A multimodal fusion enabled ensemble approach for human activity recognition in smart homes

How to deal with multi-modality data from different types of devices is a challenging issue for accurate recognition of human activities in a smart environment. In this paper, we propose a multimodal fusion enabled ensemble approach. Firstly, useful features collected from Bluetooth beacons, binary sensors, and smart floor are extracted and presented by fuzzy logic based-method with variable-size temporal windows. Secondly, a group of support vector machine classifiers are used to perform the classification task. Finally, a weighted ensemble method is used to obtain the final prediction. Especially, by applying the geometric framework, we are able to obtain the optimal weights for the ensemble. The proposed approach is evaluated on the UJAmI dataset. The experimental results demonstrate the efficacy and robustness of the proposed method

Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion

The IoT describes a development field where new approaches and trends are in constant change. In this scenario, new devices and sensors are offering higher precision in everyday life in an increasingly less invasive way. In this work, we propose the use of spatial-temporal features by means of fuzzy logic as a general descriptor for heterogeneous sensors. This fuzzy sensor representation is highly efficient and enables devices with low computing power to develop learning and evaluation tasks in activity recognition using light and efficient classifiers. To show the methodology's potential in real applications, we deploy an intelligent environment where new UWB location devices, inertial objects, wearable devices, and binary sensors are connected with each other and describe daily human activities. We then apply the proposed fuzzy logic-based methodology to obtain spatial-temporal features to fuse the data from the heterogeneous sensor devices. A case study developed in the UJAmISmart Lab of the University of Jaen (Jaen, Spain) shows the encouraging performance of the methodology when recognizing the activity of an inhabitant using efficient classifiers

Connected Health Living Lab

The school of computing, in collaboration with the institute of nursing and health research and the school of engineering, recently established the connected health living lab (CH:LL) at Ulster University. CH:LL offers a dedicated environment to support user and clinical engagement, access to state-of-the-art technology to assess usability and interaction with innovative technologies, in addition to being a dedicated environment to record user behaviours with new connected health solutions. The creation of such a dedicated environment offers a range of benefits to support multi-disciplinary research in the area of connected health. This paper illustrates the design, development, and implementation of CH:LL, including a description of the various technologies associated with the living lab at Ulster University. To conclude, the paper highlights how these resources have been used to date within various research projects

Ubiquitous Computing and Ambient Intelligence—UCAmI

The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991 [1] has recently evolved to a more general paradigm known as Ambient Intelligence (AmI) that represents a new generation of user-centred computing environments and systems. These solutions aim to find new ways to better integrate information technology into everyday life devices and activities. AmI environments are integrated by several autonomous computational devices of modern life ranging from consumer electronics to mobile phones. Ideally, people in an AmI environment will not notice these devices, but will benefit from the services these solutions provide them. Such devices are aware of the people present in those environments by reacting to their gestures, actions, and context [2]. Recently the interest in AmI environments has grown considerably due to new challenges posed by society’s demand for highly innovative services, such as smart environments, Ambient Assisted Living (AAL), e-Health, Internet of Things, and intelligent systems, among others.The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991 [1] has recently evolved to a more general paradigm known as Ambient Intelligence (AmI) that represents a new generation of user-centred computing environments and systems. These solutions aim to find new ways to better integrate information technology into everyday life devices and activities. AmI environments are integrated by several autonomous computational devices of modern life ranging from consumer electronics to mobile phones. Ideally, people in an AmI environment will not notice these devices, but will benefit from the services these solutions provide them. Such devices are aware of the people present in those environments by reacting to their gestures, actions, and context [2]. Recently the interest in AmI environments has grown considerably due to new challenges posed by society’s demand for highly innovative services, such as smart environments, Ambient Assisted Living (AAL), e-Health, Internet of Things, and intelligent systems, among others

MakeSense: An IoT Testbed for Social Research of Indoor Activities

There has been increasing interest in deploying IoT devices to study human behaviour in locations such as homes and offices. Such devices can be deployed in a laboratory or `in the wild' in natural environments. The latter allows one to collect behavioural data that is not contaminated by the artificiality of a laboratory experiment. Using IoT devices in ordinary environments also brings the benefits of reduced cost, as compared with lab experiments, and less disturbance to the participants' daily routines which in turn helps with recruiting them into the research. However, in this case, it is essential to have an IoT infrastructure that can be easily and swiftly installed and from which real-time data can be securely and straightforwardly collected. In this paper, we present MakeSense, an IoT testbed that enables real-world experimentation for large scale social research on indoor activities through real-time monitoring and/or situation-aware applications. The testbed features quick setup, flexibility in deployment, the integration of a range of IoT devices, resilience, and scalability. We also present two case studies to demonstrate the use of the testbed, one in homes and one in offices.Comment: 20 pages, 11 figure

Nuevas metodologías para el reconocimiento de cambios posturales a través de sensores

Con el fin de posibilitar nuevas alternativas que permitan mitigar la complicación de las úlceras por presión, en este trabajo se presentan los resultados de investigación de la tesis doctoral, que han permitido implementar dos metodologías de reconocimiento de cambios posturales de monitoreo en tiempo real, con dispositivos vestibles inerciales no invasivos para la detección y cálculo de postura, usando técnicas de inteligencia artificial. La primera metodología está basada en un registro histórico de la actividad corporal, dataset, y por el reconocimiento de posturas en tiempo real con técnicas de Inteligencia Artificial. Por su parte, la segunda metodología comprende el uso de dispositivos vestibles inerciales en zonas no invasivas, encargados de registrar el tiempo en que la persona ha permanecido en la misma posición, la recolección de datos de personas reales en diferentes posturas, la estimación de las posturas en tiempo real se realiza mediante técnicas de inteligencia artificial.To enable new alternatives to mitigate the complication of pressure ulcers, this work presents the research results of the doctoral thesis, which have allowed the implementation of two real-time monitoring methodologies, with devices non-invasive inertial wearables for posture detection and calculation and using artificial intelligence techniques. The first methodology is based on a historical record of body activity, a dataset, and the recognition of postures in real-time with Artificial Intelligence techniques. On other hand, the second methodology includes the use of inertial wearable devices in non-invasive areas, recording the time the person has remained in the same position, the collection of data from real people in key ulcer prevention positions, the estimation of postures in real-time using artificial intelligence techniques.Tesis Univ. Jaén. Departamento de Informática. Leída el 19/11/2021

Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledg

DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition

A LITERARY REVISION OF AMBIENT INTELIGENCE FOR THE CARE OF THE ELDERLY AT SMART HOMES

People have diverse and changing needs as they age, and the number of people living with a disability is constantly increasing. Smart homes have a unique potential to provide assisted living but are often rigidly designed with a specific and fixed problem in mind. For this reason, this research was carried out in order to identify those components of the ambient intelligence applied in home automation aimed at the elderly population or patients living with a physical or mental disability. A literature review was conducted in three (3) different databases, searching for documents published by different universities, journals, and professional associations in the international context; The topic to be addressed was ambient intelligence applied in home automation. This study aimed at a specific population: the elderly or patients who are living with a disability due to age. In this research, the main objectives, essential factors, and reliability of this technology applied in smart homes were identified considering its directed population.Las personas tienen necesidades diversas y cambiantes a medida que envejecen y el número de personas que viven con alguna discapacidad está constantemente en aumento. Las casas inteligentes tienen un potencial único para proporcionar vida asistida, pero a menudo están diseñados de manera rígida con un problema específico y fijo en mente. Por esta razón se realiza una investigación con el fin de identificar esos componentes de la inteligencia ambiental aplicada en la domótica dirigida a la población del adulto mayor o pacientes que viven con alguna discapacidad física o mental. Se realizó una revisión de la literatura en tres (3) distintas bases de datos. de documentos publicados por diferentes universidades, revistas y asociaciones profesionales en el contexto internacional; la temática abordar fue sobre la inteligencia ambiental aplicada en la domótica. Dirigido a una población en específico que es el adulto mayor o pacientes que viven con alguna discapacidad debido a la edad. En esta investigación Se identificaron los objetivos principales, factores importantes y la confiabilidad de esta tecnología aplicada en los hogares inteligentes teniendo en cuenta la población a la que va dirigida