The Layer-Wise Training Convolutional Neural Networks Using Local Loss for Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) has become one of the most important research fields to achieve real-time monitoring of human activities for timely decision making in various applications like fall detection, elderly care etc. Now-a-days, most people use smartphones which come with various embedded inertial sensors like accelerometer and gyroscope to monitor acceleration and angular velocity. These smartphone-based sensors have proven to be cost-effective solution in identification of activities belonging to ADL (Activities of Daily Living). Various Machine Learning, Deep learning and hybrid models have been proposed and implemented for HAR. This paper also proposes various hybrid deep learning approaches which combine Deep Neural Networks with other models like LSTM (Long Short Term Memory) Model and GRU (Gated Recurrent Unit) for effective classification of engineered features from CNN (Convolutional Neural Network) Model. A novel architecture that integrates CNN with Random Forest Classifier (DeepCNN-RF) is proposed to add randomness to the model. The proposed models have been tested on publicly available HAR Datasets like UCI HAR and WISDM Activity Recognition Datasets. Experimental results show that the hybrid models outperform the state-of-the-art data mining, machine learning techniques in UCI HAR and WISDM with an overall maximum accuracy of 97.77% and 98.2% respectively.

Federated Learning (FL) has recently attracted great interest in sensor-based human activity recognition (HAR) tasks. However, in real-world environment, sensor data on devices is non-independently and identically distributed (Non-IID), e.g., activity data recorded by most devices is sparse, and sensor data distribution for each client may be inconsistent. As a result, the traditional FL methods in the heterogeneous environment may incur a drifted global model that causes slow convergence and a heavy communication burden. Although some FL methods are gradually being applied to HAR, they are designed for overly ideal scenarios and do not address such Non-IID problem in the real-world setting. It is still a question whether they can be applied to cross-device FL. To tackle this challenge, we propose ProtoHAR, a prototype-guided FL framework for HAR, which aims to decouple the representation and classifier in the heterogeneous FL setting efficiently. It leverages the global prototype to correct the activity feature representation to make the prototype knowledge flow among clients without leaking privacy while solving a better classifier to avoid excessive drift of the local model in personalized training. Extensive experiments are conducted on four publicly available datasets: USC-HAD, UNIMIB-SHAR, PAMAP2, and HARBOX, which are collected in both controlled environments and real-world scenarios. The results show that compared with the state-of-the-art FL algorithms, ProtoHAR achieves the best performance and faster convergence speed in HAR datasets.

Novel Human Activity Recognition by graph engineered ensemble deep learning model

An increase in life expectancy is leading to an aging society, where gradual degradation in physical/cognitive health could result in fall injuries, cardiovascular, neurological/psychological problems, or a sedentary lifestyle. Sensor-based human activity recognition (HAR) is found to be a potential assistive technology in this context to improve the quality of life of an aging society. This chapter presents the state of the art and future trends of Internet of Things (IoT)-enabled sensor data-based Human Activity Recognition (HAR) systems, where machine learning (ML)/deep learning (DL) is used to classify human actions in the context of healthcare applications. In particular, the impact of IoT on the healthcare industry is so immense that a dedicated specialization, termed as Internet of Healthcare Things (IoHT), has emerged. Smart healthcare systems are equipped with different categories of sensors (wearable, wireless, and ambient). The huge amount of sensor data acquired by IoHT sensors allows for performing accurate HAR. The HAR involves detection/classification problems, e.g. fall detection and seizer detection, where ML is used extensively, at its heart. A lot of ML/DL techniques are being used in HAR including support vector machine (SVM), artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM), etc. However, there are a few special aspects of HAR sensor data that make it difficult to handle. Involved research is still being pursued across the globe to solve these problems.KeywordsIoTIoHTHealth careHARSensorMachine learning (ML)Deep learning (DL)

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.

Recent advancement in smart phones and computing technologies has played a vital role in people’s life. Develop a model to detect the human basic dynamic activities such as Amble, Climb stairs, coming down the stairs into the floor and human basic static activities like Sitting, Standing or Laying using the person’s smart phone and computers are the major work of this paper. Conventional Machine learning models like Logistic Regression, SVC, Decision tree, etc. results are compared with a recurrent deep neural network model named as Long Short Term Memory (LSTM). LSTM is proposed to detect the human behavior based on Human Activity Recognition (HAR) dataset. The data is monitored and recorded with the aid of sensors like accelerometer and Gyroscope in the user smart phone. HAR dataset is collected from 30 persons, performing different activities with a smart phone to their waists. The testing of the model is evaluated with respect to accuracy and efficiency. The designed activity recognition system can be manipulated in other activities like predicting abnormal human actions, disease by human actions, etc. The overall accuracy has improved to 95.40%.

Learning underlying patterns from sensory data is crucial in the Human Activity Recognition (HAR) task to avoid poor generalization when coping with unseen data. A key solution to such an issue is representation learning, which becomes essential when input signals contain activities with similar patterns or when patterns generated by different subjects for the same activity vary. To address these issues, we seek a solution to increase generalization by learning the underlying factors of each sensor signal. We develop a novel multi-channel asymmetric auto-encoder to recreate input signals precisely and extract indicative unsupervised futures. Further, we investigate the role of various activation functions in signal reconstruction to ensure the model preserves the patterns of each activity in the output. Our main contribution is that we propose a multi-task learning model to enhance representation learning through shared layers between signal reconstruction and the HAR task to improve the robustness of the model in coping with users not included in the training phase. The proposed model learns shared features between different tasks that are indeed the underlying factors of each input signal. We validate our multi-task learning model using several publicly available HAR datasets, UCI-HAR, MHealth, PAMAP2, and USC-HAD, and an in-house alpine skiing dataset collected in the wild, where our model achieved 99%, 99%, 95%, 88%, and 92% accuracy. Our proposed method shows consistent performance and good generalization on all the datasets compared to the state of the art.

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.

Many human activities consist of physical gestures that tend to be performed in certain sequences. Wearable inertial sensor data have as a consequence been employed to automatically detect human activities, lately predominantly with deep learning methods. This article focuses on the necessity of recurrent layers—more specifically Long Short-Term Memory (LSTM) layers—in common Deep Learning architectures for Human Activity Recognition (HAR). Our experimental pipeline investigates the effects of employing none, one, or two LSTM layers, as well as different layers' sizes, within the popular DeepConvLSTM architecture. We evaluate the architecture's performance on five well-known activity recognition datasets and provide an in-depth analysis of the per-class results, showing trends which type of activities or datasets profit the most from the removal of LSTM layers. For 4 out of 5 datasets, an altered architecture with one LSTM layer produces the best prediction results. In our previous work we already investigated the impact of a 2-layered LSTM when dealing with sequential activity data. Extending upon this, we now propose a metric, rGP, which aims to measure the effectiveness of learned temporal patterns for a dataset and can be used as a decision metric whether to include recurrent layers into a network at all. Even for datasets including activities without explicit temporal processes, the rGP can be high, suggesting that temporal patterns were learned, and consequently convolutional networks are being outperformed by networks including recurrent layers. We conclude this article by putting forward the question to what degree popular HAR datasets contain unwanted temporal dependencies, which if not taken care of, can benefit networks in achieving high benchmark scores and give a false sense of overall generability to a real-world setting.

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.

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 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 action recognition has been a popular issue recently because it can be applied in many applications such as intelligent surveillance systems, human-robot interaction, and autonomous vehicle control. Human action recognition using RGB video is a challenging task because the learning of actions is easily affected by the cluttered background. To cope with this problem, the proposed method estimates 3D human poses first which can help remove the cluttered background and focus on the human body. In addition to the human poses, the proposed method also utilizes appearance features nearby the predicted joints to make our action prediction context-aware. Instead of using 3D convolutional neural networks as many action recognition approaches did, the proposed method uses a two-stream architecture that aggregates the results from skeleton-based and appearance-based approaches to do action recognition. Experimental results show that the proposed method achieved state-of-the-art performance on NTU RGB+D which is a large-scale dataset for human action recognition.

A survey on wearable sensor modality centred human activity recognition in health care

Obtaining robust feature representations from multi-position wearable sensory data is challenging in human activity recognition (HAR) since data from different positions can have unordered implicit correlations. Graph neural networks (GNNs) represent data as structured graphs by mining complex relationships and interdependency via message passing between the nodes of graphs. This paper proposes a novel framework (MhaGNN) that combines GNNs and the multi-head attention mechanism, aiming to learn more informative representations for multi-position HAR tasks. The MhaGNN framework takes the sensor channels from multiple wearing positions as nodes to construct graph-structured data from the spatial dimension. Besides, the multi-head attention mechanism is introduced to complete the message passing and aggregation of the graphs for spatial-temporal feature extraction. The MhaGNN learns correlations among sensor channels that can be used as compensatory features together with the captured features from each single sensor channel to enhance HAR. Experimental evaluations on three publicly available HAR datasets and a ground-truth dataset demonstrate that our proposed MhaGNN achieves state-of-the-art recognition performance with the captured rich features, including PAMAP2, OPPORTUNITY, MHAEATH and MPWHAR.