2023
1.
Wang, Xueyi; Talavera, Estefania; Karastoyanova, Dimka; Azzopardi, George
Fall detection with a non-intrusive and first-person vision approach Journal Article
Links | BibTeX | Altmetric | Tags: deep learning, egocentric vision, wearables
@article{nokey,
title = {Fall detection with a non-intrusive and first-person vision approach},
author = {Wang, Xueyi and Talavera, Estefania and Karastoyanova, Dimka and Azzopardi, George},
doi = {10.1109/JSEN.2023.3314828},
year = {2023},
date = {2023-09-19},
urldate = {2023-09-04},
journal = {IEEE Sensors Journal},
keywords = {deep learning, egocentric vision, wearables},
pubstate = {published},
tppubtype = {article}
}
2021
2.
Lövdal, S. Sofie; Hartigh, Ruud J. R. Den; Azzopardi, George
Injury Prediction in Competitive Runners With Machine Learning Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: predictive analysis, sport science, wearables
@article{injury2021b,
title = {Injury Prediction in Competitive Runners With Machine Learning},
author = {S. Sofie L\"{o}vdal and Ruud J.R. Den Hartigh and George Azzopardi},
doi = {https://doi.org/10.1123/ijspp.2020-0518},
year = {2021},
date = {2021-04-29},
urldate = {2021-04-29},
journal = {International Journal of Sports Physiology and Performance},
volume = {16},
issue = {10},
pages = {1522-1531},
abstract = {Purpose: Staying injury free is a major factor for success in sports. Although injuries are difficult to forecast, novel technologies and data-science applications could provide important insights. Our purpose was to use machine learning for the prediction of injuries in runners, based on detailed training logs. Methods: Prediction of injuries was evaluated on a new data set of 74 high-level middle- and long-distance runners, over a period of 7 years. Two analytic approaches were applied. First, the training load from the previous 7 days was expressed as a time series, with each day’s training being described by 10 features. These features were a combination of objective data from a global positioning system watch (eg, duration, distance), together with subjective data about the exertion and success of the training. Second, a training week was summarized by 22 aggregate features, and a time window of 3 weeks before the injury was considered. Results: A predictive system based on bagged XGBoost machine-learning models resulted in receiver operating characteristic curves with average areas under the curves of 0.724 and 0.678 for the day and week approaches, respectively. The results of the day approach especially reflect a reasonably high probability that our system makes correct injury predictions. Conclusions: Our machine-learning-based approach predicts a sizable portion of the injuries, in particular when the model is based on training-load data in the days preceding an injury. Overall, these results demonstrate the possible merits of using machine learning to predict injuries and tailor training programs for athletes.},
keywords = {predictive analysis, sport science, wearables},
pubstate = {published},
tppubtype = {article}
}
Purpose: Staying injury free is a major factor for success in sports. Although injuries are difficult to forecast, novel technologies and data-science applications could provide important insights. Our purpose was to use machine learning for the prediction of injuries in runners, based on detailed training logs. Methods: Prediction of injuries was evaluated on a new data set of 74 high-level middle- and long-distance runners, over a period of 7 years. Two analytic approaches were applied. First, the training load from the previous 7 days was expressed as a time series, with each day’s training being described by 10 features. These features were a combination of objective data from a global positioning system watch (eg, duration, distance), together with subjective data about the exertion and success of the training. Second, a training week was summarized by 22 aggregate features, and a time window of 3 weeks before the injury was considered. Results: A predictive system based on bagged XGBoost machine-learning models resulted in receiver operating characteristic curves with average areas under the curves of 0.724 and 0.678 for the day and week approaches, respectively. The results of the day approach especially reflect a reasonably high probability that our system makes correct injury predictions. Conclusions: Our machine-learning-based approach predicts a sizable portion of the injuries, in particular when the model is based on training-load data in the days preceding an injury. Overall, these results demonstrate the possible merits of using machine learning to predict injuries and tailor training programs for athletes.
2020
3.
Wang, Xueyi; Ellul, Joshua; Azzopardi, George
Elderly fall detection systems: A literature survey Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: convnets, deep learning, egocentric vision, fall detection, predictive analysis, wearables
@article{wang2020elderly,
title = {Elderly fall detection systems: A literature survey},
author = {Xueyi Wang and Joshua Ellul and George Azzopardi},
doi = {https://doi.org/10.3389/frobt.2020.00071},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Frontiers in Robotics and AI},
volume = {7},
pages = {71},
publisher = {Frontiers},
abstract = {Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.},
keywords = {convnets, deep learning, egocentric vision, fall detection, predictive analysis, wearables},
pubstate = {published},
tppubtype = {article}
}
Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.