Spatiotemporal data visualisation for homecare monitoring of elderly people

Juárez, José M.; Ochotorena, Jose M.; Combi, Carlo

doi:10.1016/j.artmed.2015.05.008

Cited by 24 publications

(10 citation statements)

References 25 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Telemedicine and home monitoring systems José M. Juarez, Jose M. Ochotorena, Manuel Campos, and Carlo Combi, in their paper "Spatiotemporal data visualization for homecare monitoring of elderly people," proposed the multiple temporal axes model to visualize the behavior of elderly people who were living alone so that the health-risk scenarios and repetitive M a n u s c r i p t patterns can be identified to prevent home accidents [3]. The experiments confirmed that the proposed model were useful in representing fall and fatigue scenarios.…”

Section: Data Analytics and Predictive Modelingmentioning

confidence: 99%

Intelligent healthcare informatics in big data era

Yang

Veltri

2015

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Section: Data Analytics and Predictive Modelingmentioning

confidence: 99%

Intelligent healthcare informatics in big data era

Yang

Veltri

2015

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

“…That technique is coupled with an activity recognition module and with visualization tools to allow practitioners inspecting the trend of activity patterns. Visualization of spatiotemporal data extracted from the long-term observation of elderly's activities at home is used in [96] to identify potential risk situations.…”

Section: Long-term Analysis Of Activity Datamentioning

confidence: 99%

Human Activity Recognition in Smart-Home Environments for Health-Care Applications

Civitarese¹

2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

View full text Add to dashboard Cite

With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of serious cognitive diseases like Mild Cognitive Impairment. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This thesis presents several contributions on this topic. The major ones are two novel hybrid ADLs recognition algorithms. The former is supervised while the latter is unsupervised. Preliminary results, which still need to be confirmed, show that the recognition rate of the unsupervised method is comparable to the one obtained by the supervised one, with the great advantage of not requiring the acquisition of an annotated dataset. Beyond ADLs recognition, other contributions on smart sensing and anomaly recognition are presented. Regarding unobtrusive sensing, we propose a machine learning technique to detect fine-grained manipulations performed by the inhabitant on household objects instrumented with tiny accelerometer sensors. Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors is presented. Experimental results on several datasets show the effectiveness of all the proposed techniques.ii

show abstract

“…That technique is coupled with an activity recognition module and with visualization tools to allow practitioners inspecting the trend of activity patterns. Visualization of spatio-temporal data extracted from the long-term observation of elderly's activities at home is used in [29] to identify potential risk situations. In our work, we also aim at monitoring the elderly's behavior on the long term.…”

Section: Long-term Analysis Of Activity Datamentioning

confidence: 99%

SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment

Riboni

Bettini

Civitarese

et al. 2016

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates.

show abstract

Spatiotemporal data visualisation for homecare monitoring of elderly people

Cited by 24 publications

References 25 publications

Intelligent healthcare informatics in big data era

Intelligent healthcare informatics in big data era

Human Activity Recognition in Smart-Home Environments for Health-Care Applications

SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment

Contact Info

Product

Resources

About