@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} } @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} }