Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble

Recent research on modeling human daily activities in smart showed substantive challenges due to the heterogeneity of incoming data and the natural interleaving among human activities. This paper introduces an approach for handling multimodal activity recognition systems in smart homes by modeling atomic actions using multi-layer perceptron neural network; enriched with background knowledge from sensors' profiles. The proposed methodology defines the activity recognition problem as multiclass classification problem, in which an atomic action might belong to more than one activity at a given time. For each sensor, a profile of selected features has been created to identify the spatiotemporal aspect of its triggered actions. Then, a multimodal segmentation technique is applied to assemble actions that formulate a given activity into independent blocks of tuples. Such segmentation considers the interleaving among actions by applying multiclass classification. Experiments on multimodal dataset have been conducted to measure the performance of the proposed methodology. Measures such as F-measure and accuracy have been used to compare among different classifiers. The preliminary results showed a significant enhancement especially on activities that share high number of similar actions (performed concurrently).

Diversity is a key characteristic of a classifier ensemble. A classifier ensemble must be composed of base classifiers with different performance in different areas of the problem space. Several works studied different diversity measures by performing extensive numerical experiments. However, up to our knowledge, no method has been proposed to visualize the diversity of a classifier ensemble. In this contribution, we propose a novel approach to visualize and analyze classifier ensembles diversity. Our novel proposal (called DivGrams), allows the visualization of the diversity between base classifiers in the classifier ensemble by using a network representation. The nodes represent base classifiers and its edges represent diversity relations between the base classifiers. We apply DivGrams to fuzzy rule-based classifier ensembles, as in our previous work these machine learning models proved to deal well with complex classification problems. A preliminary experimental study is performed using two UCI datasets in order to show the usefulness of DivGrams. Furthermore, we show a practical application of DivGrams to develop manual classifier selection on eight UCI datasets. The results obtained show the interesting characteristics of our proposal and support this novel approach in the fuzzy classifier ensemble field.

A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions

An automatic human action representation and recognition technique is proposed in this paper. Appearance-change problem due to human wearing dresses and body shapes is also investigated in this study for automatic human action recognition. A tuned eigenspace technique is proposed for automatic human posture and/or motion recognition that successfully overcome the preceding problems. We employ image pre-processing by Gaussian and Sobel edge filter, called the first stage tuning, for reducing a dress effect, and a mean eigenspace produced by taking a mean of the similar postures, called the second stage tuning, for avoiding the preceding problems. An eigenspace called a tuned eigenspace is obtained from the mentioned processes and it is used for further recognition of unfamiliar postures and actions. The proposed method is compared with a related technique and the robustness of this approach is presented.

IntroductionThe increasing complexity of cyber-physical systems (CPS) demands robust and efficient action recognition frameworks capable of seamlessly integrating multi-modal data. Traditional methods often lack adaptability and perform poorly when integrating diverse information sources, such as spatial and temporal cues from diverse image sources.MethodsTo address these limitations, we propose a novel Multi-Scale Attention-Guided Fusion Network (MSAF-Net), which leverages advanced image fusion techniques to significantly enhance action recognition performance in CPS environments. Our approach capitalizes on multi-scale feature extraction and attention mechanisms to dynamically adjust the contributions from multiple modalities, ensuring optimal preservation of both structural and textural information. Unlike conventional spatial or transform-domain fusion methods, MSAF-Net integrates adaptive weighting schemes and perceptual consistency measures, effectively mitigating challenges such as over-smoothing, noise sensitivity, and poor generalization to unseen scenarios.ResultThe model is designed to handle the dynamic and evolving nature of CPS data, making it particularly suitable for applications such as surveillance, autonomous systems, and human-computer interaction. Extensive experimental evaluations demonstrate that our approach not only outperforms state-of-the-art benchmarks in terms of accuracy and robustness but also exhibits superior scalability across diverse CPS contexts.DiscussionThis work marks a significant advancement in multi-modal action recognition, paving the way for more intelligent, adaptable, and resilient CPS frameworks. MSAF-Net has strong potential for application in medical imaging, particularly in multi-modal diagnostic tasks such as combining MRI, CT, or PET scans to enhance lesion detection and image clarity, which is essential in clinical decision-making.

In the last decades, the use of ensembles of classifiers in different domains of application has received significant attention of the Machine Learning community. In a typical architecture of ensemble, a new input pattern is presented to all K components. The individual classifiers provide their output and send them to a combination method, which is responsible for providing the final output. Ensemble of classifiers can be applied to any classification problem, single-label or multi- label problems. Multi-label (ML) classification has been received much attention from various research domains, such as text categorization, bioinformatics, computer vision, among others. In this paper, we investigate the use of ensemble of classifiers when applied to multi-label problems, focusing on the use of diversity measures in these systems when applied to multi-label problems. In order to do this, the present work proposes an adapted approach for two well-known diversity measures, good and bad, for ensembles of multi-label classifiers. In order to assess the feasibility of the proposed diversity measures in ensemble of multi-label classifiers, an empirical analysis is conducted, with eight multi-label classification problems. Generally, our finds indicate that there is a strong correlation between performance of the ML ensembles with the proposed diversity measures, showing that these diversity measures can be used as an important tool in the design of efficient ensembles of multi-label classifiers.

In this paper, we present a home-monitoring oriented human activity recognition benchmark database, based on the combination of a color video camera and a depth sensor. Our contributions are two-fold: 1) We have created a publicly releasable human activity video database (i.e., named as RGBD-HuDaAct), which contains synchronized color-depth video streams, for the task of human daily activity recognition. This database aims at encouraging more research efforts on human activity recognition based on multi-modality sensor combination (e.g., color plus depth). 2) Two multi-modality fusion schemes, which naturally combine color and depth information, have been developed from two state-of-the-art feature representation methods for action recognition, i.e., spatio-temporal interest points (STIPs) and motion history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively and superior recognition performances over their uni-modality (e.g., color only) counterparts are demonstrated.

Human activity modeling and recognition using wearable sensors is important in pervasive healthcare, with applications including quantitative assessment of motor function, rehabilitation, and elder care. Previous human activity recognition techniques use a whole-motion model in which continuous sensor streams are divided into windows with a fixed time duration whose length is chosen such that all the relevant information in each activity signal can be extracted from each window. In this paper, we present a statistical motion primitive-based framework for human activity representation and recognition. Our framework is based on Bag-of-Features (BoF), which builds activity models using histograms of primitive symbols. We experimentally validate the effectiveness the BoF-based framework for recognizing nine activity classes and evaluate six factors which impact the performance of the framework. The factors include window size, choices of features, methods to construct motion primitives, motion vocabulary size, weighting schemes of motion primitive assignments, and learning machine kernel functions. Finally, we demonstrate that our statistical BoF-based framework can achieve much better performance compared to a non-statistical string-matching-based approach.

Multimodal activity recognition with local block CNN and attention-based spatial weighted CNN

Understanding semantic meaning of human actions captured in unconstrained environments has broad applications in fields ranging from patient monitoring, human-computer interaction, to surveillance systems. However, while great progresses have been achieved on automatic human action detection and recognition in videos that are captured in controlled/constrained environments, most existing approaches perform unsatisfactorily on videos with uncontrolled/unconstrained conditions (e.g., significant camera motion, background clutter, scaling, and light conditions). To address this issue, the authors propose a robust human action detection and recognition framework that works effectively on videos taken in controlled or uncontrolled environments. Specifically, the authors integrate the optical flow field and Harris3D corner detector to generate a new spatial-temporal information representation for each video sequence, from which the general Gaussian mixture model (GMM) is learned. All the mean vectors of the Gaussian components in the generated GMM model are concatenated to create the GMM supervector for video action recognition. They build a boosting classifier based on a set of sparse representation classifiers and hamming distance classifiers to improve the accuracy of action recognition. The experimental results on two broadly used public data sets, KTH and UCF YouTube Action, show that the proposed framework outperforms the other state-of-the-art approaches on both action detection and recognition.

Human Activity Recognition (HAR) is pivotal in ubiquitous computing, offering benefits to human-centric services such as health monitoring, smart homes, and eldercare systems. HAR leverages smartphones, smartwatches, and other wearable devices to collect sensory data annotated with activity labels, which are then used to train machine learning or deep learning models for automatic activity recognition. Effective HAR systems must integrate information from multiple modalities to accurately assist users. This paper introduces the XAttentionHAR model, an innovative cross-modality attention-based ensemble model for HAR. Our approach utilizes a self-attention module to extract features within each modality and an inter-domain cross-attention module to capture and integrate long-term dependencies across domains. The cross-modality attention mechanism enhances the fusion of diverse modalities, enriching the semantic information. We conducted extensive experiments on the WISDM public dataset, which includes accelerometer and gyroscope data from smartwatches and smartphones. Our results demonstrate that XAttentionHAR outperforms other state-of-the-art methods in activity recognition, achieving 98.48% accuracy for smartphone-based HAR and 98.73% accuracy for smartwatch-based HAR, paving the way for improved human-centric services.

Video-based human action recognition is one of the most important and challenging areas of research in the field of computer vision. Human action recognition has found many pragmatic applications in video surveillance, human-computer interaction, entertainment, autonomous driving, etc. Owing to the recent development of deep learning methods for human action recognition, the performance of action recognition has significantly enhanced for challenging datasets. Deep learning techniques are mainly used for recognizing actions in images and videos comprising of Euclidean data. A recent development in deep learning methods is the extension of these techniques to non-Euclidean data or graph data with many nodes and edges. Human body skeleton resembles a graph, therefore, the graph convolutional network (GCN) is applicable to the non-Euclidean body skeleton. In the past few years, GCN has emerged as an important tool for skeleton-based action recognition. Therefore, we conduct a survey using GCN methods for action recognition. Herein, we present a comprehensive overview of recent GCN techniques for action recognition, propose a taxonomy for the categorization of GCN techniques for action recognition, carry out a detailed study of the benchmark datasets, enlist relevant resources and open-source codes, and finally provide an outline for future research directions and trends. To the best of authors' knowledge, this is the first survey for action recognition using GCN techniques.

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

Human detection and action recognition form the basis for understanding human behaviors. Human detection is used to detect the positions of humans, and action recognition is able to recognize the action of specific humans. However, numerous approaches have been used to handle action recognition and human detection separately. Therefore, three main issues still exist when independent methods of human detection and action recognition are combined, 1) intrinsic errors in object detection impact the performance of action recognition, 2) features common to action recognition and object detection are missed, 3) the combination also has an impact on processing speed. We propose a single framework for human detection and action recognition to solve these issues. It is based on a hierarchical structure called Boosted Randomized Trees. The nodes are trained such that the upper nodes detect humans from the background, while the lower nodes recognize action. We were able to improve both human detection and action recognition rates over earlier hierarchical structure approaches with the proposed method.

Human activity recognition is one of the most challenging research areas in human-machine interaction. Activity recognition by wearable sensors produces significant information about a person's functional ability and lifestyle. In this paper, we present an inertial sensors-based approach for activity and postural transition recognition. Efficient features are extracted from the sensor data. Then, the features are normalized using Z-score followed by Generalized Discriminant Analysis (GDA) to make them more robust. Finally, the features are trained with a Recurrent Neural Network (RNN) for activity and postural transition recognition. The proposed method recognizes 12 activities with an overall accuracy of 97%. In the activity recognition system, the multimodal input data is collected from only one accelerometer and one gyroscope sensor embedded in the user's smartphone. The proposed system can contribute in research fields such as human-robot interaction for practical applications such as eldercare in smart homes.