Human Activity Recognition Using Deep Residual Convolutional Network Based on Wearable Sensors.

In recent years, many researchers have focused on using convolutional neural networks to perform human activity recognition as evidenced by the emergence of a number of convolutional neural network architectures such as LeNet-5, AlexNet and VGG16 and modern architectures such as ResNet, Inception V3, Inception-ResNet, MobileNet V2, NASNet and PNASNet. The main characteristic of a convolutional neural network (CNN) is its ability to extract features automatically from input images, which facilitates the processes of activity recognition and classification. Convolutional networks indeed derive more relevant and complex features with every additional layer. In addition, CNNs have achieved perfect classification on highly similar activities that were previously extremely difficult to classify. In this paper, we evaluate modern convolutional neural networks in terms of their human activity recognition accuracy, and we compare the results with the state of the art methods. In our research, we used two public data sets, HMDB (Shooting gun, kicking, falling to the floor, punching) and the Weizman dataset (walking, running, jumping, bending, one hand waving, two-hand waving, jumping in place, jumping jack, skipping). Our experimental results indicated that the CNN with NASNet architecture achieves the best performance of the six CNN architectures on both human activity data sets (HMDB and Weizman).

At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches: the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN’s temporal and spatial feature extraction processes are independent of each other, which means that it is easy to ignore the internal connection, affecting the performance of recognition. Although 3D CNN can extract the temporal and spatial features of the video sequence at the same time, the parameters of the 3D model increase exponentially, resulting in the model being difficult to train and transfer. To solve this problem, this article is based on 3D CNN combined with a residual structure and attention mechanism to improve the existing 3D CNN model, and we propose two types of human action recognition models (the Residual 3D Network (R3D) and Attention Residual 3D Network (AR3D)). Firstly, in this article, we propose a shallow feature extraction module and improve the ordinary 3D residual structure, which reduces the parameters and strengthens the extraction of temporal features. Secondly, we explore the application of the attention mechanism in human action recognition and design a 3D spatio-temporal attention mechanism module to strengthen the extraction of global features of human action. Finally, in order to make full use of the residual structure and attention mechanism, an Attention Residual 3D Network (AR3D) is proposed, and its two fusion strategies and corresponding model structure (AR3D_V1, AR3D_V2) are introduced in detail. Experiments show that the fused structure shows different degrees of performance improvement compared to a single structure.

Hyperparameter optimization poses a significant challenge when developing deep neural networks. Building a convolutional neural network (CNN) for implementation can be an arduous and time-intensive task. This work proposed an approach to optimize the hyperparameters of one dimensional (1D-CNN) to improve the accuracy of human activity recognition (HAR). The framework includes a parametric depiction of 1D-CNNs along with an optimization process for hyperparameters aimed at maximizing the model's performance. This work designed the method called OPTConvNet for hyperparameter optimization of 1D-CNN using Hierarchical Particle Swarm Optimization (H-PSO). The H-PSO algorithm is designed to optimize the architectural, layer and training parameters of 1D-CNN. The H-PSO optimizes the architecture of the 1D-CNN at initial level. Layer and training hyperparameters will be optimized at the next level. The proposed approach employs an exponential-like inertia weight to fine-tune the balance between exploration and exploitation of particles to prevent premature convergence to a local optimum solution in the PSO algorithm. The H-PSO- CNN is evaluated on publicly available sensor- human activity recognition (S-HAR) datasets namely, UCI-HAR, Daphnet Gait, Opportunity and PAMPA2 datasets.

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.

Targeting the problems of the insufficient utilization of temporal and spatial information in videos and a lower accuracy rate, this paper proposes a human action recognition method for dynamic videos of emergency rescue based on a spatial-temporal fusion network. A time domain segmentation strategy based on random sampling maintains the overall time domain structure of the video. Considering the spatial-temporal asynchronous relationship, multiple asynchronous motion sequences are increased as input of the temporal convolutional network. spatial-temporal features are fused in convolutional layers to reduce feature loss. Because time series information is crucial for human action recognition, the acquired mid-layer spatial-temporal fusion features are sent into Bidirectional Long Short-Term Memory (Bi-LSTM) to obtain the human movement features in the whole video temporal dimension. Experiment results show the proposed method fully fuses spatial and temporal dimension information and improves the accuracy of human action recognition in dynamic scenes. It is also faster than traditional methods.

Wearables are objective tools for human activity recognition (HAR). Advances in wearables enable synchronized multi-sensing within a single device. This has resulted in studies investigating the use of single or multiple wearable sensor modalities for HAR. Some studies use inertial data, others use surface electromyography (sEMG) from multiple muscles and different post-processing approaches. Yet, questions remain about accuracies relating to e.g., multi-modal approaches, and sEMG post-processing. Here, we explored how inertial and sEMG could be efficiently combined with machine learning and used with post-processing methods for better HAR. This study aims recognition of four basic daily life activities; walking, standing, stair ascent and descent. Firstly, we created a new feature vector based on the domain knowledge gained from previous mobility studies. Then, a feature level data fusion approach was used to combine inertial and sEMG data. Finally, two supervised learning classifiers (Support Vector Machine, SVM, and the k-Nearest Neighbors, kNN) were tested with 5-fold cross-validation. Results show the use of inertial data with sEMG increased overall accuracy by 3.5% (SVM) and 6.3% (kNN). Extracting features from linear envelopes instead of bandpass filtered sEMG improves overall HAR accuracy in both classifiers. Clinical Relevance- Post-processing on sEMG signals can improve the performance of multimodal HAR.

In order to improve the accuracy of human action recognition and accelerate the recognition speed, we propose a method for human action recognition by modeling the human primary visual cortex neurons. The method firstly extracted motion information by using 3DGabor spatial-temporal filters to model the classical receptive field (CRF) of simple cells in the primary visual cortex. Secondly, conductance-driven integrate and fire neuron model was used to simulate the primary visual cortex neuron, by which motion information was converted into spike train. Finally, the mean firing rate of spike train formed a feature vector that captures the characteristic of human actions in this video sequence. Using Support Vector Machine (SVM), the method is tested on the Weizmann action dataset. The obtained impressive results show that our method was more effective than model of Escobar in human action recognition.

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.

In order to improve the accuracy of human action recognition, a Residual Generative Adversarial Network (Res-GAN) method is proposed by combining the characteristics of Generative Adversarial Networks and Residual Networks. The generator uses an improved residual block extraction feature to obtain deep texture information and enhance the regularization of the model; residual blocks construct the discriminator. On the one hand, the accuracy of image feature extraction is improved, and the convergence speed of the network is accelerated. On the other hand, the discriminator performs the classification pre-training of human behavior recognition while distinguishing true and false samples. In order to avoid the influence of residual generative adversarial network generator on the recognition accuracy due to the instability of the reconstructed samples, the trained residual generative adversarial network discriminator was constructed as a classifier for human behavior recognition. Experiments on the KTH dataset demonstrate the effectiveness of the proposed method.

BackgroundIn digital medicine, human activity recognition (HAR) can be used to track and assess a patient's progress throughout rehabilitation, enhancing the quality of life for the elderly and the disabled.MethodsA patch-type flexible sensor that integrated dynamic electrocardiogram (ECG) and acceleration signal (ACC) was used to record the signals of the various behavioral activities of 20 healthy volunteers and 25 patients with pneumoconiosis. Seven HAR tasks were then carried out on the data using four different deep learning methods (CNN, LSTM, CNN–LSTM and GRU).ResultsWhen ECG and ACC were obtained simultaneously, the overall accuracy rates of HAR for healthy group were 0.9371, 0.8829, 0.9843 and 0.9486 by the CNN, LSTM, CNN–LSTM and GRU models, respectively. In contrast, the overall accuracy rates of HAR for the pneumoconiosis patients’ group were 0.8850, 0.7975, 0.9425 and 0.8525 by the four corresponding models. The accuracy of HAR for both groups using all four models is higher than when only ACC signal is detected.ConclusionThe addition of the ECG signal significantly improves HAR outcomes in the group of healthy individuals, while having relatively less enhancing effects on the group of patients with pneumoconiosis. When ECG and ACC signals were combined, the increase in HAR accuracy was notable compared to cases where no ECG data was provided. These results suggest that the combination of ACC and ECG data can represent a novel method for the clinical application of HAR.

Human Activity Recognition (HAR) is critical in a variety of disciplines, including healthcare and robotics. This paper presents a new Convolutional Neural Network with Bidirectional Long Short-Term Memory and along with Gated Recurrent Unit (CNN-BiLSTM-GRU)hybrid deep learning model designed for Human Activity Recognition (HAR) that makes use of data from wearable sensors and mobile devices. Surprisingly, the model achieves an amazing accuracy rate of 99.7% on the difficult Wireless Sensor Data Mining (WISDM) dataset, demonstrating its ability to properly identify human behaviors. This study emphasizes parameter optimization, with a focus on batch size 0.3 as a significant component in improving the model’s robustness. Furthermore, the findings of this study have far-reaching implications for bipedal robotics, where precise HAR (Human Activity Recognition) is critical to improving human–robot interaction quality and overall work efficiency. These discoveries not only strengthen Human Activity Recognition (HAR) techniques, but also provide practical benefits in real-world applications, particularly in the robotics and healthcare areas. This study thus makes a significant contribution to the continuous development of Human Activity Recognition methods and their actual applications, emphasizing their important role in stimulating innovation and efficiency across a wide range of industries.

Due to rapid development of sensor technology, human activity recognition (HAR) using wearable inertial sensors has recently become a new research hotspot. Deep learning, especially convolutional neural network (CNN) that can automatically learn intricate activity features have gained a lot of attention in ubiquitous HAR task. Most existing CNNs process sensor input by extracting channel-wise features, and the information from each channel can be separately propagated in a hierarchical way from lower layers to higher layers. As a result, they typically overlook information exchange among channels within the same layer. In this article, we first propose a shallow CNN that considers cross-channel communication in HAR scenario, where all channels in the same layer have a comprehensive interaction to capture more discriminative features of sensor input. One channel can communicate with all other channels by graph neural network to remove redundant information accumulated among channels, which is more beneficial for deploying lightweight deep models. Extensive experiments are conducted on multiple benchmark HAR datasets, namely UCI-HAR, OPPORTUNITY, PAMAP2 and UniMib-SHAR, which indicates that the proposed method enables shallower CNNs to aggregate more useful information, and surpasses baseline deep networks and other competitive methods. The inference speed is evaluated via deploying the HAR systems on an embedded system.

Wireless body area network (WBAN) is a novel technology with the incorporation of numerous types of devices, which is also employed in health monitoring applications. Human activity recognition (HAR) receives more interest in recent times along with wearable sensors. HAR system provides information about a person's identity, personality, and psychological state. In the present scenario, it becomes important to model the active learning paradigms with the help of wearable sensors for analysing human activities. Although various deep learning models and existing algorithms secure better outcomes through the sensor data analysis regarding HAR, the decision‐making evaluation seems to be a complex one. The main intent of this paper is to implement the WBAN‐based HAR system using the improved deep learning model. By connecting the wearable sensors, the signals are gathered regarding human activity from diverse benchmark sources. After collecting the required signals, the pre‐processing of the input signals is done using artefact removal and median filtering. Further, the feature extraction is performed, which intends to extract a set of features by utilizing short‐time Fourier transform (STFT) and statistical features. For reducing the feature‐length, a multi‐objective‐based optimal feature selection is adopted. Human activities such as ‘walking, walking upstairs, walking downstairs, sitting, standing, lying, and jogging’ are recognized with help of selected optimal features. The optimized probabilistic neural network (PNN) and convolutional neural network (CNN) are combined and named as adaptive probabilistic‐based CNN (AP‐CNN). The effective performance of optimal feature selection and recognition is accomplished by incorporating the developed backward updating position‐based sea lion optimization algorithm (BU‐SLnO). Finally, the performance of the BU‐SLnO‐AP‐CNN‐based suggested model is analysed, which shows 1.001%, 1.237%, 0.811%, and 0.859% advanced than SLnO‐AP‐CNN, feed‐forward‐AP‐CNN (FF‐AP‐CNN), grey wolf optimization‐AP‐CNN (GWO‐AP‐CNN), particle swarm optimization‐AP‐CNN (PSO‐AP‐CNN) when observing the dataset 1. The experimental outcomes from comparison with various classification techniques demonstrate the efficiency of the developed technique.

Due to the breadth of its application domains, Hu-man Activity Recognition (HAR) is a problematic area of human-computer interaction. HAR can be used in remote monitoring of senior healthcare and concern situations in intelligent man-ufacturing, among other applications. HAR based on wearable inertial sensors has been researched identification efficiency in various kinds of human actions considerably more than vision-based HAR. The sensor-based HAR is generally applicable to indoor and outdoor locations without privacy considerations of implementation. In this research, we explore the recognition performance of multiple deep learning (DL) models to recognize everyday living human activities. We developed a deep residual neural network that employed aggregated multi-branch transformation to boost identification performance. The proposed model is called the ResNeXt model. To evaluate its performance, three standard DL models (CNN, LSTM, and CNN-LSTM) are investigated and compared to our proposed model using a standard HAR dataset called Daily Living Activity dataset. These datasets gathered mobility signal data from multimodal sensors (accelerometer, gyroscope, and magnetometer) in three distinct body areas (wrist, hip, and ankle). The experimental findings reveal that the proposed model surpasses other benchmark DL models with maximum accuracy and F1-scores. Furthermore, the findings show that the ResNeXt model is more resistant than other models with fewer training parameters.

Accurate detection of skin diseases is essential for timely intervention and treatment. This article proposes a patch-based, interpretable deep learning framework for skin disease detection using wearable sensors and clinical data. Specifically, a fully convolutional residual neural network (FCRN) is employed to extract local features from high-resolution skin images captured via wearable sensors, using a patch-level training approach. Pre-processing techniques—including image resampling, intensity normalization, and noise reduction—standardize the input data to ensure consistency across sensor variations. To enhance local feature learning, the FCRN incorporates residual modules, which mitigate gradient vanishing and improve model performance. The framework generates disease probability maps that visualize regions of high diagnostic risk, providing interpretable insights into skin anomalies. In the proposed methodology, a convolutional neural network (CNN) integrates image-derived features with clinical data such as patient demographics, symptoms, and medical history. This CNN-based multimodal fusion approach improves the model’s ability to capture spatial relationships and enhances classification performance. Experimental evaluations demonstrate that the proposed framework achieves state-of-the-art results across multiple evaluation metrics, including accuracy, sensitivity, and specificity. The interpretable disease probability maps highlight affected skin regions, enhancing model transparency and clinical usability. This approach demonstrates the potential of combining wearable sensor technology with deep learning for efficient, scalable, and explainable skin disease detection, laying the foundation for real-time clinical applications.