Search

Highlights • We propose a representation based on Fuzzy Temporal Windows for binary-sensors. • Long Short-Term Memory is deployed as a means of sequence classifier. • A balanced training is included to build an ensemble of activity-based classifiers. • The proposed approach is evaluated and benchmarked against previous approaches. Abstract There are approaches that successfully recognize activities of daily living by using a trained classifier on feature vectors created from binary sensor data. Although these approaches have been successful, there are still open issues such as the evaluation of multiple temporal windows, ensembles of classifiers or unbalanced classes which need to be addressed in order to improve the performance of the real-time activity recognition process. In this paper, we present a methodology for Real-Time Activity Recognition based on the diverse fields of Machine Learning, including Fuzzy Logic and Recurrent Neural Networks. The methodology uses a long-term and short-term representation of binary-sensor activations based on Fuzzy Temporal Windows. The paper proposes an ensemble of activity-based classifiers for the purposes of balanced training, where each classifier in the ensemble is a Long Short-Term Memory. The approach was evaluated using two binary-sensor datasets of daily living activities and benchmarked against previous approaches based on the combination of sensor activation features. [ABSTRACT FROM AUTHOR]

Diagnosis of mobility disorder has become increasingly important in recent years. This paper investigates the feasibility of classifying six hand activities including two crucially Tremored and Non-Tremored motions, based on perturbed near-field radiation of the single body worn textile (BWT) antenna. The deployed antenna shows excellent biocompatibility and less vulnerability to electromagnetic absorption. Time-domain conversion of activities set using rational function approximation is investigated. Window cropping technique is exploited for data augmentation. Seven young and one elderly volunteer were used to assess the system's performance. The measurements were performed in a chamber with an anechoic ceiling and negligible electromagnetic emanation. The Discrete Wavelet Transform (DWT) is adopted to extract the time domain features based on the fluctuation of impedance along with sliding window. Support Vector Machine (SVM) is utilized for classification. Two more algorithms namely k-Nearest Neighbor (k-NN) and Naive Bayes (NB) also been applied to test the accuracy of the classification and it is found that SVM produces best outcome in every cases under investigation. The performance of the different participants for the wrist mount and shoulder mount are tested individually. The wrist mount performs quite well, except for the elderly subject, which obtains low accuracy in both scenarios but the shoulder wins by a margin of 0.3%. Clear distinction between the results of tremored and non-tremored activities is seen. The combined augmented data for wrist and shoulder mount is observed to be 2.1% and 1.2% higher than the non-augmented data set, respectively. [ABSTRACT FROM AUTHOR]

Emotion recognition through facial expressions represents a relevant way to understand and even predict the human behavior. Thus, it has been used in various fields such as human-robot interaction and ambient assistance. Nevertheless, it remains a challenging task since expressed emotions might be affected by different parameters such as ethnic origins, age and so on. In this paper, we introduce an efficient facial expression recognition approach based on a Convolutional Neural Network architecture. Carried experimentation on five benchmark facial expression datasets confirms the efficiency of the proposed approach with recognition rates higher than 95 % . In the context of ambient assistance, we introduced an error detection module using a user action recognition from Radio Frequency IDentification tags placed on various objects of daily living. The experiments performed in a smart environment show a consistent improvement of the error detection module when including facial expression recognition. Indeed, the false positive detection rate is significantly reduced by over 20 % . [ABSTRACT FROM AUTHOR]

Activity recognition has been a hot topic for decades, from the scientific research to the development of off-the-shelf commercial products. Since people perform the activities differently, to avoid overfitting, building a general model with activity data of various users is required before the deployment for personal use. However, annotating a large amount of activity data is expensive and time-consuming. In this paper, we build a general model for activity recognition with a limited amount of labelled data. We combine Latent Dirichlet Allocation (LDA) and AdaBoost to jointly train a general activity model with partially labelled data. After that, when AdaBoost is used for online prediction, we combine it with graphical models (such as HMM and CRF) to exploit the temporal information in human activities to smooth out the accidental misclassifications. Experiments with publicly available datasets show that we are able to obtain the accuracy of more than 90% with 1% labelled data. [ABSTRACT FROM AUTHOR]

The level of physical activity is one of the most important parameters to monitor the evolution of some physical and mental disorders, e.g., obesity or depression. Its continuous monitoring provides an objective and quantitative measurement tool to be applied by therapists when developing patient-specific treatments. Most of existing activity recognition systems have been developed using multiple accelerometers strapped to the subject’s extremities. In this paper, we design an interpretable and transparent model able to describe the basic physical activity and the energy requirements of a person, by capturing the accelerations of its smartphone placed wherever he/she wants. In addition, the use of virtual agents that interact and communicate relevant information in an emotional way, increases the effectiveness and acceptance of the Human Computer Interfaces. Thus, we develop a computational application that combines activity recognition techniques with affective computing ones. We include first experimental results that demonstrate the potential and applicability of this tool. [ABSTRACT FROM AUTHOR]

Recently, sensors and actuators have quickly spread throughout our everyday life. These devices are robust, cheap, accessible, connected to the Internet, etc. With the growing needs in terms of human and medical resources to help cognitively-impaired people to remain at home, researchers are investing in new ways to exploit this technology with artificial intelligence, in order to build expert systems to assist the residents in their daily activities. Several systems have been proposed in the last few years, mostly based on binary sensors, cameras and other sensors such as Radio-frequency identification (RFID) tags. Cameras are very intrusive, binary sensors (such as movement detectors) give only basic information, and other types of sensors (such as RFID) need complex deployment. In this context, this paper presents a new assistive expert system based on electric device identification to address the problem of guidance and supervision in the performance of activities for people with cognitive disorders living in a smart home. This system is solely based on a single power analyzer placed in the electric panel. We propose an algorithmic approach used to recognize erratic behaviors related to cognitive deficits and provides cues to guide the person in the completion of an ongoing task. This is achieved through load signatures study of appliances represented by three features (active power (P), reactive power (Q) and line-to-neutral), which allows to determine the errors committed by the resident. We implemented this system within a genuine smart-home prototype equipped with household appliances used by the patient during his morning routines. Different multimedia prompting devices (iPad, screen, speakers, etc.) were used. We tested the system with real-case scenarios modeled from former clinical trials, allowing demonstration of accuracy and effectiveness of our system in assisting a cognitively-impaired resident in the completion of daily activities. [ABSTRACT FROM AUTHOR]

In the past decades, activity recognition has aroused a great interest for the research groups majoring in context-awareness computing and human behaviours monitoring. However, the correlations between the activities and their frequent patterns have never been directly addressed by traditional activity recognition techniques. As a result, activities that trigger the same set of sensors are difficult to differentiate, even though they present different patterns such as different frequencies of the sensor events. In this paper, we propose an efficient association rule mining technique to find the association rules between the activities and their frequent patterns, and build an activity classifier based on these association rules. We also address the classification of overlapped activities by incorporating the global and local weight of the patterns. The experiment results using publicly available dataset demonstrate that our method is able to achieve better performance than traditional recognition methods such as Decision Tree, Naive Bayesian and HMM. Comparison studies show that the proposed association rule mining method is efficient, and we can further improve the activity recognition accuracy by considering global and local weight of frequent patterns of activities. [ABSTRACT FROM AUTHOR]

Recent researches on sensor based human activity recognition (HAR) are mostly devoted to designing various network architectures to enhance their feature representation capacity for raw sensor data. In this paper, we focus on strengthening the vanilla convolution without adjusting the model architectures in HAR scenario. Inspired by the idea of grouped convolution, we propose a novel heterogeneous convolution for activity recognition task, where all filters within a specific convolutional layer are separated into two uneven groups. Specifically, the sensor input is down-sampled into a low-dimensional embedding, which is then convolved by one filter group to recalibrate normal filters within the other group. The two filter groups can complement each other, which is very beneficial for augmenting the receptive field of sensor signals for HAR task. Extensive experiments are conducted on several benchmark HAR datasets, which consists of OPPORTUNITY, PAMAP2, UCI-HAR, USC-HAD as well as the Weakly Labeled HAR dataset. The results show that the baseline models can be significantly improved. Our heterogeneous convolution is simple and can easily be integrated into standard convolutional layers without increasing extra parameters and computational overhead. Finally, the actual operation of heterogeneous convolution is evaluated on an embedded Raspberry Pi platform. • The proposed approach can strengthen the basic convolution. • Our proposed method divides the filters into two groups but unevenly. • It can achieve SOTA performance without increasing computational burden. • The visualizing analysis of heterogeneous convolution is provided. • Actual operation is evaluated on a Raspberry Pi platform. [ABSTRACT FROM AUTHOR]

Knowledge-driven activity recognition is an emerging and promising research area which has already shown very interesting features and advantages. However, there are also some drawbacks, such as the usage of generic and static activity models. This paper presents an approach to using data-driven techniques to evolve knowledge-driven activity models with a user’s behavioral data. The approach includes a novel clustering process where initial incomplete models developed through knowledge engineering are used to detect action clusters which represent activities and aggregate new actions. Based on those action clusters, a learning process is then designed to learn and model varying ways of performing activities in order to acquire complete and specialized activity models. The approach has been tested with real users’ inputs, noisy sensors and demanding activity sequences. Initial results have shown that complete and specialized activity models are properly learned with success rates of 100% at the expense of learning some false positive models. [ABSTRACT FROM AUTHOR]

In the multi-sensor activity recognition domain, the input space is often large and contains irrelevant and overlapped features. It is important to perform feature selection in order to select the smallest number of features which can describe the outputs. This paper proposes a new feature selection algorithms using the maximal relevance and maximal complementary (MRMC) based on neural networks. Unlike other feature selection algorithms that are based on relevance and redundancy measurements, the idea of how a feature complements to the already selected features is utilized. The proposed algorithm is evaluated on two well-defined problems and five real world data sets. The data sets cover different types of data i.e. real, integer and category and sizes i.e. small to large set of features. The experimental results show that the MRMC can select a smaller number of features while achieving good results. The proposed algorithm can be applied to any type of data, and demonstrate great potential for the data set with a large number of features. [ABSTRACT FROM AUTHOR]

• Propose an eigendecomposition-based method to select sensors in a high-efficiency way. • Propose a domain division-based method to update rules in a high-efficiency way. • Propose an online updating EBRBS for achieving activity recognition more efficiently. • Experiments show the proposed EBRBS has a high accuracy and excellent response time. Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

• Incremental user activity recognition model learned from crowdsourcing user feedback. • Similarity metric learning is introduced and improved the activity recognition model. • Multi-metric learning mixed expert model integrated different user feedbacks. • The mixture expert model interprets the process of user feedback generation. • Manifested the efficiency by good accuracy between mixture expert and other methods. Mobile activity recognition is an effective approach to understanding human context in real time. Existing methods based on supervised learning that require a large amount of training samples for building activity recognition models. The collection of labeled training samples is a boring process and most users are reluctant to get involved. Crowdsourcing is a simple and potential approach to collecting the training samples and building accurate activity recognizers. Since different people usually have different physical features and behavior patterns, an accurate activity recognition model cannot be constructed directly from the training samples collected by crowdsourcing. In this paper, we have proposed a Mixture Expert Model for Activity Recognition (MEMAR) based on feedback and crowdsourcing samples. The proposed model can continuously discover the difference between user activity and crowdsourcing samples. Then we update activity recognition models with the discovered differences. A mobile can correctly utilize crowdsourcing samples for recognition model construction with MEMAR and can also track and recognize mobile users' behavior dynamics. The experiments based on a smartphone dataset verify the validity of MEMAR. We believe MEMAR provides a basis for context-aware mobile applications. [ABSTRACT FROM AUTHOR]