Activity Recognition In Smart Homes Research Articles

Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes.

Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant’s activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance. However, they are prohibitively slow during the evaluation process and may run into the risk of overfitting due to the extent of the search. Motivated by this characteristic, this paper attempts to reduce the cost of the evaluation process and overfitting through tree alignment. The performance of our method is evaluated on two public datasets obtained in two distinct smart home environments.

Combining emerging patterns with random forest for complex activity recognition in smart homes

New healthcare technologies are emerging with the increasing age of the society, where the development of smart homes for monitoring the elders’ activities is in the center of them. Identifying the resident’s activities in an apartment is an important module in such systems. Dense sensing approach aims to embed sensors in the environment to report the detected events continuously. The events are segmented and analyzed via classifiers to identify the corresponding activity. Although several methods were introduced in recent years for detecting simple activities, the recognition of complex ones requires more effort. Due to the different time duration and event density of each activity, finding the best size of the segments is one of the challenges in detecting the activity. Also, using appropriate classifiers that are capable of detecting simple and interleaved activities is the other issue. In this paper, we devised a two-phase approach called CARER (Complex Activity Recognition using Emerging patterns and Random forest). In the first phase, the emerging patterns are mined, and various features of the activities are extracted to build a model using the Random Forest technique. In the second phase, the sequences of events are segmented dynamically by considering their recency and sensor correlation. Then, the segments are analyzed by the generated model from the previous phase to recognize both simple and complex activities. We examined the performance of the devised approach using the CASAS dataset. To do this, first we investigated several classifiers. The outcome showed that the combination of emerging patterns and the random forest provide a higher degree of accuracy. Then, we compared CARER with the static window approach, which used Hidden Markov Model. To have a fair comparison, we replaced the dynamic segmentation module of CARER with the static one. The results showed more than 12% improvement in f-measure. Finally, we compared our work with Dynamic sensor segmentation for real-time activity recognition, which used dynamic segmentation. The f-measure metric demonstrated up to 12.73% improvement.

Multi Resident Complex Activity Recognition in Smart Home: A Literature Review

Multi Resident Complex Activity Recognition in Smart Home: A Literature Review

Analytical Review on Ontological Human Activity Recognition Approaches

New advancements in pervasive computing technology have turned smart homes into a daily living monitoring tool increasingly used for elderly. Recently, using knowledge driven approaches such as ontology to introduce semantic smart homes has received attention due to their flexibility, reasoning and knowledge representation. Due to the vast number of ontological human activity recognition methods, the proposed ontological human activity recognition framework can be effective in analyzing and evaluating different methods in different applications and dealing with various challenges. Also, due to numerous challenges involved in different aspects of ontology-based human activity recognition in smart homes, this paper offers a classification for challenges in human activity recognition in ontology based systems. Then the proposed ontological human activity recognition framework is evaluated based on the proposed classification and ontology-based techniques which are thought to solve some of the challenges are examined and analyzed.

Active learning with uncertainty sampling for large scale activity recognition in smart homes

Active learning with uncertainty sampling for large scale activity recognition in smart homes

Probabilistic ontology based activity recognition in smart homes using Markov Logic Network

Designing an activity recognition system that models various activities of an occupant is the fundamental task in creating a smart home. Activity Recognition (AR) modeling, has witnessed a comprehensive range of research, that focuses independently on probabilistic approaches and on ontology based models as well. The research presented in this paper introduces an innovative approach in AR system design that integrates probabilistic inference with the represented domain ontology. Data obtained from sensors are uncertain in nature and mapping uncertainty over ontology will not yield good accuracy in the context of AR. The proposed system augments ontology based activity recognition with probabilistic reasoning through Markov Logic Network (MLN) which is a statistical relational learning approach. The proposed system utilizes the model theoretic semantic property of description logic, to convert the represented ontology activity model to its corresponding first order rules. MLN is constructed by learning weighted first order rules that enable probabilistic reasoning within a knowledge representation framework. The experiments based on datasets obtained from smart home prototypes illustrate the effectiveness of integrating probabilistic reasoning over domain ontology and the result analysis shows enhanced recognition accuracy in comparison with existing approaches.

Activity Recognition in Smart Homes

In recent years, activity recognition in smart homes is an active research area. It is an important problem of Human Computer Interaction (HCI), and has many applications in HCI, such as assistive living and healthcare. Recognition of users’ common behaviors allows an environment to provide personalized service. Unlike activity recognition in computer vision which uses cameras, it studies activities by embedded sensors in smart homes. In this paper, we propose a method to extract latent features from sensor data by Beta Process Hidden Markov Model (BP-HMM). The contributions of our method are twofold: 1, we extend BP-HMM by dependent Beta process, and integrate state constraints of sensors into the sampling process of BP-HMM. 2, we extract latent features automatically by our dependent BP-HMM, and train a structural support vector machine (SVM) by these features in a supervised way for activity recognition. To evaluate the proposed method, we performed experiments on the real-world smart home datasets. Our results suggest that extracting latent features from sensor data leads to good performance for activity recognition.

Exploiting Passive RFID Technology for Activity Recognition in Smart Homes

The number of seniors and other people needing daily assistance continues to increase, but the current human resources available to achieve this in the coming years will certainly be insufficient. To remedy this situation, smart habitats have emerged as an innovative avenue for supporting needs of daily assistance. Smart homes aim to provide cognitive assistance in decision making by giving hints, suggestions, and reminders, with different kinds of effectors, to residents. To implement such technology, the first challenge to overcome is the recognition of ongoing activity. Some researchers have proposed solutions based on binary sensors or cameras, but these types of approaches infringed on residents' privacy. A new affordable activity-recognition system based on passive RFID technology can detect errors related to cognitive impairment. The entire system relies on an innovative model of elliptical trilateration with several filters, as well as on an ingenious representation of activities with spatial zones. The authors have deployed the system in a real smart-home prototype; this article renders the results of a complete set of experiments conducted on this new activity-recognition system with real scenarios.

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.

Integration of discriminative and generative models for activity recognition in smart homes

Activity recognition in smart homes enables the remote monitoring of elderly and patients. In healthcare systems, reliability of a recognition model is of high importance. Limited amount of training data and imbalanced number of activity instances result in over-fitting thus making recognition models inconsistent. In this paper, we propose an activity recognition approach that integrates the distance minimization (DM) and probability estimation (PE) approaches to improve the reliability of recognitions. DM uses distances of instances from the mean representation of each activity class for label assignment. DM is useful in avoiding decision biasing towards the activity class with majority instances; however, DM can result in over-fitting. PE on the other hand has good generalization abilities. PE measures the probability of correct assignments from the obtained distances, while it requires a large amount of data for training. We apply data oversampling to improve the representation of classes with less number of instances. Support vector machine (SVM) is applied to combine the outputs of both DM and PE, since SVM performs better with imbalanced data and further improves the generalization ability of the approach. The proposed approach is evaluated using five publicly available smart home datasets. The results demonstrate better performance of the proposed approach compared to the state-of-the-art activity recognition approaches.

Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

As the number of elderly people has increased worldwide, there has been a surge of research into assistive technologies to provide them with better care by recognizing their normal and abnormal activities. However, existing abnormal activity recognition (AAR) algorithms rarely consider sub-activity relations when recognizing abnormal activities. This paper presents an application of the Hidden State Conditional Random Field (HCRF) method to detect and assess abnormal activities that often occur in elderly persons’ homes. Based on HCRF, this paper designs two AAR algorithms, and validates them by comparing them with a feature vector distance based algorithm in two experiments. The results demonstrate that the proposed algorithms favorably outperform the competitor, especially when abnormal activities have same sensor type and sensor number as normal activities.

Sensor Selection in Smart Homes

Abstract There has been a lot of work focussing on activity recognition in smart homes, with the aim of the home being to monitor the activities of the inhabitant and identify deviations from the norm. For a smart home to support its inhabitants, the recognition sys- tem needs to accurately learn from the observations acquired through sensors, which are installed in the home. Given a predefined set of the inhabitant's daily activities, the question is which sensors are important to accurately recognise these activities. This paper addresses the sensor selection problem through a filter-based approach, which is based on information gain. We evaluate the effectiveness of the proposed method on two publicly available smart home datasets.

Activity recognition in smart homes with self verification of assignments

Activity recognition in smart homes provides valuable benefits in the field of health and elderly care by remote monitoring of patients. In health care, capabilities of both performing the correct recognition and reducing the wrong assignments are of high importance. The novelty of the proposed activity recognition approach lies in being able to assign a category to the incoming activity, while measuring the confidence score of the assigned category that reduces the false positives in the assignments. Multiple sensors deployed at different locations of a smart home are used for activity observations. For multi-class activity classification, we propose a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments. We obtain the confidence score of each assignment by estimating the activity distribution within each class such that the assignments with low confidence are separated for further investigation by a human operator. The proposed approach is evaluated using a comprehensive performance evaluation metrics. Experimental results obtained from nine publicly available smart home datasets demonstrate a better performance of the proposed approach compared to the state of the art.

Clustering-based ensemble learning for activity recognition in smart homes.

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

A Genetic Algorithm-based Classifier Ensemble Optimization for Activity Recognition in Smart Homes

Over the last few years, one of the most common purposes of smart homes is to provide human centric services in the domain of u-healthcare by analyzing inhabitants’ daily living. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. Smart homes indicate variation in terms of performed activities, deployed sensors, environment settings, and inhabitants’ characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. This observation has motivated towards combining multiple classifiers to take advantage of their complementary performance for high accuracy. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with Genetic Algorithm (GA). Our proposed method combines the measurement level output of different classifiers for each activity class to make up the ensemble. For the evaluation of the proposed method, experiments are performed on three real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models. The significant improvement is achieved from 0.82 to 0.90 in the F-measures of recognized activities as compare to existing methods.

An Agent-mediated Ontology-based Approach for Composite Activity Recognition in Smart Homes

Activity recognition enables ambient assisted living applications to provide activity- aware services to users in smart homes. Despite significant progress being made in activity recognition research, the focus has been on simple activity recognition leaving composite activity recognition an open problem. For instance, knowledge-driven activity recognition has recently attracted increasing attention but mainly focused on simple activities. This paper extends previous work by introducing a knowledge-driven approach to recognition of composite activities such as interleaved and concurrent activities. The approach combines the recognition of single and composite activities into a unified framework. To support composite activity modelling, it combines ontological and temporal knowledge modelling formalisms. In addition, it exploits ontological reasoning for simple activity recognition and qualitative temporal inference to support composite activity recognition. The approach is organized as a multi-agent system to enable multiple activities to be simultaneously monitored and tracked. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. The experimental results have shown that average recognition accuracy for composite activities is 88.26%.

A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes

In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users” actions to gain knowledge about their habits and preferences.

A Knowledge-Driven Approach to Activity Recognition in Smart Homes

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds.

Capacitive indoor positioning and contact sensing for activity recognition in smart homes

In smart homes, unobtrusive monitoring of user position and activity are important but challenging tasks. With the current state of technology, this task is especially hard to carry out in private areas where video surveillance is considered undesirable or even offensive. Even though some alternative methods for passive and unobtrusive monitoring of people have been proposed in the past, we still do not have a simple method that could be used to measure user position and activities as a single practical solution. To fulfill this need, this paper presents a single privacy-preserving method to measure user position and activity which can easily be adapted to measure the subject's height and posture as well. The system proposed in this paper can locate a person at floor level and monitor the subject's interaction with common household items such as a bed, sofa, table or refrigerator. The measurement method is based on the conductivity of the human body and on capacitive coupling of low-frequency signals between electrodes embedded in the floor and the in the environment. A test system was built for the TUT Smart Home and was evaluated with multiple test subjects, including a two-week-long living test to show the system's potential in long-term monitoring applications. The results show that a standing person can be positioned to within either 7 or 11-cm accuracy at a 90% confidence level using 30 × 30-cm and 60 × 60-cm-sized transmitting floor electrodes, respectively. For people walking, the respective accuracies are 17 and 33 cm. According to the long-term test results, the interactions with the environment were detected accurately. All the test data from this long-term living test, including the person's position, contact with common household items as well as the user annotations, have been made public and are available for download.

Learning Setting-Generalized Activity Models for Smart Spaces

The data mining and pervasive computing technologies found in smart homes offer unprecedented opportunities for providing context-aware services, including health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to provide these services, smart environment algorithms need to recognize and track activities that people normally perform as part of their daily routines. However, activity recognition has typically involved gathering and labeling large amounts of data in each setting to learn a model for activities in that setting. We hypothesize that generalized models can be learned for common activities that span multiple environment settings and resident types. We describe our approach to learning these models and demonstrate the approach using eleven CASAS datasets collected in seven environments.