Multiclass autoencoder-based active learning for sensor-based human activity recognition

Human activity recognition (HAR) identifies people’s motions and actions in daily life. HAR research has grown with the popularity of internet-connected, wearable sensors that capture human movement data to detect activities. Recent deep learning advances have enabled more HAR research and applications using data from wearable devices. However, prior HAR research often focused on a few sensor locations on the body. Recognizing real-world activities poses challenges when device positioning is uncontrolled or initial user training data are unavailable. This research analyzes the feasibility of deep learning models for both position-dependent and position-independent HAR. We introduce an advanced residual deep learning model called Att-ResBiGRU, which excels at accurate position-dependent HAR and delivers excellent performance for position-independent HAR. We evaluate this model using three public HAR datasets: Opportunity, PAMAP2, and REALWORLD16. Comparisons are made to previously published deep learning architectures for addressing HAR challenges. The proposed Att-ResBiGRU model outperforms existing techniques in accuracy, cross-entropy loss, and F1-score across all three datasets. We assess the model using k-fold cross-validation. The Att-ResBiGRU achieves F1-scores of 86.69%, 96.23%, and 96.44% on the PAMAP2, REALWORLD16, and Opportunity datasets, surpassing state-of-the-art models across all datasets. Our experiments and analysis demonstrate the exceptional performance of the Att-ResBiGRU model for HAR applications.

Recognizing human activity from highly sparse body sensor data is becoming an important problem in Internet of Medical Things (IoMT) industry. As IoMT currently uses batteryless or passive wearable body sensors for activity recognition, the data signals of these sensors have a high sparsity, which means that the time intervals of sensors’ readings are irregular and the number of sensors’ readings in time unit is frequently limited. Therefore, learning activity recognition models from temporally sparse data is challenging. Traditional machine learning techniques cannot be applicable in this scenario due to their focus on a single sensing modality requiring regular sampling rate. In this work, we propose an effective end-to-end deep neural network (DNN) model to recognize human activities from temporally sparse data signals of passive wearable sensors that improve the accuracy rate of human activity recognition. A dropout technique is used in the developed model to deal with sparsity problem and avoiding overfitting problem. In addition, optimization of the proposed DNN model was performed by evaluating a different number of hidden layers. Various experiments were conducted on a public clinical room dataset of sparse data signals to compare the performance of the proposed DNN model with the conventional and other deep learning approaches. The experimental results demonstrate that the proposed DNN model outperforms the existing state-of-the-art methods in terms of lower inference delay and activity recognition accuracy.

Neural networks have recently been shown to be highly effective at predicting links for constructing knowledge graphs. Existing research has mainly focused on designing 1) deep neural network models that are expressive in capturing fine-grained semantics, e.g., NTN and ConvE, but that are however less scalable; or 2) shallow models that are scalable, e.g., TransE and DistMult, yet limited in capturing expressive semantic features. In this work, we demonstrate that we can get the best of both worlds while drastically reducing the amount of data needed to train a deep network by leveraging active learning. We present a novel deep active learning framework, ActiveLink, which can be applied to actively train any neural link predictor. Inspired by recent advances in Bayesian deep learning, ActiveLink takes a Bayesian view on neural link predictors, thereby enabling uncertainty sampling for deep active learning. ActiveLink extends uncertainty sampling by exploiting the underlying structure of the knowledge graph, i.e., links between entities, to improve sampling effectiveness. To accelerate model training, ActiveLink further adopts an incremental training method that allows deep neural networks to be incrementally trained while optimizing their generalizability at each iteration. Extensive validation on real-world datasets shows that ActiveLink is able to match state-of-the-art approaches while requiring only 20% of the original training data.

A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices

Deep Learning methods have become very attractive in the wider, wearables-based human activity recognition (HAR) research community. The majority of models are based on either convolutional or explicitly temporal models, or combinations of both. In this paper we introduce attention models into HAR research as a data driven approach for exploring relevant temporal context. Attention models learn a set of weights over input data, which we leverage to weight the temporal context being considered to model each sensor reading. We construct attention models for HAR by adding attention layers to a state-of-the-art deep learning HAR model (DeepConvLSTM) and evaluate our approach on benchmark datasets achieving significant increase in performance. Finally, we visualize the learned weights to better understand what constitutes relevant temporal context.

Human activity recognition (HAR) is a main research field of context-aware computing; the performance of HAR mainly depends on the feature extraction method and classification algorithm. Extreme learning machine (ELM) is a single hidden layer neural network, which has better classification and generalization ability. However, ELM is not suitable for feature extraction. Deep learning is a hot research field as it can automatically extract significant features from raw data. In this paper, we propose an approach: an ELM-based deep model, which combined convolutional neural network (CNN), multilayer ELM (ML-ELM) as feature extractor, and used kernel ELM (KELM) as classifier. We used CNN and ML-ELM to extract significant features, and used KELM to achieve stable performance. The performance of proposed approach is validated on two public HAR datasets, and the experimental results show that the proposed approach is effective.

Human Activity Recognition (HAR) based on data from wearable sensors has become an attractive research topic thanks to its applications in different fields such as healthcare and smart environments. Recently, the advancement of deep learning with capability to perform automatically high-level feature extraction has achieved promising results. However, the performance of the deep learning models depends deeply on the characteristics of the datasets such as the number of classes, the inter-similarity and intra-variation. Therefore, directly comparing these models has become difficult since a wide variety of experimental protocols, evaluation metrics, and datasets are employed. In this paper, for the first time, a systematic evaluation of several deep learning models for HAR from wearable sensors is provided. In particular, three models named Convolutional Neural Network (CNN) [1], DeepConvLSTM - a combination of CNN and Long Short Term Memory (LSTM) [2], and SensCapsNet - a Capsule Neural Network for wearable sensor-based HAR [3] were implemented and evaluated on three benchmark datasets that are 19NonSens, CMDFall, and UCI-HAR dataset. Moreover, to have an intuitive explanation of deep learning models, a visualization of features learnt from these models is given. The evaluation codebase and results will be made publicly available for community use.

The enormous applicability of advanced human-computer interaction technology in daily life has facilitated the interest of the researchers toward the development of more intelligent autonomous systems. These human-computer interaction systems can be successful in real-life by addressing the gap among the existing techniques. This research work focuses on one of the prominent human-computer interaction applications of human activity recognition. Human activity recognition (HAR) is the process to detect human gestures, actions, and different types of interactions. The HAR process requires competent knowledge about the day to day human activities and advanced technology to recognize their activities. The conventional pattern recognition techniques are significant to recognize the human activities using the machine learning techniques but only within the controlled environment for the recognition of limited actions. In recent years, deep learning techniques are developed that can learn the deep attributes of the problem application and determine the outcomes with promising performance. The present paper has presented a systematic review of the deep learning models for video-based human activity recognition. The work describes the recent developments in the field for the analysis of different models. The paper also discusses the process of human activity recognition and the eminent datasets available for experimentation. The summarization of the work is illustrated with the future directions in the field.KeywordsDeep learningHuman computer interactionHuman activity recognitionVision based human activity recognitionVideo surveillanceVideo dataset

Clinicians lack objective means for monitoring if their knee osteoarthritis patients are improving outside of the clinic (e.g., at home). Previous human activity recognition (HAR) models using wearable sensor data have only used data from healthy people and such models are typically imprecise for people who have medical conditions affecting movement. HAR models designed for people with knee osteoarthritis have classified rehabilitation exercises but not the clinically relevant activities of transitioning from a chair, negotiating stairs and walking, which are commonly monitored for improvement during therapy for this condition. Therefore, it is unknown if a HAR model trained on data from people who have knee osteoarthritis can be accurate in classifying these three clinically relevant activities. Therefore, we collected inertial measurement unit (IMU) data from 18 participants with knee osteoarthritis and trained convolutional neural network models to identify chair, stairs and walking activities, and phases. The model accuracy was 85% at the first level of classification (activity), 89–97% at the second (direction of movement) and 60–67% at the third level (phase). This study is the first proof-of-concept that an accurate HAR system can be developed using IMU data from people with knee osteoarthritis to classify activities and phases of activities.

Human action recognition is a fundamental research problem in computer vision. The accuracy of human action recognition has important applications. In this book chapter, the authors use a YOLOv7-based model for human action recognition. To evaluate the performance of the model, the action recognition results of YOLOv7 were compared with those using CNN+LSTM, YOLOv5, and YOLOv4. Furthermore, a small human action dataset suitable for YOLO model training is designed. This data set is composed of images extracted from KTH, Weizmann, MSR data sets. In this book chapter, the authors make use of this data set to verify the experimental results. The final experimental results show that using the YOLOv7 model for human action recognition is very convenient and effective, compared with the previous YOLO model.

Human activity recognition (HAR) is one of the leading research fields in ubiquitous computing working to integrate seamless technologies in our daily lives. The researches in this field focus on the technological advancement of fine activity recognition through using minimal technological deployments along with consideration of human factors. The domain specific knowledge of human activity recognition has shed significant importance on the activity data processing, due to the innate random nature of the activity data. The necessity for improved prediction accuracy of activities within limited infrastructural support and cost is necessary for the research works to be conducted at large. The data set utilized in this study is modified through tree-based feature-engineering method, which has significantly impacted the intrinsic time-series pattern associated with the original data set acquired from UCI machine learning repository. In this paper, the primary focus lies on improving classification accuracy of the human activities included in the data set through utilizing deep learning classifier models. The particular modified pattern of the data devoid of time-series pattern to major extent has resulted in lower classification accuracy by benchmark time-series classification models namely Keras-LSTM and RNN-LSTM. CNN based networks have demonstrated consisted improved training performance on the data set. The state-of-the-art classifier ResNet introduced by Microsoft Research in 2015 has achieved 99.9% classification accuracy on the data set of this study. Another important aim of this study is to infer the risk attached with adversarial attack on machine learning models. The high vulnerability of machine learning models in face of adversarial attacks has emerged as security concern of the deployment pipeline of machine learning models. To the author’s best knowledge, there has been no prior study to find out the high insecurity associated with the human activity recognition data set and concerned benchmark models. In this study, two prevalent adversarial approaches namely Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) have been employed to carry out non-targeted attack on the pre-trained ResNet model. The accuracy loss by the adversarial attack over a small range of added perturbation is tremendous, nearly 96% accuracy is diminished in the process. Finally, we have demonstrated the comparative performance by FGSM and BIM on reducing the classification accuracy of pre-trained model. The findings shed light on the nature of the data influencing the classification accuracy and the resiliency of state-of-the-art classifier model on facing adversarial attacks.

Human activity recognition is an important and challenging topic for computer vision research community. Action representation using deep learning models are currently the dominant technique compared with other methods. However, supervised convolutional neural networks require large computational and memory resources to optimize their parameters. Recently, a simple unsupervised deep learning architecture Principal Component Analysis Network (PCANet) has emerged as an alternative of Convolutional Neural Networks (CNNs) and has significant accomplishments in various vision applications. Meanwhile, encoding and representation techniques using Bag of Words (BoW) and Vector of Locally Aggregated Descriptors (VLAD) have demonstrated great success for several visual tasks specifically in activity recognition. This work presents a novel human activity recognition technique by combining global and local features of PCANet with BoW and VLAD encoding schemes. Both global and local features are learned by PCANet utilizing selected frames from each action video. After that the dimensionality of these features is decreased via Whitening PCA (WPCA). Then encoding schemes are applied on both features to represent the final descriptors for each action. Ultimately, Support Vector Machines classifier (SVM) is trained for recognition process. Several experiments are conducted on UCF sports dataset to evaluate our method. All experimental results utilizing leave-one-out-cross validation (LOOCV) strategy are satisfactorv and comparable.

Data annotation for training of supervised learning algorithms has been a very costly procedure. The aim of deep active learning methodologies is to acquire the highest performance in supervised deep learning models by annotating as few data points as possible. As the feature space of data grows, the application of linear models in active learning settings has become insufficient. Therefore, Deep Bayesian Active Learning methodology which represents model uncertainty has been widely studied. In this paper, a study has been conducted in order to increase the performance of Deep Bayesian Active Learning method. Feature dimensionality reduction is performed on data set by using Variational Autoencoder model. Low dimensional data is used to train a Bayesian Multi Layer Perceptron. The proposed method outperformed the Bayesian Multi Layer Perceptron model which is trained on entire feature space in terms of accuracy performance. The accuracy of the proposed method is tested on baseline datasets.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies.

Human activity recognition (HAR) is one of the important research areas in pervasive computing. Among HAR, sensor-based activity recognition refers to acquiring a high-level knowledge about human activities from readings of many low-level sensor. In recent years, although traditional methods of deep learning (DL) have been widely used for sensor-based HAR with some good performance, they still face such challenges as feature extraction and characterization, continuous action segmentation in dealing with time series problems. In this study, a multichannel fusion model is proposed by the idea of dividing. In this proposed architecture, a multichannel convolutional neural network (CNN) is used to enhance the ability to extract features at different scales, and then the fused features are fed into gated recurrent unit (GRU) for feature labeling and enhanced feature representation, through the learning of temporal relationships. Finally, the multichannel CNN-GRU model is designed using global average pooling (GAP) to connect the feature maps with the final classification. The model performance was conducted on three benchmark datasets of WISDM, UCI-HAR, and PAMAP2 with the accuracy of 96.41%, 96.67%, and 96.25% respectively. The results show that the proposed model demonstrates better activity detection capability than some of the reported results.