Activity Recognition In Smart Environments Research Articles

Human activity recognition in smart environments employing margin setting algorithm

Human activity recognition is gaining promising attention in the research community with the recent revolution in artificial intelligence and machine learning to infer activities from the time-series sensor data. Significant progress has been made in the application of effective machine learning algorithms for pattern recognition and prediction of human activities in smart environments, such as ambient assisted living, healthcare monitoring, surveillance-based security and fitness tracking. In this paper, we propose to apply a supervised learning algorithm called margin setting algorithm (MSA) to predict the human activities. To validate the performance of MSA, we compare it with the support vector machine (SVM) and artificial neural network (ANN) and understand the activities of daily living of two residents in a smart home. The experimental results show that our proposed algorithm outperforms other state-of-the-art machine learning algorithms.

Complementing real datasets with simulated data: a regression-based approach

Activity recognition in smart environments is essential for ensuring the wellbeing of older residents. By tracking activities of daily living (ADLs), a person’s health status can be monitored over time. Nonetheless, accurate activity classification must overcome the fact that each person performs ADLs in different ways and in homes with different layouts. One possible solution is to obtain large amounts of data to train a supervised classifier. Data collection in real environments, however, is very expensive and cannot contain every possible variation of how different ADLs are performed. A more cost-effective solution is to generate a variety of simulated scenarios and synthesize large amounts of data. Nonetheless, simulated data can be considerably different from real data. Therefore, this paper proposes the use of regression models to better approximate real observations based on simulated data. To achieve this, ADL data from a smart home were first compared with equivalent ADLs performed in a simulator. Such comparison was undertaken considering the number of events per activity, number of events per type of sensor per activity, and activity duration. Then, different regression models were assessed for calculating real data based on simulated data. The results evidenced that simulated data can be transformed with a prediction accuracy R2 = 97.03%.

Frequent sequence pattern based activity recognition in smart environment

Various sensors are embedded in different places of smart environments to monitor and collect data about status of environments. The goal of a smart environment is to improve quality of life by enhancing the efficiency of services, providing residents’ needs using different technologies, and mining the captured data in the environment. Mining such data for extracting valuable knowledge requires critical activities and situations in smart environments to be effectively detected. Activity recognition is of great interest for researchers in context-awareness computing. However, correlations between activities and their frequent patterns have never been addressed by the traditional activity recognition techniques. Recently, some researchers have considered the frequent pattern extraction for activity detection in smart environments. Despite that, sequences and time durations between activities and sensors’ activation have not been scrutinized for activity recognition. In this paper, an extension of frequent pattern-based algorithms is proposed for activity recognition. This novel algorithm considers sequence of activated sensors as well as time durations between them in order to extract the frequent sequential patterns for activity/situation detection in smart environments. The experiment results using the publicly-available datasets demonstrated that the suggested method is efficient and can significantly improve accuracy of activity recognition in smart environments, considering the sequence matching-based conflict resolution and the order of the activated sensors.

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

In recent years, many techniques have been proposed for automatic recognition of Activities of Daily Living from smart home sensor data. However, classifiers usually use features created ad hoc. In this work, the use of ontologies is proposed for the fully automatic generation of these features. The process consists of converting the original dataset into an ontology and then combine all the concepts and relations in that ontology to obtain relevant class expressions. The high formalization of ontologies allows us to reduce the search space by discarding many meaningless expressions, such as contradictory or unsatisfiable expressions. The relevant class expressions are then used as features by the classifiers to build the classification model. To validate our proposal, we have used as reference the results obtained by four different classification algorithms that use the most commonly used features.

Unsupervised learning of sensor topologies for improving activity recognition in smart environments

There has been significant recent interest in sensing systems and ‘smart environments’, with a number of longitudinal studies in this area. Typically the goal of these studies is to develop methods to predict, at any one moment of time, the activity or activities that the resident(s) of the home are engaged in, which may in turn be used for determining normal or abnormal patterns of behaviour (e.g. in a health-care setting). Classification algorithms, such as Conditional Random Field (CRFs), typically consider sensor activations as features but these are often treated as if they were independent, which in general they are not. Our hypothesis is that learning patterns based on combinations of sensors will be more powerful than single sensors alone. The exhaustive approach – to take all possible combinations of sensors and learn classifier weights for each combination – is clearly computationally prohibitive. We show that through the application of signal processing and information-theoretic techniques we can learn about the sensor topology in the home (i.e. learn an adjacency matrix) which enables us to determine the combinations of sensors that will be useful for classification ahead of time. As a result we can achieve classification performance better than that of the exhaustive approach, whilst only incurring a small cost in terms of computational resources. We demonstrate our results on several datasets, showing that our method is robust in terms of variations in the layout and the number of residents in the house. Furthermore, we have incorporated the adjacency matrix into the CRF learning framework and have shown that it can improve performance over multiple baselines.

Active learning enabled activity recognition

Activity recognition in smart environment has been investigated rigorously in recent years. Researchers are enhancing the underlying activity discovery and recognition process by adding various dimensions and functionalities. But one significant barrier still persists which is collecting the ground truth information. Ground truth is very important to initialize a supervised learning of activities. Due to a large variety in number of Activities of Daily Living (ADLs), acknowledging them in a supervised way is a non-trivial research problem. Most of the previous researches have referenced a subset of ADLs and to initialize their model, they acquire a vast amount of informative labeled training data. On the other hand to collect ground truth and differentiate ADLs, human intervention is indispensable. As a result it takes an immense effort and raises privacy concerns to collect a reasonable amount of labeled data. In this paper, we propose to use active learning to alleviate the labeling effort and ground truth data collection in activity recognition pipeline. We investigate and analyze different active learning strategies to scale activity recognition and propose a dynamic k-means clustering based active learning approach. Experimental results on real data traces from a retirement community-(IRB #HP-00064387) help validate the early promise of our approach.

Enhancing ontological reasoning with uncertainty handling for activity recognition

Ontology-based activity recognition is gaining interest due to its expressiveness and comprehensive reasoning mechanism. An obstacle to its wider use is that the imperfect observations result in failure of recognizing activities. This paper proposes a novel reasoning algorithm for activity recognition in smart environments. The algorithm integrates OWL ontological reasoning mechanism with Dempster–Shafer theory of evidence to provide support for handling uncertainty in activity recognition. It quantifies uncertainty while aggregating contextual information and provides a degree of belief that facilitates more robust decision making in activity recognition. The presented approach has been implemented and evaluated on an internal and public datasets and compared with a data-driven approach that is using hidden Markov model. Results have shown that the proposed reasoning approach can accommodate uncertainties and subsequently infer the activities more accurately in comparison with existing ontology-based recognition and perform comparably well to the data-driven approach.

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments.

Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users' spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user or a home environment. In this paper, we propose a hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors. Our approach harnesses the power of simple ambient sensors (e.g., motion sensors) to provide additional 'hidden' context (e.g., room-level location) of an individual, and then combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how the use of spatiotemporal constraints along with multitude of data sources can be used to significantly improve the accuracy and computational overhead of traditional activity recognition based approaches such as coupled-hidden Markov models. Experimental results on two separate smart home datasets demonstrate that this approach improves the accuracy of complex ADL classification by over 30 %, compared to pure smartphone-based solutions.

A sensor and video based ontology for activity recognition in smart environments.

Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data.

A Two-stage Method for Solving Multi-resident Activity Recognition in Smart Environments

To recognize individual activities in multi-resident environments with pervasive sensors, some researchers have pointed out that finding data associations can contribute to activity recognition and previous methods either need or infer data association when recognizing new multi-resident activities based on new observations from sensors. However, it is often difficult to find out data associations, and available approaches to multi-resident activity recognition degrade when the data association is not given or induced with low accuracy. This paper exploits some simple knowledge of multi-resident activities through defining Combined label and the state set, and proposes a two-stage activity recognition method for multi-resident activity recognition. We define Combined label states at the model building phase with the help of data association, and learn Combined label states at the new activity recognition phase without the help of data association. Our two stages method is embodied in the new activity recognition phase, where we figure out multi-resident activities in the second stage after learning Combined label states at first stage. The experiments using the multi-resident CASAS data demonstrate that our method can increase the recognition accuracy by approximately 10%.

Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors

Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0.05, proving that the hybrid approach is better suited for the addressed domain.

Ambient Activity Recognition in Smart Environments for Cognitive Assistance

In this paper, the authors investigate the challenging key issues that emerge from research in the field of ambient intelligence in smart environments, under the context of activity recognition. The authors clearly describe the specific functional needs inherent in cognitive assistance for effective activity recognition, and then the authors present the fundamental research that addresses this problem in such a context. This paper is more of a survey and an analysis of existing works that have been studied for potential integration into our laboratories, rather than a focused evaluation report. The authors’ objective is to identify gaps in the capabilities of current techniques and to suggest the most productive lines of research to address this complex issue. As such, the contribution is of both theoretical and practical significance.