and 13.22% exergy efficiency. It cuts 1.37 kg of CO2 on an hourly basis. Cascaded systems can be effective in sustaining industrial process heat requirements.

Computerized Clinical Training Simulations with Virtual Clients Abusing Alcohol: Initial Feasibility, Acceptability, and Effectiveness.

Although masters-level social work students typically build clinical skills via role-playing with their peers or instructors, several innovative training simulations are emerging in the literature that may enhance existing skill-building methodologies. We evaluated the initial feasibility, acceptability, usability, and effectiveness of three computerized simulations (two cognitive behavioral therapy, one motivational interviewing) during an interpersonal practice course among 22 students in a Master of Social Work program accredited by the Council on Social Work Education. Trainees repetitively practiced their clinical skills with virtual clients while receiving feedback via real-time nonverbal cues, transcript review, and performance assessment across pre-specified theoretical learning objectives. Across the three simulations, at least 86.4% of students completed the required protocol and completed M = 468.95 (SD = 178.27) minutes of simulated sessions. Students improved their scores (range 0 to 100) across all the simulations from M = 63.41 (SD = 11.13) to M = 93.64 (SD = 3.24). Students found the simulations to be acceptable with strong usability. Paired sample t-tests revealed students reported greater self-efficacy in general clinical skills, exploration skills, insight skills, and action skills between pre-test and post-test after completing the simulations (all p < 0.001). Students reported that the clinical skills learned from the simulations translated into successful interactions with real-world clients during their field placements. We discuss the results of this initial feasibility study within the context of simulation-based learning and the potential for broader implementation within MSW programs.

Condition-Based Economic Assessment of Airplane Engines

A model is proposed for economic assessment of projected airplane engines, in terms of life-cycle costs. Adoption of a new strategy is recommended: management of engine life on the basis of the current state of the system (on-condition maintenance) and increase in economic efficiency at the operational stage. A conceptual approach to extending the life of promising aircraft engines is outlined, on the basis of a diagnostic system (real-time performance assessment). That permits decisions regarding the engine’s potential for continued operation.

Методика оценки характеристик услуги высокой точности ГНСС

The paper considers global tendencies of the GNSS high-accuracy service evolution. Key performance characteristics defined and the method of their estimation is proposed based on orbit and clock data only. The description of the pseudo-range and pseudo-phase measurement model used in the formation of the reference ephemeris-time information on the global network of stations is presented. The results of the high-accuracy a posteriori orbit and clock data comparison are presented for several analysis centers. In addition, the results of the real-time performance assessment based on the proposed method are also presented. Comparison with the consumer level processing data from the global stations network shows that all the estimates obtained through the proposed methodology are apparently positive. Despite this, the proposed technique can be used to determine the signal-in-space ranging error for one or several GNSS without real user measurements.

Real-Time Performance Evaluation of the Combustion Process of Coke Oven

Real-time performance assessment is one of the main methods to guarantee the steady operation of production during the combustion process of a coke oven. In this study, a real-time assessment method is proposed for this combustion process based on the analytic hierarchy process (AHP) and intuitionistic multiplicative preference relation. Relevant scholars, senior engineers, and elite workers participated in this project to build the AHP model with three aspects (i.e. safety, stability, and economic benefit) and perform pairwise comparisons of criteria and sub-criteria through group decisions. To support real-time, the pairwise comparisons of alternatives were realized by an automated method using measurement values. This comprehensive assessment method demonstrates ability to provide real-time performance evaluation for the combustion process. An experiment was conducted to evaluate the effectiveness and viability of the proposed method.

Unified $\mathcal{H}_{\infty}$ Observer for a Class of Nonlinear Lipschitz Systems: Application to a Real ER Automotive Suspension

This letter presents an extension of the synthesis of a unified $\mathcal {H_{\infty }}$ observer for a specific class of nonlinear systems. The objectives are to decouple the effects of bounded unknown input disturbances and to minimize the effects of measurement noises on the estimation errors of the state variables by using $\mathcal {H_{\infty }}$ criterion, while the nonlinearity is bounded through a Lipschitz condition. This new method is developed to estimate the damping force of an electro-rheological (ER) damper in an automotive suspension system, and is implemented on the INOVE testbench from GIPSA-Lab (1/5-scaled real vehicle) for real-time performance assessment. Both simulation and experimental results demonstrate the effectiveness of the proposed observer to estimate the damper force in realtime, face to measurement noises and road disturbances.

Experimental and model based performance investigation of a solid desiccant wheel dehumidifier in subtropical climate

This paper presents real time performance analysis of a silica gel based desiccant wheel dehumidifier. Thermodynamic model is also validated through comparison under a wide range of operating conditions for subtropical climate conditions. Initially, a mathematical model is developed by using various set of equations for desiccant wheel dehumidifier in engineering equation solver. After-wards, a parametric analysis of the system is performed including various design and climate parameters such as: inlet air humidity ratio, inlet air temperature, regeneration inlet humidity ratio, regeneration temperature, and rotation speed of the wheel. Then, an experimental set-up of the system is established in Taxila Pakistan for the real-time performance assessment. The results revealed that the optimal rotation speed of desiccant wheel ranged from 15-17 rph. The maximum model based and experimental effectiveness is 0.45 and 0.43, respectively at re-generation temperature of 80°C. The maximum and minimum root mean square error values for effectiveness are 3.2% and 2.1%, respectively. Thus, the comparison between experimental and model results showed a good agreement.

Development of an EMG-based exergaming system for isometric muscle training and its effectiveness to enhance motivation, performance and muscle strength

Abstract Isometric exercises can be used for general strength conditioning or rehabilitation. To maximize their benefits without excess muscle fatigue, such exercises must be performed under medical prescription, which also needs to specify their intensity and duration. However, at home or at a clinic they are rarely real-time monitored or evaluated, which may delay or prevent goal achievement, with negative influence on participants’ motivation. Therefore, in this paper, it is introduced the development and clinical evaluation of an exergaming platform based on ElectroMyoGraphy (EMG) for the real-time monitoring and performance assessment of personalized isometric exercises. The system enables medical personnel to create personalized exergame sessions that oversee in real time the behavior of the main character in a game, based on the muscle force of the user, estimated from an EMG signal. In addition, it evaluates exercise performance, in terms of muscle contraction duration and relative effort during work and rest intervals. A data storage capability enables users to upload results in a cloud database, which can be accessed remotely by healthcare providers through an application. The system operates firstly by obtaining an EMG-force model for isometric contractions of the flexor digitorum superficialis muscle, which was then integrated in the exergaming platform. Then, a 4-week randomized controlled clinical trial was conducted with tailored isometric handgrip exercises using the proposed system versus conventional handgrip exercises to assess motivation and grip strength in patients with distal radius fracture. Exercise performance and subjective workload ratings were additionally evaluated. Results notably show that handgrip exercises with the proposed system yield an improvement in grip strength of 40.2% over conventional handgrip exercises, while experiencing higher motivation levels, contrasting with lower frustration and mental demand levels. However, continued research is ongoing to generalize the findings of the clinical trial to a larger patient population.