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- Cajas, V., et al.
(författare)
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Migrating legacy Web applications
- 2021
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Ingår i: Cluster Computing. - : Springer Science and Business Media LLC. - 1386-7857 .- 1573-7543. ; 24:2, s. 1033-1049
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Tidskriftsartikel (refereegranskat)
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| 5. |
- Hemeren, Paul, et al.
(författare)
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The Visual Perception of Biological Motion in Adults
- 2020
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Ingår i: Modelling Human Motion. - Cham : Springer. - 9783030467319 - 9783030467326 ; , s. 53-71
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Bokkapitel (refereegranskat)abstract
- This chapter presents research about the roles of different levels of visual processing and motor control on our ability to perceive biological motion produced by humans and by robots. The levels of visual processing addressed include high-level semantic processing of action prototypes based on global features as well as lower-level local processing based on kinematic features. A further important aspect concerns the interaction between these two levels of processing and the interaction between our own movement patterns and their impact on our visual perception of biological motion. The authors’ results from their research describe the conditions under which semantic and kinematic features influence one another in our understanding of human actions. In addition, results are presented to illustrate the claim that motor control and different levels of the visual perception of biological motion have clear consequences for human–robot interaction. Understanding the movement of robots is greatly facilitated by the movement that is consistent with the psychophysical constraints of Fitts’ law, minimum jerk and the two-thirds power law.
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| 6. |
- Ngoc Phuong, Chau, et al.
(författare)
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Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito
- 2022
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Ingår i: Frontiers in Big Data. - : Frontiers Media SA. - 2624-909X. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO2, PM2.5, SO2, and O3) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: −48.75%, for CO, −45.76%, for SO2, −42.17%, for PM2.5, and −63.98%, for NO2. The reduction of this latter gas has induced an increase of O3 by +26.54%.
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| 7. |
- Ngoc Phuong, Chau, et al.
(författare)
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Ensemble Deep Learning For Classification Of Pollution Peaks
- 2022
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Ingår i: Air and Water Pollution XXX. - 9781784664671 - 9781784664688 ; , s. 25-36
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Konferensbidrag (refereegranskat)abstract
- The concentration peaks of atmospheric pollutants are the most challenging and important phenomena in air quality forecasting. The fact that these elevated levels of pollution do not seem to follow any specific pattern explains why current models still struggle to provide an accurate prediction of these harmful events for human health. The present study tackles this issue by testing several supervised learning methods to discriminate between peak and no peak of concentrations of five contaminants: NO2, CO, SO2, PM2.5, and O3. The classification performance of ensemble decision tree (gradient boosting machine (GBM)) models and ensemble deep learning (EDL) models are compared. The results reveal that the EDL outperforms the GBM model. An analysis of the variable importance (SHapley additive exPlanations (SHAP)) shows that both temporal and meteorological features have an impact on the proposed models. In particular, time of day and wind speed are the most important features to explain the performance of the ensemble DL models.
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| 8. |
- Pérez-Medina, J. -L, et al.
(författare)
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Serious-games-based exercises for arthroplasty rehabilitation
- 2020
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Ingår i: Advances in Usability, User Experience, Wearable and Assistive Technology. - Cham : Springer International Publishing. - 9783030518271 - 9783030518288 ; , s. 619-626
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Konferensbidrag (refereegranskat)
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| 9. |
- Pilco, Hennry, et al.
(författare)
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An agile approach to improve the usability of a physical telerehabilitation platform
- 2019
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 9:3
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Tidskriftsartikel (refereegranskat)abstract
- The goal of a telerehabilitation platform is to safely and securely facilitate the rehabilitation of patients through the use of telecommunication technologies complemented with the use of biomedical smart sensors. The purpose of this study was to perform a usability evaluation of a telerehabilitation platform. To improve the level of usability, the researchers developed and proposed an iterative process. The platform uses a digital representation of the patient which duplicates the therapeutic exercise being executed by the patient; this is detected by a Kinect camera and sensors in real time. This study used inspection methods to perform a usability evaluation of an exploratory prototype of a telerehabilitation platform. In addition, a cognitive workload assessment was performed to complement the usability evaluation. Users were involved through all the stages of the iterative refinement process. Usability issues were progressively reduced from the first iteration to the fourth iteration according to improvements which were developed and applied by the experts. Usability issues originally cataloged as catastrophic were reduced to zero, major usability problems were reduced to ten (2.75%) and minor usability problems were decreased to 141 (38.74%). This study also intends to serve as a guide to improve the usability of e-Health systems in alignment with the software development cycle.
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