Explore the vast range of titles available.

\"This book takes an insightful glance into the applications and dependability of behavior detection and looks into the social, ethical, and legal implications of these areas\"--Provided by publisher.

With the world population aging at a fast rate, ambient assisted living systems focused on elderly people gather more attention. Human activity recognition (HAR) is a component connected to those systems, as it allows identification of the actions performed and their utilization on behavioral analysis. This paper aims to provide a review on recent studies focusing on HAR and abnormal behavior detection specifically for seniors. The frameworks proposed in the literature are presented. The results are also discussed and summarized, along with the datasets and metrics used. The absence of a universal evaluation framework makes direct comparison not feasible, thus an analysis is made trying to divide the literature using a taxonomy. Solutions on the challenges identified are proposed, while discussing future work.

The prevention of falls has become crucial in the modern healthcare domain and in society for improving ageing and supporting the daily activities of older people. Falling is mainly related to age and health problems such as muscle, cardiovascular, and locomotive syndrome weakness, etc. Among elderly people, the number of falls is increasing every year, and they can become life-threatening if detected too late. Most of the time, ageing people consume prescription medication after a fall and, in the Japanese community, the prevention of suicide attempts due to taking an overdose is urgent. Many researchers have been working to develop fall detection systems to observe and notify about falls in real-time using handcrafted features and machine learning approaches. Existing methods may face difficulties in achieving a satisfactory performance, such as limited robustness and generality, high computational complexity, light illuminations, data orientation, and camera view issues. We proposed a graph-based spatial-temporal convolutional and attention neural network (GSTCAN) with an attention model to overcome the current challenges and develop an advanced medical technology system. The spatial-temporal convolutional system has recently proven the power of its efficiency and effectiveness in various fields such as human activity recognition and text recognition tasks. In the procedure, we first calculated the motion along the consecutive frame, then constructed a graph and applied a graph-based spatial and temporal convolutional neural network to extract spatial and temporal contextual relationships among the joints. Then, an attention module selected channel-wise effective features. In the same procedure, we repeat it six times as a GSTCAN and then fed the spatial-temporal features to the network. Finally, we applied a softmax function as a classifier and achieved high accuracies of 99.93%, 99.74%, and 99.12% for ImViA, UR-Fall, and FDD datasets, respectively. The high-performance accuracy with three datasets proved the proposed system’s superiority, efficiency, and generality.

PurposeThe emergence of smart wearables using clothing as a technology platform is a significant milestone with considerable implications for industrial convergence, creating new value for fashion. This paper aimed to present a premeditated prototype to integrate a human activity recognition (HAR) system into outdoor clothing.Design/methodology/approachFor the development of wearable HAR (WHAR) clothing, this paper explored three subject areas: fashion design related to the structural feature of the clothing platform, electronics related to wearable circuits and modules design and graphic user interface design related to smartphone application development.FindingsFor WHAR functions in outdoor terrains, the coexistence of accelerometer–gyroscope sensing and distance-sensing could be practical to surpass the technological limitation of activity and posture recognition with gyro sensors highly depending on the changes of acceleration and angles.Research limitations/implicationsThrough the vital sign check and physical activity–change recognition function, this study's WHAR system allows users to check their health by themselves and avoid overwork. A quick rescue is possible manually and automatically in a dangerous situation by notifying others. Thus, it can help protect users' health and safety (life).Originality/valueThis study designed the modularization of HAR functions generally installed in indoor medical spaces. Through the approach, smart clothing–embracing WHAR systems optimized for health and safety care for outdoor environments was pursued to diversify expensive roles of clothing for technological applications.

Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.

Currently, a significant amount of interest is focused on research in the field of Human Activity Recognition (HAR) as a result of the wide variety of its practical uses in real-world applications, such as biometric user identification, health monitoring of the elderly, and surveillance by authorities. The widespread use of wearable sensor devices and the Internet of Things (IoT) has led the topic of HAR to become a significant subject in areas of mobile and ubiquitous computing. In recent years, the most widely-used inference and problem-solving approach in the HAR system has been deep learning. Nevertheless, major challenges exist with regard to the application of HAR for problems in biometric user identification in which various human behaviors can be regarded as types of biometric qualities and used for identifying people. In this research study, a novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented. In order to obtain advanced information regarding users during the performance of various activities, sensory data from tri-axial gyroscopes and tri-axial accelerometers of the wearable devices are applied. Additionally, a set of experiments were shown to validate this work, and the proposed framework’s effectiveness was demonstrated. The results for the two basic models, namely, the Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) deep learning, showed that the highest accuracy for all users was 91.77% and 92.43%, respectively. With regard to the biometric user identification, these are both acceptable levels.

Wearable device based human activity recognition, as an important field of ubiquitous and mobile computing, is drawing more and more attention. Compared with simple human activity (SHA) recognition, complex human activity (CHA) recognition faces more challenges, e.g., various modalities of input and long sequential information. In this paper, we propose a deep learning model named DEBONAIR (Deep lEarning Based multimodal cOmplex humaN Activity Recognition) to address these problems, which is an end-to-end model extracting features systematically. We design specific sub-network architectures for different sensor data and merge the outputs of all sub-networks to extract fusion features. Then, a LSTM network is utilized to learn the sequential information of CHAs. We evaluate the model on two multimodal CHA datasets. The experiment results show that DEBONAIR is significantly better than the state-of-the-art CHA recognition models.

PurposeThis paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups.Design/methodology/approachIn this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%.FindingsThe activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg.Practical implicationsThis work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities.Originality/valueThe data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.

Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments. As the volume of publications in this domain continues to grow, staying abreast of the most pertinent and innovative methodologies can be challenging. This survey provides a comprehensive overview of the state-of-the-art methods employed in HAR, embracing both classical machine learning techniques and their recent advancements. We investigate a plethora of approaches that leverage diverse input modalities including, but not limited to, accelerometer data, video sequences, and audio signals. Recognizing the challenge of navigating the vast and ever-growing HAR literature, we introduce a novel methodology that employs large language models to efficiently filter and pinpoint relevant academic papers. This not only reduces manual effort but also ensures the inclusion of the most influential works. We also provide a taxonomy of the examined literature to enable scholars to have rapid and organized access when studying HAR approaches. Through this survey, we aim to inform researchers and practitioners with a holistic understanding of the current HAR landscape, its evolution, and the promising avenues for future exploration.

Human Activity Recognition (HAR) is a crucial research focus in the body area networks and pervasive computing domains. The goal of HAR is to examine activities from raw sensor data, video sequences, or even images. It aims to classify input data correctly into its underlying category. In the current study, machine and deep learning approaches along with different traditional dimensionality reduction and TDA feature extraction techniques are suggested to solve the HAR problem. Two public datasets (i.e., WISDM and UCI-HAR) are used to conduct the experiments. Different data balancing techniques are utilized to deal with the problem of imbalanced data. Additionally, a sampling mechanism with two overlapping percentages (i.e., 0% and 50%) is applied to each dataset to retrieve four balanced datasets. Five traditional dimensionality reduction techniques in addition to the Topological Data Analysis (TDA) are utilized. Seven machine learning (ML) algorithms are used to perform HAR where six of them are ensemble classifiers. In addition to that, 1D-CNN, BiLSTM, and GRU deep learning approaches are utilized. Three categories of experiments (i.e., ML with traditional features, ML with TDA, and DL) are applied. For the first category experiments, the best-reported scores concerning the WISDM dataset are accuracy and WSM of 99.10% and 86.61%, respectively. When concerning the UCI-HAR dataset, the best-reported scores are accuracy and WSM of 100% and 100%, respectively. For the second category experiments, the best-reported scores concerning the WISDM dataset are accuracy and WSM of 95.34% and 89.62%, respectively. When concerning the UCI-HAR dataset, the best-reported scores are accuracy and WSM of 96.70% and 92.57%, respectively. For the third category experiments, the best-reported scores concerning the WISDM dataset are accuracy and WSM of 99.90% and 99.76%, respectively. When concerning the UCI-HAR dataset, the best-reported scores are accuracy and WSM of 100% and 100%, respectively. After concluding the final results, the suggested approach is compared with 6 related studies utilizing the same dataset(s).