5 research outputs found
Modélisation et mise en place d’une assistance cognitive personnalisée pour la qualité du sommeil et la déambulation nocturne
Les troubles du sommeil des personnes âgées atteintes de maladie neurodégénérative
entraine des comportements d’errance nocturne qui diminuent leur qualité de vie et
peuvent avoir des conséquences sur leur sécurité et sur les autres personnes vivant dans
les résidences de type EHPAD. Ce projet réalisé en collaboration entre le laboratoire
DOMUS et le CENTICH, Angers, France, a pour but d’explorer des solutions
technologiques pour accompagner les personnes atteintes d’errance nocturne. Les
solutions s’harmonisent avec les objets connectés déjà installés à la résidence Les
Noisetiers d’Angers. Construits à partir des données recueillies dans la résidence, deux
ensembles de scénarios sont proposés, l’un pour assister l’errance nocturne à l’intérieur
des chambres des résidents, l’autre lorsque les résidents sortent de leur chambre la nuit.
Ces scénarios sont implantés sur un intergiciel (middleware) et respectent le protocole
de communication KNX qui est utilisé en France
Unsupervised monitoring of an elderly person\u27s activities of daily living using Kinect sensors and a power meter
The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored.
Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly.
To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel.
A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value.
Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns.
As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours.
The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs
Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living
Following the recent advances in technology and the growing use of mobile devices such as
smartphones, several solutions may be developed to improve the quality of life of users in the
context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g.,
accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS)
receiver, which allow the acquisition of physical and physiological parameters for the
recognition of different Activities of Daily Living (ADL) and the environments in which they are
performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare
tasks, based on the types of skills that people usually learn in early childhood, including
feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping,
watching TV, working, listening to music, cooking, eating and others. On the context of AAL,
some individuals (henceforth called user or users) need particular assistance, either because
the user has some sort of impairment, or because the user is old, or simply because users
need/want to monitor their lifestyle. The research and development of systems that provide a
particular assistance to people is increasing in many areas of application. In particular, in the
future, the recognition of ADL will be an important element for the development of a personal
digital life coach, providing assistance to different types of users. To support the recognition
of ADL, the surrounding environments should be also recognized to increase the reliability of
these systems.
The main focus of this Thesis is the research on methods for the fusion and classification of the
data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL
in almost real-time, taking into account the large diversity of the capabilities and
characteristics of the mobile devices available in the market. In order to achieve this objective,
this Thesis started with the review of the existing methods and technologies to define the
architecture and modules of the method for the identification of ADL. With this review and
based on the knowledge acquired about the sensors available in off-the-shelf mobile devices,
a set of tasks that may be reliably identified was defined as a basis for the remaining research
and development to be carried out in this Thesis. This review also identified the main stages
for the development of a new method for the identification of the ADL using the sensors
available in off-the-shelf mobile devices; these stages are data acquisition, data processing,
data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One
of the challenges is related to the different types of data acquired from the different sensors,
but other challenges were found, including the presence of environmental noise, the positioning
of the mobile device during the daily activities, the limited capabilities of the mobile devices
and others. Based on the acquired data, the processing was performed, implementing data
cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of
the research their implementation does not have influence in the results of the identification
of the ADL and environments, as the features are extracted from a set of data acquired during
a defined time interval and there are no missing values during this stage. The joint selection of
the set of usable sensors and the identifiable set of tasks will then allow the development of a
framework that, considering multi-sensor data fusion technologies and context awareness, in
coordination with other information available from the user context, such as his/her agenda
and the time of the day, will allow to establish a profile of the tasks that the user performs in
a regular activity day. The classification method and the algorithm for the fusion of the features
for the recognition of ADL and its environments needs to be deployed in a machine with some
computational power, while the mobile device that will use the created framework, can
perform the identification of the ADL using a much less computational power. Based on the
results reported in the literature, the method chosen for the recognition of the ADL is composed
by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron
(MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural
Networks (DNN).
Data acquisition can be performed with standard methods. After the acquisition, the data must
be processed at the data processing stage, which includes data cleaning and feature extraction
methods. The data cleaning method used for motion and magnetic sensors is the low pass filter,
in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform
(FFT) was applied to extract the different frequencies. When the data is clean, several features
are then extracted based on the types of sensors used, including the mean, standard deviation,
variance, maximum value, minimum value and median of raw data acquired from the motion
and magnetic sensors; the mean, standard deviation, variance and median of the maximum
peaks calculated with the raw data acquired from the motion and magnetic sensors; the five
greatest distances between the maximum peaks calculated with the raw data acquired from
the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel-
Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw
data acquired from the microphone data; and the distance travelled calculated with the data
acquired from the GPS receiver. After the extraction of the features, these will be grouped in
different datasets for the application of the ANN methods and to discover the method and
dataset that reports better results. The classification stage was incrementally developed,
starting with the identification of the most common ADL (i.e., walking, running, going upstairs,
going downstairs and standing activities) with motion and magnetic sensors. Next, the
environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen,
living room, hall, street and library. After the environments are recognized, and based on the
different sets of sensors commonly available in the mobile devices, the data acquired from the
motion and magnetic sensors were combined with the recognized environment in order to
differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled,
extracted from the GPS receiver data, allowing also to recognize the driving activity.
After the implementation of the three classification methods with different numbers of
iterations, datasets and remaining configurations in a machine with high processing
capabilities, the reported results proved that the best method for the recognition of the most
common ADL and activities without motion is the DNN method, but the best method for the
recognition of environments is the FNN method with Backpropagation. Depending on the
number of sensors used, this implementation reports a mean accuracy between 85.89% and
89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of
environments, and equals to 100% for the recognition of activities without motion, reporting
an overall accuracy between 85.89% and 92.00%.
The last stage of this research work was the implementation of the structured framework for
the mobile devices, verifying that the FNN method requires a high processing power for the
recognition of environments and the results reported with the mobile application are lower
than the results reported with the machine with high processing capabilities used. Thus, the
DNN method was also implemented for the recognition of the environments with the mobile
devices. Finally, the results reported with the mobile devices show an accuracy between 86.39%
and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition
of environments, and equal to 100% for the recognition of activities without motion, reporting
an overall accuracy between 58.02% and 89.15%.
Compared with the literature, the results returned by the implemented framework show only
a residual improvement. However, the results reported in this research work comprehend the
identification of more ADL than the ones described in other studies. The improvement in the
recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum
number of ADL and environments previously recognized was 13, while the number of ADL and
environments recognized with the framework resulting from this research is 16. In conclusion,
the framework developed has a mean improvement of 2.93% in the accuracy of the recognition
for a larger number of ADL and environments than previously reported.
In the future, the achievements reported by this PhD research may be considered as a start
point of the development of a personal digital life coach, but the number of ADL and
environments recognized by the framework should be increased and the experiments should be
performed with different types of devices (i.e., smartphones and smartwatches), and the data
imputation and other machine learning methods should be explored in order to attempt to
increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por
exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade
de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted
Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro,
giroscĂłpio, magnetĂłmetro, microfone e recetor de Sistema de Posicionamento Global (GPS),
que permitem a aquisição de vários parâmetros fĂsicos e fisiolĂłgicos para o reconhecimento de
diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um
conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos
de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem
alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir
escadas, dormir, ver televisĂŁo, trabalhar, ouvir mĂşsica, cozinhar, comer, entre outras. No
contexto de AVA, alguns indivĂduos (comumente chamados de utilizadores) precisam de
assistĂŞncia particular, seja porque o utilizador tem algum tipo de deficiĂŞncia, seja porque Ă©
idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de
vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência
particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o
reconhecimento das AVD Ă© uma parte importante para o desenvolvimento de um assistente
pessoal digital, fornecendo uma assistĂŞncia pessoal de baixo custo aos diferentes tipos de
pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se
desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas.
O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos
dados adquiridos a partir dos sensores disponĂveis nos dispositivos mĂłveis, para o
reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade
das caracterĂsticas dos dispositivos mĂłveis disponĂveis no mercado. Para atingir este objetivo,
esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a
arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da
literatura e com base no conhecimento adquirido sobre os sensores disponĂveis nos dispositivos
mĂłveis disponĂveis no mercado, um conjunto de tarefas que podem ser identificadas foi
definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os
principais conceitos para o desenvolvimento do novo método de identificação das AVD,
utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de
dados, imputação de dados, extração de caracterĂsticas, fusĂŁo de dados e extração de
resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado
aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram
encontrados, sendo os mais relevantes o ruĂdo ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos mĂłveis.
As diferentes caracterĂsticas das pessoas podem igualmente influenciar a criação dos mĂ©todos,
escolhendo pessoas com diferentes estilos de vida e caracterĂsticas fĂsicas para a aquisição e
identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos,
realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a
extração de caracterĂsticas, para iniciar a criação do novo mĂ©todo para o reconhecimento das
AVD. Os mĂ©todos de imputação de dados foram excluĂdos da implementação, pois nĂŁo iriam
influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são
utilizadas as caracterĂsticas extraĂdas de um conjunto de dados adquiridos durante um intervalo
de tempo definido.
A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o
desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados
adquiridos com múltiplos sensores em coordenação com outras informações relativas ao
contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de
tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o
método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes
Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural
Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, apĂłs a
criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a
implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para
testes adicionais.
Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando
dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos
comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de
processamento de dados, que inclui os métodos de correção de dados e de extração de
caracterĂsticas. O mĂ©todo de correção de dados utilizado para sensores de movimento e
magnĂ©ticos Ă© o filtro passa-baixo de modo a reduzir o ruĂdo, mas para os dados acĂşsticos, a
Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências.
ApĂłs a correção dos dados, as diferentes caracterĂsticas foram extraĂdas com base nos tipos de
sensores usados, sendo a mĂ©dia, desvio padrĂŁo, variância, valor máximo, valor mĂnimo e
mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio
padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos
sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos
calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio
padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas
com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os
dados adquiridos pelo recetor de GPS. ApĂłs a extração das caracterĂsticas, as mesmas sĂŁo agrupadas em diferentes conjuntos de dados
para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de
caracterĂsticas que reporta melhores resultados. O mĂłdulo de classificação de dados foi
incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores
magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em
seguida, os ambientes sĂŁo identificados com dados de sensores acĂşsticos, i.e., quarto, bar, sala
de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes
reconhecidos e os restantes sensores disponĂveis nos dispositivos mĂłveis, os dados adquiridos
dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para
diferenciar algumas atividades sem movimento (i.e., dormir e ver televisĂŁo), onde o nĂşmero
de atividades reconhecidas nesta fase aumenta com a fusĂŁo da distância percorrida, extraĂda
a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir.
Após a implementação dos três métodos de classificação com diferentes números de iterações,
conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os
resultados relatados provaram que o melhor método para o reconhecimento das atividades
comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o
reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número
de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51%
para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes,
e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidĂŁo
global entre 85,89% e 92,00%.
A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando
que o método FNN requer um alto poder de processamento para o reconhecimento de
ambientes e os resultados reportados com estes dispositivos sĂŁo inferiores aos resultados
reportados com a máquina com alta capacidade de processamento utilizada no
desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o
reconhecimento dos ambientes com os dispositivos mĂłveis. Finalmente, os resultados relatados
com os dispositivos mĂłveis reportam uma exatidĂŁo entre 86,39% e 89,15% para o
reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual
a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidĂŁo geral
entre 58,02% e 89,15%.
Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram
uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais
estudos disponĂveis na literatura. A melhoria no reconhecimento das AVD com base na mĂ©dia
das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos
estudos disponĂveis na literatura Ă© 13, enquanto o nĂşmero de AVD e ambientes reconhecidos
com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93%
na exatidĂŁo do reconhecimento num maior nĂşmero de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto
de partida para o desenvolvimento de um assistente digital pessoal, mas o nĂşmero de ADL e
ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser
repetidas com diferentes tipos de dispositivos mĂłveis (i.e., smartphones e smartwatches), e os
métodos de imputação e outros métodos de classificação de dados devem ser explorados de
modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e
ambientes
L’analyse du langage spontané comme outil de détection précoce du déclin cognitif : une approche écologique
La maladie d'Alzheimer (MA) – la forme la plus courante de trouble neurocognitif majeur – se caractérise typiquement par des troubles progressifs et insidieux de la mémoire épisodique. Des déficits langagiers font également partie du portrait clinique de la maladie, et sont déjà présents au stade préclinique du trouble neurocognitif léger (TNCL). Des difficultés sur le plan de la production du langage ont été rapportées dans la MA et même le TNCL, ce qui suggère que son évaluation pourrait représenter une opportunité unique de détection précoce du déclin cognitif. Un consensus croissant propose d’ailleurs que le langage spontané (LS) pourrait permettre une évaluation écologiquement valide des capacités de production langagière. Toutefois, les résultats d’études s’étant penchées sur l’évaluation du LS ne convergent pas tous pour dresser un portrait clair de l’impact du déclin cognitif sur la production langagière dans la MA, et moindrement encore dans le TNCL.
La première partie de la thèse visait ainsi à décrire de façon exhaustive l’étendue de la recherche dans le domaine de l'évaluation du LS dans les populations MA et TNCL, en réalisant un examen de la portée (étude 1). Les résultats ont révélé que l’évaluation traditionnelle du LS consistait le plus souvent en une analyse quantitative d’une sélection de variables microlinguistiques de LS obtenu à l’aide d’une mesure descriptive standardisée. Ayant répliqué le patron des déficits langagiers largement répandu dans les écrits scientifiques, les résultats de l’examen de la portée soulignent l’apport complémentaire de l’évaluation du LS à l’évaluation globale du langage dans les populations MA et TNCL. Toutefois, l’examen de la portée a également souligné d’importantes lacunes dans le domaine de recherche, notamment le très peu d’études s’étant intéressées au TNCL comparativement à la MA, ainsi que le très peu d’approches écologiques à l’évaluation du LS.
Prenant en compte ces lacunes, la deuxième partie de la thèse visait à examiner l’apport d’une évaluation écologique du LS auprès de participants TNCL et de contrôles, dans un contexte expérimental se rapprochant de la vraie vie (étude 2). Plus précisément, une évaluation fonctionnelle des actes de langage produits par ces deux groupes lors de la réalisation, dans un appartement-test, de tâches écologiques inspirées d'activités de la vie quotidienne a été réalisée. La description qualitative des actes de langage spontanément produits pendant la planification et l'exécution de ces tâches complexes a permis d'extraire des stratégies, des barrières et des réactions distinctes en réponse aux demandes des tâches ainsi qu'aux difficultés rencontrées chez les participants TNCL et contrôles. Ainsi, les résultats ont montré que les participants TNCL mettaient en place moins de stratégies proactives avant d’entamer l’expérimentation, puis davantage de stratégies compensatoires pour supporter leur organisation des tâches pendant leur exécution. Plus distraits et moins portés à tenir compte de l’assistance offerte, ils validaient et justifiaient davantage leur performance de façon défensive et étaient plus réactifs à leurs difficultés que les sujets contrôles. Les résultats de la deuxième étude de la thèse soulignent ainsi l’apport novateur d’une évaluation fonctionnelle du LS comme outil d'exploration de l'impact du déclin cognitif lors de tâches écologiques complexes se rapprochant d'activités de la vie quotidienne.
Ensemble, les études de la thèse convergent pour appuyer l’apport complémentaire d'une évaluation fonctionnelle du LS à son évaluation traditionnelle dans l’avancement des connaissances au sujet de l’impact du déclin cognitif dans les populations TNCL et MA sur la production langagière.Alzheimer's disease (AD) – the most common form of major neurocognitive disorder – is typically characterized by progressive and insidious impairment of episodic memory. Language deficits are also part of the clinical picture of the disease, and are already present in the preclinical stage of mild neurocognitive disorder (mild NCD). Difficulties in language production have been reported in AD and even in mild NCD, suggesting that its assessment may represent a unique opportunity for early detection of cognitive decline. There is a growing consensus that connected speech (CS) may provide an ecologically valid assessment of language production abilities. However, the results of studies that have examined CS assessment do not all converge to provide a clear picture of the impact of cognitive decline on language production in AD, and even less so in mild NCD.
The first part of the thesis thus aimed to comprehensively describe the extent of research in the area of CS assessment in AD and mild NCD populations, by conducting a scoping review (study 1). The results revealed that traditional CS assessment most often consisted of quantitative analysis of a selection of microlinguistic variables of CS, obtained using a standardized descriptive measure. Having replicated the pattern of language deficits widely found in the scientific literature, the results of the scoping review highlight the complementary contribution of CS assessment to the overall assessment of language in AD and mild NCD populations. However, the scoping review also highlighted important gaps in the research field, including the very few studies that have focused on mild NCD in comparison to AD, as well as the very few ecological approaches to CS assessment.
Taking these gaps into account, the second part of the thesis thus aimed to examine the contribution of a functional assessment of CS that is closer to the context of real life, with mild NCD participants and controls (study #2). More precisely, a functional assessment of the speech acts produced by these two groups during the performance of ecological tasks inspired by activities of daily living in a laboratory-apartment was carried out. Qualitative description of the speech acts spontaneously produced by these participants while performing complex tasks allowed for the extraction of distinct strategies, barriers and reactions in response to task demands as well as to the difficulties encountered by the mild NCD participants and controls. Thus, results showed that mild NCD participants implemented fewer proactive strategies before beginning the experiment, and then more compensatory strategies to support their task organization during task execution. More distracted and less likely to take into account the assistance offered, they validated and justified their performance more defensively and were more reactive to their difficulties than the control subjects. The results of the second article of the thesis thus highlight the innovative contribution of a functional assessment of CS as a tool for exploring the impact of cognitive decline in complex, ecological tasks that are similar to activities of daily living.
Together, the studies in this thesis converge to support the complementary contribution of a functional assessment of CS to its traditional assessment in advancing knowledge about the impact of cognitive decline on language production in the mild NCD and AD populations
Proceedings of the ECMLPKDD 2015 Doctoral Consortium
ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium