Suchergebnisse

International Conference on Intelligent Systems Design and Applications (ISDA) (19th. 2019. Online) ; Fall Detection (FD) has drawn the attention of the research community for several years. A possible solution relies on on-wrist wear- able devices including tri-axial accelerometers performing FD autonomously. This type of approaches makes use of an event detection stage followed by some pre-processing and a final classification stage. The event de- tection stage is basically performed using thresholds or a combination of thresholds and finite state machines. In this research, a novel event detec- tion is proposed avoiding the use of user predefined thresholds; this fact represents the main contribution of this study. It is worth noticing that avoiding the use of thresholds make solutions more general and easy to deploy. Moreover, a new set of features are extracted from a time window whenever a peak is detected, classifying it with a Neural Network. The proposal is evaluated using the UMA Fall, one of the publicly available simulated fall detection data sets. Results show the improvements in the event detection using the new proposal, outperforming the base line method; however, the classifica- tion stage still needs improvement. Future work includes introducing a finite state machine in the event detection method, adding extra features and a pre-classification of the post-peak interval and a better training configuration of the Neural Networks. ; This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2017-84804-R, and by the Grant FC-GRUPINIDI/2018/000226 project from the Asturias Regional Government.

International Conference on Hybrid Artificial Intelligent Systems, HAIS (15th. 2020. Gijón, Spain) ; This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO TIN2017-84804-R and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government.

Spanish Ministry of Science and Innovation, Spanish Government [MINECO-TIN2017-84804-R, FC-GRUPIN-IDI/2018/000226]; Asturias Regional Government.

This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government.

IEEE International Symposium on Computer-Based Medical Systems (CBMS) (34th. 2021. Virtual) ; This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R and by the Grant FC-GRUPIN-IDI/2018/000226 project from the Asturias Regional Government.

This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2017-84804-R, and by the Grant FC-GRUPIN-IDI/2018/000226 from the Asturias Regional Government.

This work was supported by the Innterconecta call of the European Union Structural Funds (FEDER) INTERCONECTA CDTI project ABECATIM (SOL-00082271 / ITC-20151039) and by the Spanish Government through the MINECO projects TIN2017-87600-P and TIN2017-87600-P. Also, by the University of Oviedo project FC-GRUPIN-IDI/2018/000176.

This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R and by the Grant FC-GRUPIN-IDI/2018/000226 project from the Asturias Regional Government.

Detecting falls in the elderly population is a very important issue that is related with the time of recovery. This study focuses on using wearable smart watches to monitor the movements of the user in order to detect patterns that might be related to fall events. The proposed solution explores Symbolic Aggregate approXimation (SAX) Time Series representation, together with two information retrieval techniques enriched with transfer learning (TL). The solution is user centred; that is, a model is developed for each specific user. Basically, the fall detection approach makes use of a finite-state machine to detect peaks; the time series window embedding these peaks are represented using SAX. Assuming the data from the public fall detection data sets are valid, a dictionary is prepared using the most relevant words. This dic- tionary is then introduced as previous knowledge to an online learning classifier that is trained with normal activities of daily living. The two classifiers are evaluated and compared with two classical approaches. Before this comparison, two clustering approaches are studied to produce the bag of relevant words. A complete experimen- tation is included, which makes use of several publicly available data sets and also with a data set developed by the research group. Comparisons are performed for all the data sets, showing how the TL stage empowers the classifier. The results show that this solution produces high detection rates and at the same time performed simi- larly for all the individuals tested. Furthermore, the positive effects of TL in this con- text are clearly remarked. ; This research has been funded by the Spanish Ministry of Science and Innovation, under Project MINECO-TIN2017-84804-R, and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government (Gobierno del Principado de Asturias).

Fall detection (FD) has been the focus of many research studies during the last years. Developing reliable FD systems is relevant, for instance, to pro- vide support to the elderly population in their everyday life. Besides, the generalization of the use of wearable devices (and more specifically, on-wrist devices) to measure the daily activity strongly suggests that in a short period of time, the elderly people will be making use of this type of devices. On-wrist devices can be used as the FD basic sensing unit; while the intelligent classi- fication can be obtained either autonomously (on the device) or requested to a remote service (via the paired smartphone or via web services). This study tries to analyze the current challenges in autonomous on-wrist wearable de- vices for producing a reliable and robust FD system. To do so, we analyze the related work; one of the possible solutions is implemented with several alternatives and evaluated with publicly available simulated falls data sets. The most remarkable findings in this research are that i) real fall data sets are needed, at least, a valid merging method to produce real fall like Time Series, ii) generalized solutions might not be enough and research is needed in models that learns from the user, iii) the need of tuning and fitting to the current user performance, iv) the amount of fall types suggests that hybrid and ensemble approaches might be interesting. ; This research has been funded by the Spanish Ministry of Science and Innovation, under projects MINECO-TIN2014-56967-R and MINECO-TIN2017- 84804-R, and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government.