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Visualizing Academic Contributions to Achieving the Sustainable Development Goals through AI

J. Alberto Conejero
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Visualizing Academic Contributions to Achieving the Sustainable Development Goals through AI

How are you contributing to SDGs and measuring sustainable improvements? AI solutions can help you to quantify it. This pilot experience shows the case of the university’s scientific contributions.

we have an initial pilot experience with this AI solution to quantify the impact of the scientific contributions of the university members to the SDGs agenda. For this experience, we have downloaded 50,000 abstracts of works with at least one author from UPV and more than 500 characters in length since 1990. Our solution is based on the Automatic Classification of Impact to Sustainable Development Goals (ASDG) pipeline, which relies on four language models (NMF, LDA, BERTopic, and Top2VEC). This has been applied to automatically identify these scientific contributions’ potential impact on the SDG agenda.

See more details at: D. Domingo-Calabuig, S. Hoyas, R. Vinuesa, and J.A. Conejero. Visualizing Academic Contributions to Achieving the Sustainable Development Goals through AI: The Case of Universitat Politècnica de València. ACS Sustainable Resour. Manage. 2024. https://doi.org/10.1021/acssusresmgt.4c00074

Multifractal spectrum and complex cepstrum analysis of armature currents and stray flux signals for sparking detection in DC motors

J. Alberto Conejero
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0
Multifractal spectrum and complex cepstrum analysis of armature currents and stray flux signals for sparking detection in DC motors

Sparking is a common phenomenon in brushed DC motors. Early detection of sparks can prevent their aggravation and promptly adopt appropriate maintenance actions. In this work, we propose the application of a multifractal analysis to armature currents and stray flux signals to detect the presence of sparks in the commutation system of DC motors. Two methodologies are proposed in the paper, the first one using the spectral kurtosis of armature currents and the second one using the flux signal envelope. We apply this method to signals captured both under starting and at steady state to compare their suitability and analyze their feasibility to be used as the basis of the sparking detection method. Additionally, we also propose a quantitative indicator based on the variance of the complex cepstrum to determine the severity of the failure based on the cepstral analysis of both considered signals. Our results demonstrate the potential of the approach for sparking detection and sparking level assessment in DC motors and its suitability for potential future incorporation into autonomous systems.

This work has been published in: J. Guerra-Carmenate et al., «Multifractal Spectrum and Complex Cepstrum Analysis of Armature Currents and Stray Flux Signals for Sparking Detection in DC Motors,» in IEEE Transactions on Industry Applications, vol. 60, no. 1, pp. 164-173, Jan.-Feb. 2024, doi: 10.1109/TIA.2023.3312235.

A pre-processing procedure for the implementation of the greedy rank-one algorithm to solve high-dimensional linear systems

J. Alberto Conejero
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A pre-processing procedure for the implementation of the greedy rank-one algorithm to solve high-dimensional linear systems

Algorithms that use tensor decompositions are widely used due to how well they perfor with large amounts of data. Among them, we find the algorithms that search for the solution of a linear system in separated form, where the greedy rank-one update method stands out, to be the starting point of the famous proper generalized decomposition family. When the matrices of these systems have a particular structure, called a Laplacian-like matrix which is related to the aspect of the Laplacian operator, the convergence of the previous method is faster and more accurate.

The main goal of this paper is to provide a procedure that explicitly gives, for a given square matrix, its best approximation to the set of Laplacian-like matrices. Clearly, if the residue of this approximation is zero, we will be able to solve, by using the greedy rank-one update algorithm, the associated linear system at a lower computational cost. As a particular example, we prove that the discretization of a general partial differential equation of the second order without mixed derivatives can be written as a linear system with a Laplacian-type matrix. Finally, some numerical examples based on partial differential equations are given.

To sum up, in this work, we have studied the Laplacian decomposition algorithm, which, given any square matrix, calculates its best Laplacian approximation. For us, the greatest interest in this algorithm lies in the computational improvement of combining it with the GROU Algorithm 1 to solve linear systems through the discretization of a partial derivative equation. Said improvement can be seen in the different numerical examples shown, where we have compared this procedure with the standard resolution of Matlab by means of the instruction. This proposal is a new way of dealing with certain large-scale problems, where classical methods prove to be more inefficient.

You can have access (open) here:

J.A. Conejero, A., Falcó, M. Mora-Jiménez. A pre-processing procedure for the implementation of the greedy rank-one algorithm to solve high-dimensional linear systems. AIMS Math 8(11) 25633-25653 (2023). doi:10.3934/math.20231308

The Electric Vehicle Traveling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics

J. Alberto Conejero
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The Electric Vehicle Traveling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics

With the increasing demand for online shopping and home delivery services, optimizing the routing of electric delivery vehicles in urban areas is crucial to reduce environmental pollution and improve operational efficiency. To address this opportunity, we optimize the Steiner Traveling Salesman Problem (STSP) for electric vehicles (EVs) in urban areas by combining city graphs with topographic and traffic information. The STSP is a variant of the traditional Traveling Salesman Problem (TSP) where it is not mandatory to visit all the nodes present in the graph. We train an artificial neural network (ANN) model to estimate electric consumption between nodes in the route using synthetic data generated with historical traffic simulation and topographical data. This allows us to generate smaller-weighted graphs that transform the problem from an STSP to a normal TSP where the 2-opt optimization algorithm is used to solve it with a Nearest Neighbor (NN) initialization. Compared to the approach of optimizing routes based on distance, our proposed algorithm offers a fast solution to the STSP for EVs (EV-STSP) with routes that consume 17.34% less energy for the test instances generated.

This work has been the result of a students’s project in the subject Project III in the Data Science degree. It has been published in Algorithms:

Ahsini, Y.; Díaz-Masa, P.; Inglés, B.; Rubio, A.; Martínez, A.; Magraner, A.; Conejero, J.A. The Electric Vehicle Traveling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics. Algorithms 2023, 16, 402. https://doi.org/10.3390/a16090402

A variant-dependent molecular clock with anomalous diffusion models SARS-CoV-2 evolution in humans (PNAS)

J. Alberto Conejero
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A variant-dependent molecular clock with anomalous diffusion models SARS-CoV-2 evolution in humans (PNAS)

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans has been monitored at an unprecedented level due to the public health crisis, yet the stochastic dynamics underlying such a process is dubious. Here, considering the number of acquired mutations as the displacement of the viral particle from the origin, we performed biostatistical analyses from numerous whole genome sequences on the basis of a time-dependent probabilistic mathematical model. We showed that a model with a constant variant-dependent evolution rate and nonlinear mutational variance with time (i.e., anomalous diffusion) explained the SARS-CoV-2 evolutionary motion in humans during the first 120 wk of the pandemic in the United Kingdom. In particular, we found subdiffusion patterns for the Primal, Alpha, and Omicron variants but a weak superdiffusion pattern for the Delta variant. Our findings indicate that non-Brownian evolutionary motions occur in nature, thereby providing insight for viral phylodynamics. 19This work has been published in Proc. Natl. Acad. Sci. USA (PNAS).

L. Goiriz, R. Ruiz, Ò. Garibo-I-Orts, J.A. Conejero, and G. Rodrigo. A variant-dependent molecular clock with anomalous diffusion models SARS-CoV-2 evolution in humans. Proc. Natl. Acad. Sci. USA. 2023 120(30) e2303578120 (2023). https://doi.org/10.1073/pnas.2303578120

Halting generative AI advancements may slow down progress in climate research (Nature Clim. Change)

J. Alberto Conejero
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Halting generative AI advancements may slow down progress in climate research (Nature Clim. Change)

Large language models offer an opportunity to advance climate and sustainability research. We believe that a focus on regulation and validation of generative artificial intelligence models would provide more benefits to society than a halt in development.

In our recent work published in Nature Climate Research, we expose that the research and practice AI community has animated a lively debate around the moratorium request that was published in March 2023 to pause the training of very large AI systems4. Although we share some of the concerns that the signatories rightfully flag, we feel that the letter’s proposed solution to pause progress can be misunderstood to imply a broader halt on AI development by the policy community. Furthermore, the letter does not open a holistic debate about implications of this temporary halt for other scientific communities. We believe that the risk is that some countries, not aware of the full picture of this debate, may halt developments in AI altogether. As a result, research on key areas could be slowed down by a moratorium that limits a tool that has become essential to advance knowledge on complex problems with hidden interactions, such as climate change.

You can see our work here: Larosa, F., Hoyas, S., García-Martínez, J. et al. Halting generative AI advancements may slow down progress in climate research. Nat. Clim. Chang. (2023). https://doi.org/10.1038/s41558-023-01686-5

Photonic snake states in two-dimensional frequency combs (Nature Photonics)

J. Alberto Conejero
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Photonic snake states in two-dimensional frequency combs (Nature Photonics)

Taming the instabilities inherent to many nonlinear optical phenomena is of paramount importance for modern photonics. In particular, the so-called snake instability is universally known to severely distort localized wave stripes, leading to the occurrence of transient, short-lived dynamical states that eventually decay. This phenomenon is ubiquitous in nonlinear science—from river meandering to superfluids—and so far it apparently remains uncontrollable; however, here we show that optical snake instabilities can be harnessed by a process that leads to the formation of stationary and robust two-dimensional zigzag states. We find that such a new type of nonlinear waves exists in the hyperbolic regime of cylindrical microresonators, and that it naturally corresponds to two-dimensional frequency combs featuring spectral heterogeneity and intrinsic synchronization. We uncover the conditions of the existence of such spatiotemporal photonic snakes and confirm their remarkable robustness against perturbations. Our findings represent a new paradigm for frequency comb generation, thus opening the door to a whole range of applications in communications, metrology, and spectroscopy.

Our results are developed in the following publication:

Ivars, S.B., Kartashov, Y.V., de Córdoba, P.F., Conejero, J.A., Torner, L., and Milián, C. Photonic snake states in two-dimensional frequency combs. Nat. Photon. (2023). doi:10.1038/s41566-023-01220-1

Inferring the fractional nature of Wu Baleanu trajectories

J. Alberto Conejero
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Inferring the fractional nature of Wu Baleanu trajectories

We infer the parameters of fractional discrete Wu Baleanu time series by using machine learning architectures based on recurrent neural networks. Our results shed light on how clearly one can determine that a given trajectory comes from a specific fractional discrete dynamical system by estimating the fractional exponent and the growth parameter μ. With this example, we also show how machine learning methods can be incorporated into the study of fractional dynamical systems.

We have published this work in Nonlinear Dynamics as a Gold Open Access publication with the support of CRUE-Universitat Politècnica de València. The reference is

J. Alberto Conejero, Ò. Garibo-i-Orts, C. Lizama. Inferring the fractional nature of Wu Baleanu trajectories. Nonlinear Dyn. doi:10.1007/s11071-023-08463-1

Automatic classification of field winding faults in synchronous motors

J. Alberto Conejero
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Automatic classification of field winding faults in synchronous motors

In this work, the application of the bicoherence (a squared normalized version of the bispectrum) of the stray flux signal is proposed as a way of detecting faults in the field winding of synchronous motors. These signals are analyzed both under the starting and at steady state regime. Likewise, two quantitative indicators are proposed, the first one based on the maximum values of the asymmetry and the kurtosis of the bicoherence matrix obtained from the flux signals and the second one relying on an algorithm based on the bicoherence image segmentation of the obtained pattern for each analyzed state. The results are analyzed through a comparative study for the two considered motor regimes, obtaining satisfactory results that sustain the potential application of the proposed methodology for the automatic field winding fault detection in real applications.

M. E. Iglesias-Martínez et al., «Automatic Classification of Field Winding Faults in Synchronous Motors based on Bicoherence Image Segmentation and Higher Order Statistics of Stray Flux Signals,» in IEEE Trans. Ind. Appl., doi: 10.1109/TIA.2023.3262220.

https://ieeexplore.ieee.org/document/10081463

Gramian angular fields for leveraging pretrained computer vision models with anomalous diffusion trajectories

J. Alberto Conejero
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0
Gramian angular fields for leveraging pretrained computer vision models with anomalous diffusion trajectories

Anomalous diffusion is present at all scales, from atomic to large ones. Some exemplary systems are ultracold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, arthropods’ free movement, and birds’ migration patterns. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent α with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge [Muñoz-Gil et al., Nat. Commun. 12, 6253 (2021)]. Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian angular fields (GAF) to encode one-dimensional trajectories as images (Gramian matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pretrained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime and infer the anomalous diffusion exponent α. Short raw trajectories of lengths between 10 and 50 are commonly encountered in single-particle tracking experiments and are the most difficult ones to characterize. We show that GAF images can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.

Ò. Garibo-i-Orts, N. Firbas, L. Sebastiá, and J.A. Conejero. Gramian angular fields for leveraging pretrained computer vision models with anomalous diffusion trajectories
Phys. Rev. E 107, 034138 (2022).

The Sustainable Development Goals and Aerospace Engineering

J. Alberto Conejero
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The Sustainable Development Goals and Aerospace Engineering

The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs’ achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing on accuracy, scalability, and objectiveness. With this objective in mind, in this work, we develop ASDG, an easy-to-use Artificial-Intelligence-based model for automatically identifying the potential impact of scientific papers on the UN SDGs. As a demonstrator of ASDG, we analyze the alignment of recent aerospace publications with the SDGs. The Aerospace data set analyzed in this paper consists of approximately 820,000 papers published in English from 2011 to 2020 and indexed in the Scopus database. The most-contributed SDGs are 7 (on clean energy), 9 (on industry), 11 (on sustainable cities), and 13 (on climate action). The establishment of the SDGs by the UN in the middle of the 2010 decade did not significantly affect the data. However, we find clear discrepancies among countries, likely indicative of different priorities. Also, different trends can be seen in the most and least cited papers, with apparent differences in some SDGs. Finally, the number of abstracts the code cannot identify decreases with time, possibly showing the scientific community’s awareness of SDG.

Dynamics of vortices in a Bose-Einstein condensate

J. Alberto Conejero
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Dynamics of vortices in a Bose-Einstein condensate
We present a method to study the dynamics of a quasi-two dimensional Bose-Einstein condensate which initially contains several vortices at arbitrary locations. The method allows one to find the analytical solution for the dynamics of the Bose-Einstein condensate in a homogeneous medium and in a parabolic trap, for the ideal non-interacting case. Secondly, the method allows one to obtain algebraic equations for the trajectories of the position of phase singularities present in the initial condensate along with time (the vortex lines). With these equations, one can predict quantities of interest, such as the time at which a vortex and an antivortex contained in the initial condensate will merge. For the homogeneous case, this method was introduced in the context of photonics. Here, we adapt it to the context of Bose-Einstein condensates, and we extend it to the trapped case for the first time. Also, we offer numerical simulations in the non-linear case, for repulsive and attractive interactions. We use a numerical split-step simulation of the non-linear Gross-Pitaevskii equation to determine how these trajectories and quantities of interest are changed by the interactions. We illustrate the method with several simple cases of interest, both in the homogeneous and parabolically trapped systems.
This work has appeared in
S. De María-García, A. Ferrando, J.A. Conejero, P. Fernández De Córdoba and M.A. García-March. A Method for the Dynamics of Vortices in a Bose-Einstein Condensate: Analytical Equations of the Trajectories of Phase Singularities Condens. Matter 2023, 8(1), 12; DOI: 10.3390/condmat8010012

Characterization of anomalous diffusion through convolutional transformers

J. Alberto Conejero
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Characterization of anomalous diffusion through convolutional transformers

The results of the Anomalous Diffusion Challenge (AnDi Challenge) (Muñoz-Gil G et al 2021 Nat. Commun. 12 6253) have shown that machine learning methods can outperform classical statistical methodology at the characterization of anomalous diffusion in both the inference of the anomalous diffusion exponent α associated with each trajectory (Task 1), and the determination of the underlying diffusive regime which produced such trajectories (Task 2). Furthermore, of the five teams that finished in the top three across both tasks of the AnDi Challenge, three of those teams used recurrent neural networks (RNNs). While RNNs, like the long short-term memory network, are effective at learning long-term dependencies in sequential data, their key disadvantage is that they must be trained sequentially. In order to facilitate training with larger data sets, by training in parallel, we propose a new transformer based neural network architecture for the characterization of anomalous diffusion. Our new architecture, the Convolutional Transformer (ConvTransformer) uses a bi-layered convolutional neural network to extract features from our diffusive trajectories that can be thought of as being words in a sentence. These features are then fed to two transformer encoding blocks that perform either regression (Task 1 1D) or classification (Task 2 1D). To our knowledge, this is the first time transformers have been used for characterizing anomalous diffusion. Moreover, this may be the first time that a transformer encoding block has been used with a convolutional neural network and without the need for a transformer decoding block or positional encoding. Apart from being able to train in parallel, we show that the ConvTransformer is able to outperform the previous state of the art at determining the underlying diffusive regime (Task 2 1D) in short trajectories (length 10–50 steps), which are the most important for experimental researchers.

N. Firbas, Ò. Garibo-i-Orts, M.A. Garcia-March, and J.A. Conejero. Characterization of anomalous diffusion through convolutional transformers.  J. Phys. A: Math. Theor. 56 014001 doi:10.1088/1751-8121/acafb3

Soft Skills Pecha Kucha session

J. Alberto Conejero
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Soft Skills Pecha Kucha session

As a part of my course on Soft Skills in the MSc on Transmedia Communication, we held a Pecha Kucha session with our students. Pecha Kucha is a fast-paced, concise presentation style that challenges presenters to deliver their message in a limited amount of time, typically 20 slides for 20 seconds each. Our idea is to bring attention to these critical skills and provide an opportunity for individuals to share their experiences and insights. With these concise and engaging Presentations, we want to show multiple perspectives that also encouragee Interaction between speakers and the public and promotes learning and spreading of the importance of soft skills in communication.

ICEGOV’22 – ASDG — An AI-based framework for automatic classification of impact on the SDGs

J. Alberto Conejero
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0
ICEGOV’22 – ASDG — An AI-based framework for automatic classification of impact on the SDGs

Achieving the Sustainable Development Goals of the United Nations is the primary goal of the 2030 Agenda. A critical step towards that objective is identifying if the scientific production is going in this way. Funders must do a manual recognition, impacting accuracy, scalability, and objectiveness. For this reason, we propose in this work an AI-based model for the automatic classification of scientific papers based on their impacts on the SDGs.

The training database consists of manually extracted texts from the UN page. After preprocessing these texts, we train three models: NMF, LDA, and Top2Vec. The output of these models is the probability of a paper being associated with each SDG. We then combine their scores by implementing a voting function to take advantage of their inherently different mathematical nature. To validate this methodology, we use the database provided by Vinuesa et al., Nature Communications 11, with more than 150 papers labeled with at least 1 SDG. Using only the abstracts, we correctly identify a of the SDGs presented in a paper, while a better is obtained when fetching the complete paper information.

Moreover, we find that the other identified SDGs which were not labeled are also related to the text contents. We recognize that more training files are required for the other cases since they are based on more complex human reasoning. We open-source these databases and trained models to enable future investigation in this field and allow public institutions to use this tool.

S. Hoyas et al. 2022. ASDG — An AI-based framework for automatic classification of impact on the SDGs. In Proceedings of the 15th International Conference on Theory and Practice of Electronic Governance (ICEGOV ’22). Association for Computing Machinery, New York, NY, USA, 119–123. https://doi.org/10.1145/3560107.3560128

 

COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave

J. Alberto Conejero
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COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave

The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported.

Methods: In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks.

We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model.

We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts.

You can access the publication here https://www.frontiersin.org/articles/10.3389/fpubh.2022.1010124/full

Joining Intelligent Computing & Systems Optimization (ICSO) Meta Group

J. Alberto Conejero
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Joining Intelligent Computing & Systems Optimization (ICSO) Meta Group

I have recently joined ICSO-Meta group, an inter-university meta-group of experts in Data Analytics, Industrial Optimization, Machine Learning, and Artificial Intelligence. These experts belong to some of the most prestigious national universities and carry out research, transfer, and training activities in collaboration with other academic and industrial partners worldwide.

ICSO Meta develops innovation and transfer projects in multiple business and industrial areas, including Smart Cities, Logistics and Transportation, Production, Finance and Insurance, Energy, Bioinformatics and Healthcare, Telecommunications Networks, etc.

You can see more details about this group at https://icso.webs.upv.es/

Benicàssim Tech 2022 Artificial Intelligence & Optimization Workshop

J. Alberto Conejero
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Benicàssim Tech 2022 Artificial Intelligence & Optimization Workshop

The applications of Artificial Intelligence, Data Science, and Systems Optimization allow the efficient and sustainable digitalization of our business, industrial, and social fabric. In collaboration with the Benicassim City Council, the Universitat Politècnica de València and the Universitat Jaume I of Castellón, I have participated as member of the organizing committee of the Benicassim Artificial Intelligence & Optimization workshop (BAIO 2022). This event, framed within the actions of Benicassim Tech, that has been held in Villa Elisa, Benicàssim (https://goo.gl/maps/M5qnHs3knqEy4sZ67) on 20 and 21 October 2022.

With the aim of promoting applied research and transfer in the above mentioned areas, this workshop has enabled direct interaction between a select group of experts from different universities and companies in the sector. In the workshop, we have explored different ways of effective collaboration in CDTI, Red.es, Next Generation, and Horizon Europe projects. The workshop has been also an excellent opportunity to present in society some of the research and transfer projects that we are currently developing in university-industry collaborations.

You can find more information at https://baio.webs.upv.es/

Testing lung cancer patients’ and oncologists’ experience with the Lalaby App for monitoring the Quality of Life

J. Alberto Conejero
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Testing lung cancer patients’ and oncologists’ experience with the Lalaby App for monitoring the Quality of Life

As there is now a growing interest in mHealth apps for cancer patients, we here present and test the Lalaby App to monitor lung cancer patients’ Quality of life (QoL) through mobile sensors and integrated questionnaires. The app was used in a 2-week study to register two lung cancer patients’ activity without problems or interruptions. The patients frequently reported activities, symptoms, and questionnaires, indicating their engagement with the app. They registered their experience through the UEQ-S integrated into the app. Patient 1 mainly reported a neutral experience, while Patient 2 found it highly positive. They considered the app leading-edge and helpful and would recommend it to others, while both patients valued it positively (3.72 and 4.64 on a scale of 1–5). The app’s aesthetics and its notifications helped their engagement. We found correlations between sensors’ data and patients’ QoL. We also detected QoL and functional status variations after treatment for both patients. After a “Tasks Test,” two oncologists assessed the app’s dashboard usability as excellent (SUS scores 85 and 87.5 on a 0–100 scale), easy-to-use and helpful. Their experience was positive (UEQ-S overall scale 2.81 (mean), −3 to +3 scale). The app allows monitoring the QoL of lung cancer patients remotely and in real-time while controlling patients’ experience to stop the use if necessary, avoiding overwhelm.

You can find the paper at https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2121561

Lalaby app: Phone sensors monitoring quality of life indicators for cancer patients

J. Alberto Conejero
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Lalaby app: Phone sensors monitoring quality of life indicators for cancer patients

Non-small cell lung cancer (NSCLC) constitutes a healthcare problem because of its incidence, mortality, and the burden of symptoms that it produces. Therefore, treatment goals include symptom control and maintenance of quality of life (QoL). However, QoL is not fully evaluated in clinical practice. The higher acceptance of wearables technology enables to automatically monitor health parameters. Sensors from smartphones allow continuous quantification of parameters regarding QoL along with patient reported outcomes measures (PROMs) using a single app. Our objective is to assess the feasibility of using smartphone sensors and self-registering PROMs through the app Lalaby® in outpatients with NSCLC receiving systemic treatment.

You can access the paper here:

Soria-Comes, T., et al. «EP10. 01-017 Use of Lalaby in Lung Cancer Patients to Trace Performance from Phone Sensors and Reported Outcomes Involving Quality of Life.» Journal of Thoracic Oncology 17.9 (2022): S507-S508. https://doi.org/10.1016/j.jtho.2022.07.894

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Personal website of J. Alberto Conejero (Full Professor of Applied Mathematics at Universitat Politècnica de València, Spain.

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J. Alberto Conejero - Universitat Politècnica de València