Dissertations / Theses on the topic 'Detection and recognition of activities of daily living'
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Peng, Yingli. "Improvement of Data Mining Methods on Falling Detection and Daily Activities Recognition." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25521.
Full textViard, Kévin. "Modelling and Recognition of Human Activities of Daily Living in a Smart Home." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLN022/document.
Full textMost of the work done in the field of ambient assisted living (AAL) is based on the use of visual and audio sensors such as cameras. However, these sensors are often rejected by the patient because of their invasiveness. Alternative approaches require the use of sensors embedded on the person (GPS, electronic wristbands or RFID chips in clothing ...), and their relevance is therefore reduced to the assumption that people actually wear them, without rejecting nor forgetting them. For these reasons, in this thesis, we find more relevant the approaches based on the use of binary sensors integrated into the habitat only, such as motion detectors, sensory mats or optical barriers. In such a technological context, it becomes interesting to use paradigms, models and tools of Discrete Event Systems (DES), initially developed for modeling, analysis and control of complex industrial systems. In this thesis work, the goal is to build an activity of daily living modeling and monitoring approach, based on the models and the paradigm of the DES and answering a problem that is expressed as follows:The objective is to develop a global framework to discover and recognise activities of daily living of an inhabitant living alone in a smart home. This smart home have to be equipped with binary sensors only, expert labeling of activities should not be needed and activities can be represented by probabilistic models. The first method presented in this thesis allows to build a probabilistic finite-state automata (PFA) from a learning database and an expert description of the activities to be modeled given by the medical staff. The second method developed during this thesis estimates, according to the observations, the activity performed by the monitored inhabitant. The methods described in this thesis are applied on data generated using an apartment lent by ENS Paris-Saclay and equipped according the experimental needs of this thesis
La maggior parte dei lavori nel settore dell’Ambient Assisted Living (AAL) si basasull’uso di sensori visivi e audio come le telecamere. Tuttavia, questi sensori sonospesso rifiutati dal paziente a causa della loro natura invasiva. Gli approcci alternativi richiedono l’uso di sensori integrati nella persona (GPS, bracciali elettronici o chipRFID...), e la loro rilevanza è quindi ridotta all’ipotesi che le persone li indossino effettivamente, senza mai rifiutarli o dimenticarli.Per questi motivi, in questa tesi, troviamo approcci più rilevanti basati esclusivamente sull’uso di sensori binari integrati nell’habitat, come rilevatori di movimento,tappeti sensoriali o barriere fotoelettriche.In tale contesto tecnologico, diventa interessante utilizzare i paradigmi, i modelli egli strumenti dei sistemi ad eventi discreti (SED), inizialmente sviluppati per la modellazione, l’analisi e il controllo di sistemi industriali complessi.In questo lavoro di tesi, l’obiettivo è quello di presentare un metodo per la modellazione e il monitoraggio delle abitudini di vita, basato sui modelli e paradigmi di SEDe rispondendo ad un problema che si esprime come segue : L’obiettivo è quello di sviluppare un quadro globale per rivelare e riconoscere le attività della vita quotidiana di una persona che abita da sola in una smart home chedovrebbe essere dotata solo di sensori binari. Inoltre si suppone che non sia necessarial’etichettatura delle attività osservate da parte di un esperto e tali attività sono rappresentate da modelli probabilistici.Il primo metodo presentato in questa tesi permette di costruire un modello probabilistico di automa a stati finiti (PFA) ottenuto da un database di apprendimento e unadescrizione delle attività da parte di medici. Il secondo metodo sviluppato in questa tesi stima, alla luce delle osservazioni, qualeattività svolge la persona osservata. I metodi descritti sono illustrati utilizzando dati generati localmente attraverso l’usodi un appartamento messo a disposizione da ENS Paris-Saclay e attrezzato per soddisfarele esigenze sperimentali di questa tesi
Ball, Stephen. "Investigating telemonitoring technologies for the detection of activities and the application of BLE in smart homes for elderly independent living." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/123510/1/Stephen%20Ball%20Thesis.pdf.
Full textTayyub, Jawad. "Hierarchical modelling and recognition of activities of daily living." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/22186/.
Full textBalasubramanian, Koushik. "Perception Framework for Activities of Daily Living Manipulation Tasks." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/450.
Full textUitto, T. (Teemu). "Detection and recognition of daily activities by utilizing novel technologies." Master's thesis, University of Oulu, 2017. http://urn.fi/URN:NBN:fi:oulu-201711093088.
Full textOpinnäytetyössä tutkittiin ulko- ja sisäpaikannusteknologioita, sekä yhdessä niiden kanssa käytettäviä kannettavia ja/tai puettavia liikesensoreita, joilla voidaan havaita ja tunnistaa päivittäisiä aktiviteettejä. Ikääntyvien ihmisten hoidossa on erinomaisen tärkeää pystyä havaitsemaan muutoksia fyysisessä, psykososiaalisessa sekä kognitiivisessa toimintakyvyssä. Tässä työssä käsiteltiin älykkäitä järjestelmiä ikääntymisestä aiheutuneiden muutosten arvioimiseksi. Uusia teknologioita ja menetelmiä hyödyntämällä voidaan parantaa kotihoitopalvelujen tehokkuutta. Työssä tutkittu konsepti mahdollistaa toimintakyvyn muutosten, sekä päivittäisen suorituskyvyn muutosten varhaisen tunnistamisen. Tutkimusmenetelminä käytettiin sekä haastatteluja että kirjallisuustutkimuksia. Teknologiatutkimus suoritettiin kirjallisuustutkimuksena eri lähteistä, kun taas konseptointi ja teknologioiden valinta suoritettiin haastattelemalla 5GTN allianssiin kuuluvia moniammatillisia asiantuntijoita. Tutkimuksen tuloksena valittiin käytettävät teknologiat ulko- ja sisäpaikannusmenetelmiin, sekä valittiin sensorityypit päivittäisten toimintojen ja rutiinien reaaliaikaiseen seurantaan. Syksyllä 2017 on käynnistymässä pilottiprojekti, jossa sisätila-antureiden avulla seurataan ikäihmisten toimintaa omassa kodissaan. Tuloksia tuosta projektista ei käsitellä tässä opinnäytetyössä, vaan ne käsitellään tulevissa opinnäytteissä
Bouaziz, Ghazi. "Développement et mise en œuvre d'un système de détection de l'isolement social basé sur la reconnaissance des activités en matière de repas et de mobilité chez les personnes âgées à domicile." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES137.
Full textThe recognition of daily life activities has been the subject of research for years to provide effective solutions. It is based on the spatio-temporal analysis of situations and behaviors whose input data is information provided by ambient sensors or by sensors worn by the person. This thesis focuses on the instrumentation of the living space by ambient sensors and on the detection of a state of social isolation in elderly people. Two approaches are used to assess social isolation. The first one is based on questionnaires. The second approach is based on the use of technologies for the objective acquisition of data representative of a state, behavior, etc. In particular, the activity "eating" is linked to a sequence of actions such as shopping, cooking, eating and washing dishes. The activity "moving" is linked to mobility within the home and leaving the home. The literature shows that these two activities seem to be relevant for assessing a potential risk of social isolation among older people. The thesis work focuses on four main contributions: A bibliographic review of ADLs detection research to identify its contributions and limitations, and to outline relevant research directions. Specific criteria were chosen to include articles presenting activity detection systems. A system design approach applied to the detection of ADLs. This approach is part of a system engineering process. It describes the analysis of requirements, their modeling through SysML diagrams and the implementation of a hardware and software architecture based on an IoT network. The analysis of ADLs, in our study, uses data from motion detectors and contact sensors. A display on a web application allows you to visualize the results obtained for the caregiver or the family. The original use of four methods to classify ADLs, namely "preparing the meal", "eating the meal", "washing the dishes", "sleeping/relaxing", "hygiene", "the person outside the home", "a visitor inside the home" and "other activities". The first three methods used are K-means, the Gaussian mixture model and BIRCH, which applies weights to the data. Meal-related activities therefore do not have the same weight as the rest of the data, which made it possible to improve the detection of ADLs. The fourth algorithm is based on a logical method following the determination of a correlation matrix using all the available sensors as input. Using the data from the correlation matrix, the algorithm personalizes the detection of meal-related activities by distinguishing a person preparing their meal from a person using a meal delivery service. We validate our methods by referring to the forms filled in by the participants at the beginning and end of the experiment, in which they describe the course of their typical day. These algorithms were applied to an open annotated database to confirm the accuracy of our approaches. The proposal of a score for the level of social isolation of the person being monitored. This score is based on the identification of activities to extract daily habits through behavioral indicators (time spent outside the house and in the kitchen, etc.). Six elderly people were followed for more than 3 months. The logistic regression algorithm was used to extract the level of social isolation, which was compared with the level of social isolation identified using the "Lubben Social Network Scale" questionnaire, which was completed by each participant at the beginning and end of the study
Li, Yunjie. "Applying Data Mining Techniques on Continuous Sensed Data : For daily living activity recognition." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-23424.
Full textPazhoumand-Dar, Hossein. "Unsupervised monitoring of an elderly person's activities of daily living using Kinect sensors and a power meter." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2017. https://ro.ecu.edu.au/theses/1971.
Full textZaineb, Liouane. "Services e-santé basés sur la reconnaissance et la prédiction des activités quotidiennes dans les espaces intelligents." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S109.
Full textAdvances in sensor technology and their availability have measured various properties and activities of residents in a smart home. However, obtaining significant knowledge from a large amount of information collected from a sensor network is not a simple task. Due to the complexity of the behavior of the inhabitants, the extraction of meaningful information and the accurate prediction of values representing the future activities of an occupant are research challenges. The main objective of our thesis work is to ensure an efficient analysis of data collected from occupancy sensors in a smart home. In this regard, this work is based on the recognition and evaluation of the daily activities of an elderly person in order to observe, predict and monitor the evolution of his state of dependence, health and to detect by the same occasion, the presence of a loss or a disruption of autonomy in real time
El, Khadiri Yassine. "Inférence bayésienne pour la détection des activités de la vie quotidienne pour faciliter le maintien à domicile des personnes âgées." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0251.
Full textThe increase of the senior population constitutes a major public health issue. The demographic share of the elderly is ever more growing thanks to the progress and advances in medicine and our health care systems. However, with the aging of this population comes a plethora of dependency problems, and this, of course, exponentially.Retirement homes are an expensive and not very popular solution. As a result, we are seeing a surge in home assisted living solutions in the recent years.This topic is in the crossroads between sensor technologies, data transmission, assistance to elderly people and activity monitoring.This thesis explores the application of data analysis algorithms for activity monitoring of elderly people at home. The idea is that with day-to-day monitoring of residents it is possible to infer their autonomy and capacity to perform day-to-day tasks. It also allows caregivers to intervene in cases where the start of some degradation is detected.We explored and adapted some Bayesian inference and time series segmentation methods for activity recognition. And then, we proposed a visualization tool to facilitate the detection of anomalies or changes in everyday habits.This work is part of a CIFRE thesis. The methods and algorithms presented have been put into production and are packaged into Diatelic's the assisted living commercial solution
Liao, Chun-Hao, and 廖俊豪. "Modeling and Recognition of Activities of Daily Living." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/69362585913976372582.
Full text國立臺灣大學
資訊工程學研究所
98
We combine the camera, the sound recorder, and the accelerometer on the smart-phone with the machanism of SenseCam to invent a new application of smartphones called as SenseMobile. SenseMobile employs images, sounds and accelerometer values to build an activities of daily living(ADLs) recognition system. However other portable devices merely recognize physical activities instead of high-level activities. In this thesis, we extract effective features to implementing activity recognition. In image feature extraction, we detect human face and cluster local images after pre-processing. For sound feature extraction, in the time domain, we extract volume, non-silent ratio and two human voice features - maximum peak value and number of peaks. Furthermore, From the frequency domain, we extract Mel-frequency cepstral coefficients (MFCCs), which are popular in speech recognition. After clustering vibration types, we calculate probabilities of types in accelerometer feature extraction. Then we sample instances on sliding time window and implement classification on machine learning models. We design two experiments - ADLs recognition in experimental environment and in real environment. In multiple classifications, we compare accuracy from Support Vector Machine(SVM) and Hidden Markov Model(HMM) models, and from distinct data types. In binary classifications, we utilize one-against-all method and optimize individual activity recognition. Eventually, results of two experiments prove success in ADLs recognition and bring forward unsolved defects of SenseMobile.
Kalra, Love. "Activities of Daily Living Detection Using Markov Models." 2011. http://hdl.handle.net/10222/14399.
Full textVieira, Mário Augusto da Costa. "Recognition of Daily Activities and Risk Situations Towards Robot-Assisted Living." Master's thesis, 2015. http://hdl.handle.net/10316/40548.
Full text蔡宗憲. "Applying Human Activity Recognition System to Medicine Taking and Activities of Daily Living." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/78859965692824907635.
Full text國立交通大學
電控工程研究所
100
Human activity recognition system is now a very popular subject for research and application. Using a fixed camera to track a person and recognize his (her) activity is widely seen in home surveillance. For real-time surveillance, the embedded algorithms must be efficient and fast to meet the real-time constraint. In the thesis, a new person tracking and continuous activity recognition is proposed. We build two background models, in grayscale and HSV color space as well to extract the human correctly, and we could also reduce the shadowing effect well. For better efficiency and separability, the binary image is firstly transformed to a new space by eigenspace and then canonical space transformation, and the recognition is finally done in canonical space. A three image frame sequence, 5:1 down sampling from the video, is converted to a posture sequence by template matching. The posture sequence is classified to an action by fuzzy rules inference. Fuzzy rule approach can not only combine temporal sequence information for recognition but also be tolerant to variation of action done by different people and time. Moreover, we make use of the hue component to recognize the medical pouch’s color when one is taking medicine. By combining with the hue-based pouch’s color model and human activity recognition system, we can know someone is taking medicine and its medical pouch’s color as well. Finally, we also employ the activity recognition system to record a student’s activity in the daily living.
Tseng, Shao-Wu, and 曾紹武. "A DNN-based System for the Recognition of the Activities of Daily Living." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/py688u.
Full text國立中央大學
資訊工程學系
105
In recent years, because of the improvement of medical technology, Taiwan is facing the severe problem of population aging. Since young people move out for work or marriage, the health care of independent-living Elderly is more important than ever. How to measure the activities of daily living for the elderly in an effective way is the crucial issue nowadays. In this paper, we developed a DNN-based System for the Recognition of the Activities of Daily Living. The system estimates skeleton data from the color image, which is recorded from webcam or surveillance system, and using the neural network like CNN, BPN or DNN to classify these features proposed by this paper. After recognized motions, we log the data in order to give the user a daily report. In this paper, we design ten different activities of daily living including one Scene of falling movement, and testing these data with angular tolerance and person independent experiments. In these experiments, we obtained a great result of over 90% recognition rate. Even in the real-life test, this system precision rate can also achieve 92.93%. With these experiments, we can prove that the system is good enough to provide a robust report to the user for consulting.
Krynská, Martina. "Detekce prvotních příznaků Alzheimerovy nemoci blízkou osobou nemocného." Doctoral thesis, 2015. http://www.nusl.cz/ntk/nusl-350957.
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