@conference {xCaros19, title = {Automatic Reminiscence Therapy for Dementia}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)}, year = {2020}, month = {06/2020}, publisher = {ACM}, organization = {ACM}, address = {Dublin, Ireland}, abstract = {

With people living longer than ever, the number of cases with dementia such as Alzheimer{\textquoteright}s disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. (demo paper)

Automatic Reminiscence Therapy for Dementia from Universitat Polit{\`e}cnica de Catalunya



}, doi = {https://doi.org/10.1145/3372278.3391927}, url = {https://arxiv.org/abs/1910.11949}, author = {Caros, Mariona and Garolera, Maite and Radeva, Petia and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCaros, title = {A Generative Dialogue System for Reminiscence Therapy}, year = {2019}, abstract = {

With people living longer than ever, the number of cases with neurodegenerative diseases such as Alzheimer{\textquoteright}s or cognitive impairment increases steadily. In Spain it affects more than 1.2 million patients and it is estimated that in 2050 more than 100 million people will be affected. While there are not effective treatments for this terminal disease, therapies such as reminiscence, that stimulate memories of the patient{\textquoteright}s past are recommended, as they encourage the communication and produce mental and emotional benefits on the patient. Currently, reminiscence therapy takes place in hospitals or residences, where the therapists are located. Since people that receive this therapy are old and may have mobility difficulties, we present an AI solution to guide older adults through reminiscence sessions by using their laptop or smartphone.\ 

Our solution consists in a generative dialogue system composed of two deep learning architectures to recognize image and text content. An Encoder-Decoder with Attention is trained to generate questions from photos provided by the user, which is composed of a pretrained Convolution Neural Network to encode the picture, and a Long Short-Term Memory to decode the image features and generate the question. The second architecture is a sequence-to-sequence model that provides feedback to engage the user in the conversation.

Thanks to the experiments, we realise that we obtain the best performance by training the dialogue model with Persona-Dataset and fine-tuning it with Cornell Movie-Dialogues dataset. Finally, we integrate Telegram as the interface for the user to interact with Elisabot, our trained conversational agent.

}, author = {Caros, Mariona}, editor = {Radeva, Petia and Xavier Gir{\'o}-i-Nieto} } @article {aDimiccoli18, title = {Introduction to the special issue: Egocentric Vision and Lifelogging}, journal = {Journal of Visual Communication and Image Representation}, year = {2018}, month = {06/2018}, abstract = {

}, doi = {10.1016/j.jvcir.2018.06.010}, url = {https://www.sciencedirect.com/science/article/pii/S1047320318301354}, author = {Dimiccoli, M. and Gurrin, Cathal and Crandall, David and Xavier Gir{\'o}-i-Nieto and Radeva, Petia} } @conference {cGurrina, title = {LTA 2017: The Second Workshop on Lifelogging Tools and Applications}, booktitle = {ACM Multimedia}, year = {2017}, month = {10/2017}, publisher = {ACM}, organization = {ACM}, address = {Mountain View, California USA}, abstract = {

The organisation of personal data is receiving increasing research attention due to the challenges we face in gathering, enriching, searching, and visualising such data. \ Given the increasing ease with which personal data being gathered by individuals, the concept of a lifelog digital library of rich multimedia and sensory content for every individual is fast becoming a reality. \ The LTA~2017 workshop aims to bring together academics and practitioners to discuss approaches to lifelog data analytics and applications; and to debate the opportunities and challenges for researchers in this new and challenging area.

}, doi = {10.1145/3123266.3132050}, author = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Dimiccoli, M. and Dang-Nguyen, Duc-Tien and Joho, H.} } @conference {xLidona, title = {Semantic Summarization of Egocentric Photo Stream Events}, booktitle = {ACM Multimedia 2017 Workshop on Lifelogging Tools and Applications}, year = {2017}, month = {10/2017}, publisher = {ACM}, organization = {ACM}, address = {Mountain View, CA, USA}, abstract = {

With the rapid increase of users of wearable cameras in recent years and of the amount of data they produce, there is a strong need for automatic retrieval and summarization techniques. This work addresses the problem of automatically summarizing egocentric photo streams captured through a wearable camera by taking an image retrieval perspective. After removing non-informative images by a new CNN-based filter, \ images are ranked by relevance to ensure semantic diversity and \ finally re-ranked by a novelty criterion to reduce redundancy. \ To assess the results, a new evaluation metric is proposed which takes into account the non-uniqueness of the solution. Experimental results applied on a database of 7,110 images from 6 different subjects and evaluated by experts gave 95.74\% of experts satisfaction and a Mean Opinion Score of 4.57 out of 5.0.

}, doi = {10.1145/3133202.3133204}, url = {https://dl.acm.org/citation.cfm?id=3133204}, author = {Lidon, Aniol and Bola{\~n}os, Marc and Dimiccoli, M. and Radeva, Petia and Garolera, Maite and Xavier Gir{\'o}-i-Nieto} } @conference {cdeOliveira-Barra, title = {LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task}, booktitle = {The 12th NTCIR Conference, Evaluation of Information Access Technologies}, year = {2016}, month = {06/2016}, publisher = {National Institute of Informatics (NII)}, organization = {National Institute of Informatics (NII)}, address = {Tokyo, Japan}, abstract = {

Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. \ LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. \ Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.

LEMoRe - A Lifelog Engine for Moments Retrieval at NTCIR-12 from University of Barcelona
}, url = {http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings12/NTCIR/toc_ntcir.html$\#$Lifelog}, author = {Gabriel de Oliveira-Barra and Xavier Gir{\'o}-i-Nieto and Cartas-Ayala, Alejandro and Radeva, Petia} } @conference {cGurrin, title = {LTA 2016 - The First Workshop on Lifelogging Tools and Applications}, booktitle = {ACM Multimedia}, year = {2016}, month = {10/2016}, publisher = {ACM}, organization = {ACM}, address = {Amsterdam, The Netherlands}, abstract = {

The organisation of personal data is receiving increasing research attention due to the challenges that are faced in gathering, enriching, searching and visualising this data. Given the increasing quantities of personal data being gathered by individuals, the concept of a lifelog digital library of rich multimedia and sensory content for every individual is fast becoming a reality. The LTA2016 lifelogging workshop at ACM MM 2016 aims to bring together academics and practitioners to discuss approaches to lifelog data analytics and the applications of same, and to debate the opportunities and challenges for researchers in this new and challenging area.

[Workshop web page]

[Workshop proceedings]

[UPCommons]

}, keywords = {lifelogging, Personal digital archives, Personal information management}, doi = {http://dx.doi.org/10.1145/2964284.2980534}, url = {http://lta2016.computing.dcu.ie/}, author = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Dimiccoli, M. and Johansen, H. and Joho, H. and Singh, Vivek K} } @mastersthesis {xLidon, title = {Semantic and Diverse Summarization of Egocentric Photo Events}, year = {2015}, abstract = {

Student: Aniol Lidon

Advisors: Petia Radeva (UB) and Xavier Gir{\'o}-i-Nieto (UPC)

Program: Master in Computer Vision

Grade: A (9.8/10.0)

This project generates visual summaries of events depicted from egocentric photos taken with a wearable camera. These summaries are addressed to mild-dementia patients in order to exercise their memory in a daily base. The main contribution is an iterative approach that guarantees the semantic diversity of the summary and a novel soft metric to assess subjective results. Medical experts validated the proposed solution with a Mean Opinion Score of 4.6 out of of 5.0. The flexibility and quality of the solution was also tested in the 2015 Retrieving Diverse Social Images Task from the scientific international benchmark, MediaEval.\ 

Semantic and Diverse Summarization of Egocentric Photo Events from Xavier Giro
Aniol Lidon Master Thesis
}, author = {Lidon, Aniol}, editor = {Radeva, Petia and Xavier Gir{\'o}-i-Nieto} } @conference {cLidon, title = {UPC-UB-STP @ MediaEval 2015 Diversity Task: Iterative Reranking of Relevant Images}, booktitle = {MediaEval 2015 Workshop}, year = {2015}, month = {09/2015}, abstract = {

This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task.The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity.

UPC-UB-STP @ MediaEval 2015 diversity task iterative reranking of relevant images from Xavier Giro
}, url = {http://ceur-ws.org/Vol-1436/}, author = {Lidon, Aniol and Bola{\~n}os, Marc and Seidl, Markus and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Zeppelzauer, Matthias} } @conference {cMestre, title = {Visual Summary of Egocentric Photostreams by Representative Keyframes}, booktitle = {IEEE International Workshop on Wearable and Ego-vision Systems for Augmented Experience (WEsAX) 2015}, year = {2015}, month = {07/2015}, address = {Turin, Italy}, abstract = {

Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement).\ In this paper, we propose a new summarization method based on keyframes selection that\ uses visual features extracted by means of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the summaries.

}, keywords = {egocentric, keyframes, lifelogging, summarization}, url = {http://arxiv.org/abs/1505.01130}, author = {Bola{\~n}os, Marc and Mestre, Ricard and Talavera, Estefan{\'\i}a and Xavier Gir{\'o}-i-Nieto and Radeva, Petia} }