Sensor-based Approach Research Articles

Holistic Sensor-Based Approach for Assessing Community Mobility and Participation of Manual Wheelchair Users in the Real World

Holistic Sensor-Based Approach for Assessing Community Mobility and Participation of Manual Wheelchair Users in the Real World

Flexible Sensor-based Whole-body Biomechanics of Exoskeleton-assisted Patient Handling

The issue of work-related musculoskeletal disorders (WMSDs) stemming from manual handling tasks remains a significant economic and societal burden, with healthcare workers being one of the most persistently affected groups. Exoskeletons have seen growing implementation for aiding manual handling tasks in industrial sectors. Traditional measurements and analyses of joint kinematics and dynamics commonly utilize optical motion capture systems and force plates. Nevertheless, the limitations of optical motion capture systems include their dependence on marker visibility and the necessity for a controlled lab environment. An innovative wireless technology featuring miniaturized, skin-mounted sensors, BioStamp nPoint (MC10, Inc., MA, USA) exhibits significant potential in addressing the challenges mentioned above. Therefore, the objective of this study was to explore a flexible sensor-based approach to assessing the effects of a back exoskeleton on joint kinematics during patient handling.

Sensor-based Approach for Objective Balance Skill Assessment: A Review

Sensor-based Approach for Objective Balance Skill Assessment: A Review

Real-time monitoring of disc cutter wear in tunnel boring machines: A sound and vibration sensor-based approach with machine learning technique

Large portions of the tunnel boring machine (TBM) construction cost are attributed to disc cutter consumption, and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter. Therefore, the need to monitor disc cutter wear in real-time has emerged as a technical challenge for TBMs. In this study, real-time disc cutter wear monitoring is developed based on sound and vibration sensors. For this purpose, the microphone and accelerometer were used to record the sound and vibration signals of cutting three different types of rocks with varying abrasions on a laboratory scale. The relationship between disc cutter wear and the sound and vibration signal was determined by comparing the measurements of disc cutter wear with the signal plots for each sample. The features extracted from the signals showed that the sound and vibration signals are impacted by the progression of disc wear during the rock-cutting process. The signal features obtained from the rock-cutting operation were utilized to verify the machine learning techniques. The results showed that the multilayer perceptron (MLP), random subspace-based decision tree (RS-DT), DT, and random forest (RF) methods could predict the wear level of the disc cutter with an accuracy of 0.89, 0.951, 0.951, and 0.927, respectively. Based on the accuracy of the models and the confusion matrix, it was found that the RS-DT model has the best estimate for predicting the level of disc wear. This research has developed a method that can potentially determine when to replace a tool and assess disc wear in real-time.

On the Generalization Capability of a Data-Driven Turbulence Model by Field Inversion and Machine Learning

This paper discusses the generalizability of a data-augmented turbulence model with a focus on the field inversion and machine learning approach. It is highlighted that the augmented model based on two-dimensional (2D) separated airfoil flows gives poor predictive capability for a different class of separated flows (NASA wall-mounted hump) compared to the baseline model due to extrapolation. We demonstrate a sensor-based approach to localize the data-driven model correction to tackle this generalizability issue. Furthermore, the applicability of the augmented model to a more complex aeronautical three-dimensional case, the NASA Common Research Model configuration, is studied. Observations on the pressure coefficient predictions and the model correction field suggest that the present 2D-based augmentation is to some extent applicable to a three-dimensional aircraft flow.

A Novel Upper-Extremity Sensor-Based Approach to Predict COPD Adverse Outcomes in an Acute Setting

A Novel Upper-Extremity Sensor-Based Approach to Predict COPD Adverse Outcomes in an Acute Setting

Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques.

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient-doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes.

A Sensor-Based Simulation Method for Spatiotemporal Event Detection

Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as “sensors”, which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur.

Immersive Emotion Analysis in VR Environments: A Sensor-Based Approach to Prevent Distortion

As virtual reality (VR) technology advances, research has focused on enhancing VR content for a more realistic user experience. Traditional emotion analysis relies on surveys, but they suffer from delayed responses and decreased immersion, leading to distorted results. To overcome these limitations, we propose an emotion analysis method using sensor data in the VR environment. Our approach can take advantage of the user’s immediate response and not reduce immersion. Linear regression, classification analysis, and tree-based methods were applied to electrocardiogram and galvanic skin response (GSR) sensor data to measure valence and arousal values. We introduced a novel emotional dimension model by analyzing correlations between emotions and the valence and arousal values. Experimental results demonstrated the highest accuracy of 77% and 92.3% for valence and arousal prediction, respectively, using GSR sensor data. Furthermore, an accuracy of 80.25% was achieved in predicting valence and arousal using nine emotions. Our proposed model improves VR content through more accurate emotion analysis in a VR environment, which can be useful for targeting customers in various industries, such as marketing, gaming, education, and healthcare.

Sensor-Based Quantitative Assessment of Children's Fine Motor Competence: An Instrumented Version of the Placing Bricks Test.

The assessment of fine motor competence plays a pivotal role in neuropsychological examinations for the identification of developmental deficits. Several tests have been proposed for the characterization of fine motor competence, with evaluation metrics primarily based on qualitative observation, limiting quantitative assessment to measures such as test durations. The Placing Bricks (PB) test evaluates fine motor competence across the lifespan, relying on the measurement of time to completion. The present study aims at instrumenting the PB test using wearable inertial sensors to complement PB standard assessment with reliable and objective process-oriented measures of performance. Fifty-four primary school children (27 6-year-olds and 27 7-year-olds) performed the PB according to standard protocol with their dominant and non-dominant hands, while wearing two tri-axial inertial sensors, one per wrist. An ad hoc algorithm based on the analysis of forearm angular velocity data was developed to automatically identify task events, and to quantify phases and their variability. The algorithm performance was tested against video recordings in data from five children. Cycle and Placing durations showed a strong agreement between IMU- and Video-derived measurements, with a mean difference <0.1 s, 95% confidence intervals <50% median phase duration, and very high positive correlation (ρ > 0.9). Analyzing the whole population, significant differences were found for age, as follows: six-year-olds exhibited longer cycle durations and higher variability, indicating a stage of development and potential differences in hand dominance; seven-year-olds demonstrated quicker and less variable performance, aligning with the expected maturation and the refined motor control associated with dominant hand training during the first year of school. The proposed sensor-based approach allowed the quantitative assessment of fine motor competence in children, providing a portable and rapid tool for monitoring developmental progress.

Optimizing Water Use for Okra Cultivation: A Soil Moisture Sensor-Based Approach

Indian agriculture relies on monsoons, a water source notorious for its unreliability. To ensure sustainable production and productivity, the judicious and timely utilization of available water becomes imperative, emphasizing the need to maximize soil moisture while minimizing water losses. Drip irrigation stands out as a widely adopted and highly regarded water-conserving technology. The potential of an automated drip system becomes evident as it holds the promise of blessing farmers with higher yields despite the constraints of limited irrigation water. The necessity for a soil moisture-based drip irrigation system becomes apparent, tailored to the specific moisture and soil types of individual farms. A low-cost soil moisture sensor-based automated drip irrigation system emerges as the research objective, aiming to enhance efficiency and resource management. Gravity, coupled with drip irrigation, emerges as the most suitable method. The system operates in a closed-loop fashion, continuously monitoring and adjusting based on soil moisture content and the crop's water requirements. In the agricultural landscape of Chhattisgarh, where the total cultivable land spans 4.8 million hectares, with 0.51 million hectares dedicated to cultivating okra crops, the Crop Water Requirement (CWR) for okra varies from 30 cm to 55 cm, influenced by factors such as variety and crop seasons. The research yields insightful results, showcasing maximum water use efficiency in the sensor-based treatment at 46.76 kg/ha/mm, while the control irrigation exhibits a minimum efficiency of 26.66 kg/ha/mm.

Importance of sensor-based nitrogen application and effect of growth parameters in wheat crop

An experiment of sensor-based nitrogen application and effect of growth parameters in wheat crop was conducted during the Rabi season of 2022 at the Technology Park, CTAE campus, MPUAT, Udaipur. This study examined the impact of various fertilizer treatments, including a crop sensor-based approach, on wheat crop growth. The results showed that the crop sensor-based treatment demonstrated significant improvements in plant height and tiller density as compared to the 100% Recommended Dose of Fertilizer (RDF) treatment.”

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Influence of nanocarrier additives on biomechanical response of a rat skin.

Skin health monitoring focuses on identifying diseases through the assessment of the mechanical properties of the skin. These properties may degrade with time, which can alter the skin's natural frequencies and the form of the modes associated with those frequencies. Exploring the skin's mechanical properties can enhance our understanding of its dynamics, improving clinical trials and diagnostics. In this work, the dynamics of the skin were measured using a laser-based non-invasive optical sensor experiment. We measured the skin's mechanical properties over time by analyzing its resonant frequencies and mode shapes. A nanocarrier gel and ketoconazole cream were topically applied to keep the skin hydrated and facilitate deeper penetration of the additives in the skin. Time-based research was used to assess the effect of different formulations on skin elasticity. Experimental results for the modulus of elasticity were compared with those obtained using Finite Element Analysis (FEA) simulations. We observed a reduction in frequencies of cream and gel-treated skin by 29.98% and 44.029% respectively compared to normal skin (frequency: 263.3 ± 1.18 Hz and Modulus of elasticity: 7.56 ± 2.60 MPa). A decrease in stiffness (function of frequency) attributed to increased water content, was observed in cream- and nanocarrier gel-treated skin compared to normal skin. Experimental and numerical results are found to be consistent with one another. This optical sensor-based approach has the potential for studying diseased skin mechanics and its response to gel and cream treatments, aiming to reduce skin disorder morbidity and severity.

Comparative Analysis of Ultrasonic and Traditional Gas-Leak Detection Systems in the Process Industries: A Monte Carlo Approach

Gas leaks can cause disasters at process sites, including fires and explosions, and thus, effective gas-leak detection systems are required. This study investigated the limitations of conventional detectors and introduced an innovative ultrasonic sensor-based approach for continuous monitoring. A new configuration for a stationary remote ultrasonic gas-leak monitoring system is proposed. The selected material was 1-Butene. The detection probability was assessed through a simulation based on a gas-leak scenario, detailing the selection criteria for leak sites and simulation conditions. Computational fluid-dynamics (CFD) simulations were used to evaluate the detection capability of the existing system, whereas Monte Carlo simulations were used to compare it with the proposed ultrasonic system. The CFD simulation was performed by setting the lower detection limit of the concentration-measurement-type gas detector to 600 ppm, and the leak-detection time was approximately 8.895 s. A Monte Carlo simulation accounting for wind direction showed that the existing system’s average detection probability was 3.41%. By integrating the existing and new systems, the detection probability increased by 96.58% and 3.92% compared with that using only the concentration-measurement and ultrasonic measurement methods, respectively. These rates provide valuable insights for future applications in oil refining and petrochemical industries.

Neural-network-based automatic trajectory adaptation for quality characteristics control in powder compaction

AbstractFuture manufacturing systems will have to become more intelligent to be able to guarantee a constantly high quality of products while simultaneously reducing labor-intensive quality-assurance tasks to address the shortage in workforce. In this work, we study the application of neural networks to the field of powder metallurgy and more specifically the production of green parts as part of a typical sintering process. More specifically, we explore the usage of neural-network-based predictions in closed-loop control. We train neural networks based on a series of produced workpieces, and use these networks in closed-loop production to predict quality characteristics like weight and dimensions of the workpiece in real-time. Based on these predictions an adaptive trajectory planner adjusts then trajectory key points and with this the final piston trajectories to bring and keep quality characteristics of workpieces within tolerance. We finally compare the control performance of this neural network-based approach with a pure sensor-based approach. Results indicate that both approaches are able to bring and keep quality characteristics within their tolerance limits, but that the neural network-based approach outperforms the sensor-based approach in the transient phase, whereas in steady state the neural network needed to be updated from time to time to reach the same high performance as the sensor-based approach. Since updating needs to be performed only from time to time, required expensive sensors can be shared among multiple machines and thus, costs can be reduced. At the same time the superior prediction performance of the neural-network-based approach in transient phases can be exploited to accelerate setting up times for new workpieces. Future work will target the automation of the recording of the training dataset, the exploration of further machine learning methods as well as the integration of additional sensor data to further improve predictions.

Robust and adaptive terrain classification and gait event detection system

Real-time gait event detection (GED) system can be utilized for gait analysis and tracking fitness activities. GED for various types of terrains (e.g., stair-walk, uneven surfaces, etc.) is still an open research problem. This study presents an inertial sensor-based approach for real-time GED system that works for diverse terrains in an uncontrolled environment. The GED system classifies three types of terrains, i.e., flat-walk, stair-ascend and stair-descend, with an average classification accuracy of 99%. It also accurately detects various gait events, including, toe-strike, heel-rise, toe-off, and heel-strike. It is computationally efficient, implemented on a low-cost microcontroller, works in real-time and can be used in portable rehabilitation devices for use in dynamic environments.

Automatic trajectory adaptation for the control of quality characteristics in a powder compaction process

The manufacturing process of sintered components requires the compaction of powder in a rigid die and the sintering of the resulting green parts. To produce green parts of the same constant quality over a long period of time, regular quality checks and trajectory adjustments are needed to account for changing operating conditions due to e.g. varying temperature, humidity, stroke rate, or powder quality. Also the first setup of the machine requires manual measurements and iterative adjustments of trajectory key points. To overcome these labour-intensive, manual measurements and adjustments, we propose an algorithm for the automatic adaptation of trajectory key-points that for the first time allows bringing and keeping more than one quality characteristic (mass and dimensions) fully automatically within predefined intervals. To safe costs, we further present two real-time capable, model-based solutions by employing grey-box and black-box models for the online estimation of the workpiece mass and height instead of their direct measurement. The grey-box model combines a filling and analytical compaction model and allows estimating mass and height rather than density as typically investigated in literature. The black-box model based on neural networks represents a complete novelty in the field as no similar data-driven approach could be found that aims at estimating mass and dimensions of green compacts. We finally compare the performance of these models to the baseline of sensor measurements as well as study their effect on the overall control performance. Results indicate that for both sensor-based and model-based solutions it is possible to reach and keep aforementioned quality characteristics within desired intervals. While overall the sensor-based approach performed best, also for the model-based approaches a good estimation performance is observed with the black-box model based on neural networks outperforming the grey-box model. While the black-box approach is fully data-driven and thus allows to widely avoid manual modelling activities, it still requires the presence of expensive sensors for capturing training data. The proposed grey-box approach, on the other hand, requires some manual modelling and model calibration activities, but provides a cost-effective solution to the problem and represents an alternative to more expensive, sensor-based implementations.

Machine learning and sensor-based approach for defect detection in MEX additive manufacturing process- A Review

Machine learning and sensor-based approach for defect detection in MEX additive manufacturing process- A Review

Comparative analysis of biophysical methods for monitoring protein proximity induction in the development of small molecule degraders

BackgroundTargeted protein degradation relies on inducing proximity between an E3 ubiquitin ligase and a target protein, and subsequent proteasomal degradation of the latter. Biophysical methods allow the measurement of the ternary complex formation by recombinant target and E3 ligase proteins in the presence of molecular glues and bifunctional degraders. The development of new chemotypes of degraders mediating ternary complex formation of unknown dimensions and geometries requires the use of different biophysical approaches. MethodsThe TR-FRET and AlphaLISA platforms have been applied to study molecular glues and bifunctional degraders. The performance of the label-based proximity assays was compared with the BLI method, which is a label-free, sensor-based approach. ResultsWe present and compare two commonly used assays to monitor proximity induction, AlphaLISA and TR-FRET. The LinkScape system consisting of the CaptorBait peptide and the CaptorPrey protein is a novel method of protein labeling compatible with TR-FRET assay. ConclusionsThe TR-FRET and AlphaLISA proximity assays enable detection of ternary complexes formed between an E3 Ligase, a target protein and a small molecule degrader. Experiments with different chemotypes of GSPT1 degraders showed that ALphaLISA was more susceptible to chemotype-dependent interference than TR-FRET assay. General significanceThe discovery and optimization of small-molecule inducers of ternary complexes is greatly accelerated by using biophysical assays. The LinkScape-based TR-FRET assay is an alternative to antibody-based proximity assays due to the CaptorPrey's subnanomolar affinity to the CaptorBait-tagged protein target, and the 10-fold lower molecular weight of the CaptorPrey protein compared to the antibody.