Human Activities - Science topic

This preprint introduces the Biodiversity Sustainability Equation (BSE), a mathematical model for quantifying the impact of human activities on global biodiversity. The model is designed to assess sustainability and aid in developing effective conservation strategies.

In recent years, the research on human activity recognition based on channel state information (CSI) of Wi-Fi has gradually attracted much attention in order to avoid the deployment of additional devices and reduce the risk of personal privacy leakage. In this paper, we propose a hybrid network architecture, named VBCNet, that can effectively ident...

With the increasing availability of wearable devices for data collection, studies in human activity recognition have gained significant popularity. These studies report high accuracies on k-fold cross validation, which is not reflective of their generalization performance but is a result of the inappropriate split of testing and training datasets,...

The long-term characteristics of phytoplankton blooms and the relative importance of driving factors in the Yangtze River Estuary (YRE) and its adjacent waters remains unclear. This study explored the temporal and spatial patterns of blooms and their driving factors in the YRE and its adjacent waters using MODIS bloom data from 2003 to 2020. Bloom...

Fatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to leverage Human Activity Recognition (HAR) through de...

Recognition of human activity is an active research area. It uses the Internet of Things, Sensory methods, Machine Learning, and Deep Learning techniques to assist various application fields like home monitoring, robotics, surveillance, and healthcare. However, researchers face problems such as time complexity, more execution time of the model, and...

Carbon use efficiency (CUE) and water use efficiency (WUE) are key metrics for quantifying the coupling between terrestrial ecosystem carbon and water cycles. The impacts of intensifying climate change and human activities on carbon and water fluxes in Central Asian vegetation remain unclear. In this study, the CUE and WUE in Central Asia from 2001...

Tick-borne encephalitis virus (TBEV) is the etiological agent of tick-borne encephalitis. TBEV is maintained in nature by the ixodid ticks Ixodes ricinus and I. persulcatus, which are its hosts and main vectors. The classification distinguishes three TBEV subtypes: Far Eastern, European (TBEV-Eu) and Siberian. Each subtype is characterized by its o...

Ultra-wideband radar technology (UWB) has demonstrated its vital role through various applications in surveillance, search and rescue, health monitoring, and the military. Unlike conventional radars, UWB radars use high-frequency, wide-bandwidth pulses, enabling long-range detection and penetrating obstacles. This work presents an in-depth review o...

This paper proposes an energy-efficient federated learning method and its application in human activity monitoring and recognition. In the proposed approach, the device that needs a model for an application requests its nearby devices for collaboration. The nearby devices that accept the request, send their model updates to the requesting device. T...

As a typical ecologically fragile region in the north of China, ecosystems in western Jilin Province have been severely damaged by a combination of natural factors and human activities. Ecological restoration sites need to be identified and viable strategies need to be developed to maximize the restoration of ecosystem functions and enhance human w...

Understanding streamflow behavior under the influence of climate change and human activities is crucial for developing adaptation strategies and policies for water resource planning and management. However, detecting natural or negligible impact periods is challenging. We aimed to distinguish human impacts on streamflow changes during the affected...

With the intensification of climate change and anthropogenic impacts, the ecological environment in drylands faces serious challenges, underscoring the necessity for regionally adapted ecological quality evaluation. This study evaluates the suitability of the original Remote Sensing Ecological Index (oRSEI), modified RSEI (mRSEI), and adapted RSEI...

Effective use of species distribution models can assess the risk of spreading forest pests. In this study, based on 434 occurrence records and eight environmental variables, an ensemble model was applied to identify key environmental factors affecting the distribution of Apriona rugicollis Chevrolat, 1852 and predict its potential habitat and its r...

Multivariate time series (MTS) classification is widely applied in fields such as industry, healthcare, and finance, aiming to extract key features from complex time series data for accurate decision-making and prediction. However, existing methods for MTS often struggle due to the challenges of effectively modeling high-dimensional data and the la...

A floresta amazônica é a maior floresta tropical do mundo e casa da maior biodiversidade do planeta. Sua conservação é questão de enorme importância para toda a humanidade. As queimadas florestais, naturais ou causadas por agentes humanos, são a maior ameaça desse ecossistema. Isso levanta a seguinte questão: como encontrar alternativas eficazes na...

Net ecosystem productivity (NEP) plays a vital role in quantifying the carbon exchange between the atmosphere and terrestrial ecosystems. Understanding the effects of dominant driving forces and their respective contribution rates on NEP can aid in the effective management of terrestrial carbon sinks, especially in rapidly urbanizing coastal areas...

Human activity recognition (HAR) in real-world settings has gained significance due to the growth of Internet of Things (IoT) devices such as smartphones and smartwatches. Nonetheless, limitations such as fluctuating environmental conditions and intricate behavioral patterns have impacted the accuracy of the current procedures. This research introd...

In recent decades, the Loess Plateau has undergone rapid urbanization alongside extensive afforestation efforts aimed at controlling soil erosion. These large-scale land use changes have inevitably affected the region’s hydrological cycle. Despite these changes, the impact on groundwater has not been thoroughly investigated. This study aims to exam...

As of today, state-of-the-art activity recognition from wearable sensors relies on algorithms being trained to classify fixed windows of data. In contrast, video-based Human Activity Recognition, known as Temporal Action Localization (TAL), has followed a segment-based prediction approach, localizing activity segments in a timeline of arbitrary len...

In this work, we tackle the problem of performing multi-label classification in the case of extremely heterogeneous data and with decentralized Machine Learning. Solving this issue is very important in IoT scenarios, where data coming from various sources, collected by heterogeneous devices, serve the learning of a distributed ML model through Fede...

Waste is the result of human activities and comes from a natural process, where waste often causes problems, especially in public places. The market is an important means of trade for the community so that it has the potential to produce quite a lot of waste. The market is a certain place, where sellers and buyers meet, including facilities where s...

Climate change and human activities were identified as the primary drivers of streamflow in arid alpine regions. However, limitations in observational data have resulted in a limited understanding of streamflow changes in these water sources, which hinders efforts to adapt to ongoing climate change and to formulate effective streamflow management p...

The middle reaches of the Yellow River Basin (MYRB) are known for their significant soil erosion and fragile ecological environment, where vegetation growth is important. However, the vegetation’s reaction to climate change (CC) and human activity (HA), and the potential driving mechanisms underlying such changes in the MYRB, have not yet been clar...

Cross-domain generalization is an open problem in WiFi-based sensing due to variations in environments, devices, and subjects, causing domain shifts in channel state information. To address this, we propose Domain-Adversarial Test-Time Adaptation (DATTA), a novel framework combining domain-adversarial training (DAT), test-time adaptation (TTA), and...

Human activity recognition (HAR) technology is related to human safety and convenience, making it crucial for it to infer human activity accurately. Furthermore, it must consume low power at all times when detecting human activity and be inexpensive to operate. For this purpose, a low-power and lightweight design of the HAR system is essential. In...

Human Activity Recognition (HAR) is a vital technology in domains such as healthcare, fitness, and smart environments. This paper presents an innovative HAR system that leverages machine-learning algorithms deployed on the B-L475E-IOT01A Discovery Kit, a highly efficient microcontroller platform designed for low-power, real-time applications. The s...

The main challenges in smart home systems and cyber-physical systems come from not having enough data and unclear interpretation; thus, there is still a lot to be done in this field. In this work, we propose a practical approach called Discrete Human Activity Recognition (DiscHAR) based on prior research to enhance Human Activity Recognition (HAR)....

Human Activity Recognition (HAR) is crucial in healthcare monitoring and smart home systems, tracking patient movements, de- tecting falls, and monitoring daily activities. Despite its importance, HAR faces significant challenges due to the scarcity of large-scale, diverse datasets and the lack of data representing abnormal activities, essential fo...

The rapidly advancing Convolutional Neural Networks (CNNs) have brought about a paradigm shift in various computer vision tasks, while also garnering increasing interest and application in sensor-based Human Activity Recognition (HAR) efforts. However, the significant computational demands and memory requirements hinder the practical deployment of...

Human Activity Recognition (HAR) is an important area of research due to its applications in health monitoring, elderly care, and personal fitness tracking. The challenge is deploying efficient and accurate HAR systems on resource-constrained and/or battery-powered embedded devices, which require low power consumption and processing efficiency. Thi...

Because of the rapid development of smartphone sensor technology and the continuous progress of machine learning algorithms, it has become possible to use smartphones for human activity recognition. Sensors such as accelerometers, gyroscopes, and magnetometers built into smartphones are able to collect human motion data in different activities, whi...

The rise of mobile communication, low-power chips, and the Internet of Things has made smartwatches increasingly popular. Equipped with inertial measurement units (IMUs), these devices can recognize user activities through artificial intelligence (AI) analysis of sensor data. However, most existing AI-based activity recognition algorithms require s...

Aromia bungii is a pest that interferes with the health of forests and hinders the development of the fruit tree industry, and its spread is influenced by changes in abiotic factors and human activities. Therefore, exploring their spatial distribution patterns and potential distribution areas under such conditions is crucial for maintaining forest...

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/ri...

This study assesses the impact of tourism and human activities on greenhouse gas (GHG) emissions and water quality at Kenyir Lake, Terengganu. Kenyir Lake, a tourist destination in Malaysia, faces environmental challenges due to increased anthropogenic activities. The research quantifies GHG emissions from various sources such as forest land manage...

Clarifying the relationship between human activities and the provision of ecosystem services has received significant interest in recent years because of a growing need for sustainable socio-ecological system development. Using multi-source remote sensing data, we assessed the spatial and temporal distribution of the human footprint index and five...

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 d...

Ecosystem health refers to a state where the interactions and relationships among the internal components of an ecosystem and its external environment are in a balanced and stable condition. A healthy ecosystem can maintain its structure and functions, possessing the capacity for self-regulation, self-repair, and resilience to external disturbances...

This study, leverages the Living Lab research platform to explore the variances in thermal comfort between dynamic and static activities in garden spaces during autonomous activity states. Volunteers participated by completing thermal comfort questionnaires in the Living Lab's garden area. Through monitoring and observation of participants’ activit...

Human activity recognition (HAR) is complex in real time because of varying views, illuminations, backgrounds, and colors. With the current state of the art, deep learning (DL) algorithms are gaining more attention because of their automated feature extraction in contrast to the handcrafted machine learning (ML) methods. In this work, we aim to exp...

With rapid urbanization, the urban heat island (UHI) effect has intensified, posing challenges to human health and ecosystems. This study explores the impact of sunlight exposure areas of artificial structures and human activities on land surface temperature (LST) in Hefei and Xuzhou, using Landsat 9 data, Google imagery, nighttime light data, and...

This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split Learning (FSL) with Differential Privacy (DP) over edge networks. Our FSL-DP framework leverages both accelerometer and gyroscope data, achieving significant improvements in HAR accuracy. The evaluation includes a detailed com...

Human activity often has profound effects on plant growth and evolution. Orchids are the most diverse group of flowering plants and are threatened by habitat fragmentation, over-harvesting, and urbanization. A population of Phaius flavus from Beikengding Mount (BM) in the Fujian Province of China was divided into two patches by road construction. T...

Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as attention-based models and Graph Neural Networks, have made great strides, this work focuses on data aug...

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep sem...

We propose WiFlexFormer, a highly efficient Transformer-based architecture designed for WiFi Channel State Information (CSI)-based person-centric sensing. We benchmark WiFlexFormer against state-of-the-art vision and specialized architectures for processing radio frequency data and demonstrate that it achieves comparable Human Activity Recognition...

Human activity recognition (HAR) for disabled people is a vital research area, which aims to help individuals with disabilities in their daily lives. HAR involves using technology, typically wearable devices or sensors, to automatically identify and classify human activities and movements. HAR using deep learning (DL) is an effective and popular me...

In Human Activity Recognition (HAR), understanding the intricacy of body movements within high-risk applications is essential. This study uses SHapley Additive exPlanations (SHAP) to explain the decision-making process of Graph Convolution Networks (GCNs) when classifying activities with skeleton data. We employ SHAP to explain two real-world datas...

The incorporation of human-computer interface technologies into daily life has garnered the interest of researchers in developing more advanced autonomous systems. Human-computer interaction systems can succeed in actual applications by addressing the shortcomings of existing techniques. This study focuses on a crucial application of human-computer...

Advances in brain–computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled...

Globally, land subsidence (LS) often adversely impacts infrastructure, humans, and the environment. As climate change intensifies the terrestrial hydrologic cycle and severity of climate extremes, the interplay among extremes (e.g., floods, droughts, wildfires, etc.), LS, and their effects must be better understood since LS can alter the impacts of...

Rapid global urbanization has a complex impact on soil organic carbon (SOC) stocks. Through its direct and indirect impacts on soil formation and development, urbanization greatly influences SOC stocks. However, the extent to which urbanization affects SOC stocks globally remains unclear. In this study, we utilized an urban–rural gradient approach...

This study presents the “ESP32 Dataset,” a dataset of radio frequency (RF) data intended for human activity detection. This dataset comprises 10 activities carried out by 8 volunteers in three different indoor floor plan experiment setups. Line-of-sight (LOS) scenarios are represented by the first two experiment setups, and non-line-of-sight (NLOS)...

Net primary production (NPP) serves as a critical proxy for monitoring changes in the global capacity for vegetation carbon sequestration. The assessment of the factors (i.e., human activities and climate changes) influencing NPP is of great value for the study of terrestrial systems. To investigate the influence of factors on grassland NPP, the ec...

Climate variability and human activities are major influences on the hydrological cycle. However, the driving characteristics of hydrological cycle changes and the potential impact on runoff in areas where natural forests have been converted to rubber plantations on a long-term scale remain unclear. Based on this, the Mann–Kendall (MK) and Pettitt...

The deep learning community has increasingly focused on the critical challenges of human activity segmentation and detection based on sensors, which have numerous real-world applications. In most prior efforts, activity segmentation and recognition have been treated as separate processes, relying on pre-segmented sensor streams. This research propo...

This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns associated with the centralized cloud processing....

Water quality degradation and eutrophication of lakes are global ecological and environmental concerns, especially shallow lakes. This study collected hydrochemical data from 2935 samples of the Chinese part of Xingkai (Khanka) Lake, based on 40 published papers spanning the period from 2001 to 2023. Using the water quality index (WQI), improved ge...

The Yarlung Zangbo River Basin (YZRB), situated within the Qinghai‐Tibetan Plateau, has experienced significant alterations due to global warming and vegetation greening. This region serves as a critical indicator of the interplay between vegetation growth and climatic fluctuations, as evidenced by substantial changes in spatiotemporal land surface...

In recent years, the research community has shown a growing interest in the continuous temporal data gathered from motion sensors integrated into wearable devices. This type of data is highly valuable for analyzing human activities in a variety of domains, including surveillance, healthcare, and sports. Various deep-learning models have been develo...

Human Activity Recognition (HAR) plays a critical role in applications such as security surveillance and healthcare. However, existing methods, particularly two-stream models like Inflated 3D (I3D), face significant challenges in real-time applications due to their high computational demand, especially from the optical flow branch. In this work, we...

The Walker circulation is projected to slow down in response to greenhouse gas warming. However, detecting the impact of human activities on changes in the Walker circulation is challenging due to the significant influence of internal variability. Here, based on ensembles of multiple climate models from the Coupled Model Intercomparison Project Pha...