A self-supervised algorithm to detect signs of social isolation in the elderly from daily activity sequences

Prenkaj, Bardh; Aragona, Dario; Alessandro, Flaborea,; Galasso, Fabio; Gravina, Saverio; Podo, Luca; Reda, Emilia; Velardi, Paola

doi:10.1016/j.artmed.2022.102454

“…When it comes to spotting outliers, our approach achieves an optimal sensitivity of 0.88 and an overall precision of 0.95. Prenkaj et al [22] as HypAD, and it is based on Anomaly Detection. HypAD utilises hyperbolic neural networks to provide end-to-end uncertainty estimation and incorporates this into the "traditional" idea of reconstruction loss in anomaly detection.…”

Section: Related Workmentioning

confidence: 99%

“…Elite l − R L * . gazelle l (22) For the gazelle's top speed, S, the Lévy distribution notation for a vector of random values is used, R L . Equation 23 depicts the exact perfect for the behaviour of the predator pursuing the gazelle.…”

Section: Explorationmentioning

confidence: 99%

Daily Activities of Elder Adults Using Optimized Deep Learning Model in China

Yang,

Rosni,

Binti Zainol

et al. 2023

Int. j. commun. netw. inf. secur.

0

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The health and happiness of seniors can be influenced by the design of public spaces and buildings. Planning for age-friendly communities requires taking into account the wide variety of daily activities and public facility consumption among older persons. Yet, conventional approaches fall short when it comes to providing comprehensive, objective measures of the actions of the elderly. This research proposes an attention-based recognition approach by decomposing neural network outputs into class-dependent features using dictionary learning to improve discrimination performance. This research aimed to develop an empirical typology of activity of daily living (ADL) and its connection to health in China's ageing population. A deep learning-based model was rummaged to categorise the contributors in the Chinese Longitudinal Healthy Longevity Study (CLHLS) into subgroups based on their abilities with ADL. To be more precise, the study trains a category-based recognition network (CRN) by superimposing a basic but effective specific recognition encoding (SRE) unit on top of convolutional layers. In order to encode attention maps for each class, the SRE module uses the feature maps produced by the Convolutional Neural Networks (CNN) to learn a class-specific vocabulary. Class-wise adaptive feature refining is achieved by multiplying these attention maps by the input features. In this case, a gazelle optimization algorithm (GOA) handles the hyper-parameter tuning process of the proposed model. This case study is the first of its kind in a dense metropolitan region, and it uses objective measurements to evaluate the activity spaces of the elderly. This research provides empirical data for the promotion of healthy ageing in urban areas.

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“…On the other end of the spectrum of explainability, we find inherently interpretable white-box prediction models (Loyola-González 2019), which are preferred for decisionmaking purposes (Verenich et al 2019). Alas, black-box models demonstrate superior performance and generalisation capabilities when dealing with high-dimensional data (Aragona et al 2021;Ding et al 2019;Feng, Tang, and Liu 2019;Huang et al 2020;Madeddu, Stilo, and Velardi 2020;Prenkaj et al 2021Prenkaj et al , 2020Prenkaj et al , 2023aVerma, Mandal, and Gupta 2022;Wang, Yu, and Miao 2017).…”

Section: Introductionmentioning

confidence: 95%

Developing and Evaluating Graph Counterfactual Explanation with GRETEL

Prado-Romero

¹

,

Prenkaj

²

,

Stilo

³

2023

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

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Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI systems. While extensively researched in domains such as medical imaging and autonomous vehicles, Graph Counterfactual Explanation (GCE) methods have been comparatively under-explored. GCEs generate a new graph similar to the original one, with a different outcome grounded on the underlying predictive model. Among these GCE techniques, those rooted in generative mechanisms have received relatively limited investigation despite demonstrating impressive accomplishments in other domains, such as artistic styles and natural language modelling. The preference for generative explainers stems from their capacity to generate counterfactual instances during inference, leveraging autonomously acquired perturbations of the input graph. Motivated by the rationales above, our study introduces RSGG-CE, a novel Robust Stochastic Graph Generator for Counterfactual Explanations able to produce counterfactual examples from the learned latent space considering a partially ordered generation sequence. Furthermore, we undertake quantitative and qualitative analyses to compare RSGG-CE's performance against SoA generative explainers, highlighting its increased ability to engendering plausible counterfactual candidates.

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“…On the other end of the spectrum of explainability, we find inherently interpretable white-box prediction models (Loyola-González 2019), which are preferred for decisionmaking purposes (Verenich et al 2019). Alas, black-box models demonstrate superior performance and generalisation capabilities when dealing with high-dimensional data (Aragona et al 2021;Ding et al 2019;Feng, Tang, and Liu 2019;Huang et al 2020;Madeddu, Stilo, and Velardi 2020;Prenkaj et al 2021Prenkaj et al , 2020Prenkaj et al , 2023aVerma, Mandal, and Gupta 2022;Wang, Yu, and Miao 2017).…”

Section: Introductionmentioning

confidence: 99%

Robust Stochastic Graph Generator for Counterfactual Explanations

Prado-Romero,

Prenkaj,

Stilo

2024

AAAI

1

0

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Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI systems. While extensively researched in domains such as medical imaging and autonomous vehicles, Graph Counterfactual Explanation (GCE) methods have been comparatively under-explored. GCEs generate a new graph similar to the original one, with a different outcome grounded on the underlying predictive model. Among these GCE techniques, those rooted in generative mechanisms have received relatively limited investigation despite demonstrating impressive accomplishments in other domains, such as artistic styles and natural language modelling. The preference for generative explainers stems from their capacity to generate counterfactual instances during inference, leveraging autonomously acquired perturbations of the input graph. Motivated by the rationales above, our study introduces RSGG-CE, a novel Robust Stochastic Graph Generator for Counterfactual Explanations able to produce counterfactual examples from the learned latent space considering a partially ordered generation sequence. Furthermore, we undertake quantitative and qualitative analyses to compare RSGG-CE's performance against SoA generative explainers, highlighting its increased ability to engendering plausible counterfactual candidates.

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A self-supervised algorithm to detect signs of social isolation in the elderly from daily activity sequences

Cited by 13 publications

References 28 publications

Daily Activities of Elder Adults Using Optimized Deep Learning Model in China

Daily Activities of Elder Adults Using Optimized Deep Learning Model in China

Developing and Evaluating Graph Counterfactual Explanation with GRETEL

Robust Stochastic Graph Generator for Counterfactual Explanations

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