A Machine Learning based Activity Recognition for Ambient Assisted Living

Ambient assisted living (AAL) technology is of considerable interest in supporting the independence and quality of life of older adults. As such, it is a core focus of the emerging field of gerontechnology, which considers how technological innovation can aid health and well-being in older age. Human activity recognition plays a vital role in AAL. Successful identification of human activity is crucial for any assistive care services for elderly people living alone in a home. In this paper, a method for activity recognition is proposed which recognizes or classifies activities based on sensor data. The method uses most trending algorithm in deep learning domain, i.e. LSTM to build the model .The proposed method is evaluated using a well known activity sensor dataset

HMM-based activity recognition with a ceiling RGB-D camera

Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, the abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available

Multi-view stacking for activity recognition with sound and accelerometer data

Many Ambient Intelligence (AmI) systems rely on automatic human activity recognition for getting crucial context information, so that they can provide personalized services based on the current users’ state. Activity recognition provides core functionality to many types of systems including: Ambient Assisted Living, fitness trackers, behavior monitoring, security, and so on. The advent of wearable devices along with their diverse set of embedded sensors opens new opportunities for ubiquitous context sensing. Recently, wearable devices such as smartphones and smart-watches have been used for activity recognition and monitoring. Most of the previous works use inertial sensors (accelerometers, gyroscopes) for activity recognition and combine them using an aggregation approach, i.e., extract features from each sensor and aggregate them to build the final classification model. This is not optimal since each sensor data source has its own statistical properties. In this work, we propose the use of a multi-view stacking method to fuse the data from heterogeneous types of sensors for activity recognition. Specifically, we used sound and accelerometer data collected with a smartphone and a wrist-band while performing home task activities. The proposed method is based on multi-view learning and stacked generalization, and consists of training a model for each of the sensor views and combining them with stacking. Our experimental results showed that the multi-view stacking method outperformed the aggregation approach in terms of accuracy, recall and specificity

CLAN: A Contrastive Learning based Novelty Detection Framework for Human Activity Recognition

In ambient assisted living, human activity recognition from time series sensor data mainly focuses on predefined activities, often overlooking new activity patterns. We propose CLAN, a two-tower contrastive learning-based novelty detection framework with diverse types of negative pairs for human activity recognition. It is tailored to challenges with human activity characteristics, including the significance of temporal and frequency features, complex activity dynamics, shared features across activities, and sensor modality variations. The framework aims to construct invariant representations of known activity robust to the challenges. To generate suitable negative pairs, it selects data augmentation methods according to the temporal and frequency characteristics of each dataset. It derives the key representations against meaningless dynamics by contrastive and classification losses-based representation learning and score function-based novelty detection that accommodate dynamic numbers of the different types of augmented samples. The proposed two-tower model extracts the representations in terms of time and frequency, mutually enhancing expressiveness for distinguishing between new and known activities, even when they share common features. Experiments on four real-world human activity datasets show that CLAN surpasses the best performance of existing novelty detection methods, improving by 8.3%, 13.7%, and 53.3% in AUROC, balanced accuracy, and [email protected] metrics respectively

Revealing Daily Human Activity Pattern using Process Mining Approach

In  the  last  few  years,  with  the  emergence  of ambient assisted living, the study of human behavioral pattern took  a  wide  interest  from  research  communities  around  the world. In many literatures, pattern recognition was widely adopted approach to implements in human behavior study from computing perspective. Pattern recognition brings a promising results in terms of accuracy for modeling human behavior. But the problem with this approach is the complexity of knowledge representation  which  formulated  in  mathematical  model.  In turns, a correction by the experts is hardly conducted. In another hand, gathering a graphical insight is  not a trivial task. This paper  investigate the use of process mining technology to gives an alternative to such problems. Process mining is data-driven approach to infer a graphical representation of any kind of process. In terms of human behavior, process can be defined as sequences of activities performed by human on daily basis. From the conducted experiments process mining was shown a potential use to infer a human daily activity pattern in a graphical representation

Evolving models for incrementally learning emerging activities

Ambient Assisted Living (AAL) systems are increasingly being deployed in real-world environments and for longperiods of time. This significantly challenges current approaches that require substantial setup investment and cannot account forfrequent, unpredictable changes in human behaviours, health conditions, and sensor deployments. The state-of-the-art method-ology in studying human activity recognition is cultivated from short-term lab or testbed experimentation, i.e., relying on well-annotated sensor data and assuming no change in activity models. This paper propose a technique,EMILEA, to evolve an ac-tivity model over time with new types of activities. This technique novelly integrates two recent advances in continual learning:Net2Net – expanding the architecture of a model while transferring the knowledge from the previous model to the new modeland Gradient Episodic Memory – controlling the update on the model parameters to maintain the performance on recognisingpreviously learnt activities. This technique has been evaluated on two real-world, third-party, datasets and demonstrated promising results on enhancing the learning capacity to accommodate new activities that are incrementally introduced to the modelwhile not compromising the accuracy on old activities.PostprintPeer reviewe

Modelo predictivo para el reconocimiento de actividades humanas basado en técnicas de Machine Learning y de selección de características

Ambient assisted living (AAL), focus on generating innovative products and services in order to aid and medical attention to elderly people who suffer from neurodegenerative diseases or a disability. This research area is responsible for the development of activity recognition systems (ARS) which are based on Human Activity Recognition (HAR), specifically in activities of daily life (ADL) in indoor environments. These systems make it possible to identify the type of activity that people carry out, offering a possibility of effective assistance that allows them to carry out daily activities with total normality. The performance of the ARS in the HAR process must be evaluated through the approach of experimental scenarios with data sets available by the scientific community in online repositories, this work proposes a variety of combinations of machine learning algorithms with feature selection algorithms, obtaining as a result a functional model for the HAR, which combines the classification algorithm Logistic model trees (LMT) and the feature selection algorithm One R.Los ambientes asistidos para la vida - AAL por sus siglas en inglés (Ambient Assisted Living), se enfocan en generar productos y servicios innovadores en aras de proporcionar asistencia y atención médica a personas de avanzada edad que padezcan enfermedades neurodegenerativas o alguna discapacidad. Esta área de investigación se encarga del desarrollo de sistemas para el reconocimiento de actividad - ARS (Activity Recognition Systems) los cuales están basados en el reconocimiento de actividades humanas - HAR (Human Activity Recognition), específicamente en actividades de la vida diaria - ADL (Activities of Daily Living) en ambientes interiores (indoor). Estos sistemas permiten identificar el tipo de actividad que realizan las personas, ofreciendo una posibilidad de asistencia efectiva que les permita llevar a cabo actividades cotidianas con total normalidad. El desempeño de los ARS en el proceso de HAR, debe ser evaluado a través del planteamiento de escenarios experimentales con conjuntos de datos dispuestos por la comunidad científica en repositorios en linea, este trabajo plantea una variedad de combinaciones de técnicas de machine learning con técnicas de selección de características, obteniendo como resultado un modelo funcional para el HAR, que combina la técnica de clasificación árboles para el modelamiento logístico - LMT por sus siglas en inglés (Logistic Model Trees) y la técnica de selección de características One R

Fuzzy transfer learning in human activity recognition.

Assisted living environments are incorporated with different technological solutions to improve the quality of life and well-being. In recent years, there has been a growing interest in the research community on how to develop evolving solutions to aid assisted living. Different techniques have been studied to address the need for technological systems which are intelligent enough to evolve their knowledge to solve tasks which have not been previously encountered. One such approach is Transfer Learning (TL), for example, between humans and robots. Humans excel at dealing with everyday activities, learning and adapting to different activities. This comprises different complex techniques which enable the lifelong learning process from observation of our environment. To obtain similar learning in assistive agents, TL is needed. The aim of the research reported in this thesis is to address the challenge associated with learning and reuse of knowledge by assistive agents in an Ambient Assisted Living (AAL) environment. In this thesis, a novel approach to transfer learning of human activities through the combination of three methods; TL, Fuzzy Systems (FS) and Human Activity Recognition (HAR) is presented. Through the incorporation of FS into the proposed approach, uncertainty that is evident in the dynamic nature of human activities are embedded into the learning model. This research is focused on applications in assistive robotics. This is with a purpose of enabling assistive robots in AAL environments to acquire knowledge of such activities as are performed by humans. To achieve this, an extensive investigation into existing learning methods applied in human activities is conducted. The investigation encompasses current state-of-the-art of TL approaches employed in skill transfer across different but contextually related activities. To address the research questions identified in the thesis, the contributions of the methodology employed are in three main categories; 1) Firstly, a novel framework for human activity learning from information observed. Experiments are conducted on selected human activities to acquire enough information for building the framework. From the acquired information, relevant features extracted are used in a learning model to recognise different activities. 2) Secondly, the sequence of occurrence(s) of tasks in an activity needs to be considered in the learning process. Therefore, in this research, a novel technique for adaptive learning of activity sequences from acquired information is developed. 3) Finally, from the sequence obtained, a novel technique for transfer of human activity across heterogeneous feature space existing between a human and an assistive robot is developed. These categories form the basis of the TL framework modelled in this research. The framework proposed is applied to TL of human activity from data generated experimentally and benchmark datasets of various classes of human activities. The results presented in this thesis show that exploring the process of human activity learning is an important aspect in the TL framework. The features extracted sufficiently distinguish relevant patterns for each activity. Also, the results demonstrate the ability of the methodology to learn and predict human actions with a high degree of certainty. This encourages the use of TL in assisted living environments and other applications. This and many more applications of TL in technology would be a potential driver of the next revolution in artificial intelligence

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context

Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living

YesHuman activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers pervasive sensing environment for the elderly, persons with disability and chronic illness