Human Movement Patterns Research Articles

Stochastic recognition of human daily activities via hybrid descriptors and random forest using wearable sensors

Human daily activity recognition (HDAR) using wearable sensors is an important task for researchers aiming to develop an effective and feasible model which is capable of accurately detecting human motion patterns. These applications provide elderly care, surveillance systems, and wellness tracking. Despite the pervasive use, recognition and monitoring of human physical activities remains inaccurate, which may contribute to negative reactions and feedback. This paper addresses a data-driven approach to recognizing human daily activities in an indoor-outdoor environment. To improve the classification and recognition of human life-log activities (for example, walking, drinking, and exercising), a model is introduced that incorporates pre-processing (such as denoising), hybrid features extraction from four domains, including time, frequency, wavelet, and time-frequency respectively. After that, stochastic gradient descent is exploited to optimize the selected features. The optimal extracted features are advanced to random forest classifiers in order to develop adaptive for human life-log activities. Additionally, the proposed HDAR model is experimentally evaluated on three benchmark datasets, namely, USC-HAD, which is comprised of 12 physical activities, IM-WSHA, which involves 11 life-log activities, and MOTIONSENSE which contains six static and dynamic activities, respectively. The experimental results show that the proposed HDAR method significantly achieves better results and outperforms others in terms of recognition rates of 91.08%, 91.45%, and 93.16% respectively, when the USC-HAD, IM-WSHA, and MOTIONSENE databases are applied.

Commuting Analysis of the Budapest Metropolitan Area Using Mobile Network Data

The analysis of human movement patterns based on mobile network data makes it possible to examine a very large population cost-effectively and has led to several discoveries about human dynamics. However, the application of this data source is still not common practice. The goal of this study was to analyze the commuting tendencies of the Budapest Metropolitan Area using mobile network data as a case study and propose an automatized alternative approach to the current, questionnaire-based method, as commuting is predominantly analyzed by the census, which is performed only once in a decade in Hungary. To analyze commuting, the home and work locations of cell phone subscribers were determined based on their appearances during and outside working hours. The detected home locations of the subscribers were compared to census data at a settlement level. Then, the settlement and district level commuting tendencies were identified and compared to the findings of census-based sociological studies. It was found that the commuting analysis based on mobile network data strongly correlated with the census-based findings, even though home and work locations were estimated by statistical methods. All the examined aspects, including commuting from sectors of the agglomeration to the districts of Budapest and the age-group-based distribution of the commuters, showed that mobile network data could be an automatized, fast, cost-effective, and relatively accurate way of analyzing commuting, that could provide a powerful tool for sociologists interested in commuting.

An innovative approach to assess the impact of urban flooding: Modeling transportation system failure due to urban flooding

The transportation system is often considered a critical infrastructure as the failure of its components can have a substantial impact on the economy and social well-being of a country. Extreme weather events such as urban flooding disrupt transportation networks with delays, diversions, and partial or full closure of roads and, thereby, influence human movement patterns. Most studies in the field of transportation network failure due to urban flooding are focused only on micro-scale, in-situ approaches rather than referring to the holistic impact on the entire transportation system. Moreover, such approaches are failed to address the changes in travel behaviour concerning their trip purpose, mode choice, and income which can be changed due to inundation. Hence, this study presents a novel approach to assess the entire transportation system failure due to urban flooding. Consequently, the impact of urban flooding on human movement patterns was assessed. The proposed framework is developed based on the network centrality measures, namely, betweenness centrality and closeness centrality. The study is predominantly based on Open-Source GIS tools and the case study is carried out in Colombo, Sri Lanka which has the highest concentration of population and transportation network. The hotspots of the failure of the network were statistically and spatially illustrated. The study revealed three key findings; (a) the fragmented network due to inundated road segments triggers the spatial changes in the accessibility, particularly decreasing the accessibility in most of the core areas whereas increasing the accessibility in some peripheral areas which are not significant under the baseline condition (2) the functional changes of the entire transport network occurs due to flooding events (3) the considerable changes in the trip length occurs due to the disruption of transport network links and the impact is mostly notable in long-distance trips compared to short distance trips. The proposed framework can be utilized as a planning tool for both disaster practitioners and infrastructure operators in identifying the flood risk; and prioritizing flood mitigation investments, thereby, making the transportation system more robust to extreme flooding.

A spatiotemporal decay model of human mobility when facing large-scale crises

A common feature of large-scale extreme events, such as pandemics, wildfires, and major storms is that, despite their differences in etiology and duration, they significantly change routine human movement patterns. Such changes, which can be major or minor in size and duration and which differ across contexts, affect both the consequences of the events and the ability of governments to mount effective responses. Based on naturally tracked, anonymized mobility behavior from over 90 million people in the United States, we document these mobility differences in space and over time in six large-scale crises, including wildfires, major tropical storms, winter freeze and pandemics. We introduce a model that effectively captures the high-dimensional heterogeneity in human mobility changes following large-scale extreme events. Across five different metrics and regardless of spatial resolution, the changes in human mobility behavior exhibit a consistent hyperbolic decline, a pattern we characterize as "spatiotemporal decay." When applied to the case of COVID-19, our model also uncovers significant disparities in mobility changes-individuals from wealthy areas not only reduce their mobility at higher rates at the start of the pandemic but also maintain the change longer. Residents from lower-income regions show a faster and greater hyperbolic decay, which we suggest may help account for different COVID-19 rates. Our model represents a powerful tool to understand and forecast mobility patterns post emergency, and thus to help produce more effective responses.

Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion.

An exoskeleton is a kind of intelligent wearable device with bioelectronics and biomechanics. To realize its effective assistance to the human body, an exoskeleton needs to recognize the real time movement pattern of the human body in order to make corresponding movements at the right time. However, it is of great difficulty for an exoskeleton to fully identify human motion patterns, which are mainly manifested as incomplete acquisition of lower limb motion information, poor feature extraction ability, and complicated steps. Aiming at the above consideration, the motion mechanisms of human lower limbs have been analyzed in this paper, and a set of wearable bioelectronics devices are introduced based on an electromyography (EMG) sensor and inertial measurement unit (IMU), which help to obtain biological and kinematic information of the lower limb. Then, the Dual Stream convolutional neural network (CNN)-ReliefF was presented to extract features from the fusion sensors’ data, which were input into four different classifiers to obtain the recognition accuracy of human motion patterns. Compared with a single sensor (EMG or IMU) and single stream CNN or manual designed feature extraction methods, the feature extraction based on Dual Stream CNN-ReliefF shows better performance in terms of visualization performance and recognition accuracy. This method was used to extract features from EMG and IMU data of six subjects and input these features into four different classifiers. The motion pattern recognition accuracy of each subject under the four classifiers is above 97%, with the highest average recognition accuracy reaching 99.12%. It can be concluded that the wearable bioelectronics device and Dual Stream CNN-ReliefF feature extraction method proposed in this paper enhanced an exoskeleton’s ability to capture human movement patterns, thus providing optimal assistance to the human body at the appropriate time. Therefore, it can provide a novel approach for improving the human-machine interaction of exoskeletons.

Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model.

More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar. An agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation. Mobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups. Using an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.

Learning to predict diverse trajectory from human motion patterns

Diverse and reasonable multi-modal trajectory prediction of pedestrian is vital for the safety of intelligent autonomous systems. Currently, most of existing methods primarily focus on modeling the extrinsic interactions of pedestrians, but neglect to take advantage of the intrinsic properties of pedestrian motion, which may generate unrealistic pedestrian movement. We investigate the natural motion of pedestrians and explore future motion patterns of pedestrians with similar observed trajectories, which provides a new perspective for multi-modal prediction of pedestrian trajectories. On this basis, we propose a novel model to learn diverse trajectories from human motion patterns, which is mainly composed of a motion pattern selector and a multi-modal trajectory generator. With a constructed pattern gallery, the motion pattern selector mines the possible future motion patterns based on the similar observed trajectories. Subsequently, the diverse future motion patterns are fed into the multi-modal trajectory generator, in which these motion patterns are selected and refined through a scoring network and a regression network, and then directly generate multiple diverse trajectories. We use two public pedestrian trajectory ETH/UCY datasets to compare our method with several baseline methods. Experimental results show that our model outperforms most of the state-of-the-art approaches both in accuracy and diversity by fully leveraging the intrinsic information of trajectories.

Human (but not animal) motion can be recognized at first sight – After treatment for congenital blindness

The long-standing nativist vs. empiricist debate asks a foundational question in epistemology – does our knowledge arise through experience or is it available innately? Studies that probe the sensitivity of newborns and patients recovering from congenital blindness are central in informing this dialogue.One of the most robust sensitivities our visual system possesses is to ‘biological motion’ – the movement patterns of humans and other vertebrates. Various biological motion perception skills (such as distinguishing between movement of human and non-human animals, or between upright and inverted human movement) become evident within the first months of life. The mechanisms of acquiring these capabilities, and specifically the contribution of visual experience to their development, are still under debate.We had the opportunity to directly examine the role of visual experience in biological motion perception, by testing what level of sensitivity is present immediately upon onset of sight following years of congenital visual deprivation. Two congenitally blind patients who underwent sight-restorative cataract-removal surgery late in life (at the ages of 7 and 20 years) were tested before and after sight restoration. The patients were shown displays of walking humans, pigeons, and cats, and asked to describe what they saw.Visual recognition of movement patterns emerged immediately upon eye-opening following surgery, when the patients spontaneously began to identify human, but not animal, biological motion. This recognition ability was evident contemporaneously for upright and inverted human displays.These findings suggest that visual recognition of human motion patterns may not critically depend on visual experience, as it was evident upon first exposure to un-obstructed sight in patients with very limited prior visual exposure, and furthermore, was not limited to the typical (upright) orientation of humans in real-life settings.

A method of attitude measurement and level assessment for skiers based on wearable inertial measurement

<p>Quantitative analysis of sports is an important development direction of scientific skiing training, and the digital expression of human movement patterns during skiing is the basis for scientific quantitative analysis. A human motion capture and attitude reconstruction system based on a wearable BSBD inertial measurement unit was designed and built, combined with the human multi-rigid body motion model to realize the human body reconstruction during the skiing, and applied to the auxiliary training of slewing movements in alpine skiing. At the same time, for the indoor training scene based on the multi-degree-of-freedom simulated ski training platform, a digital evaluation method suitable for ski slalom is proposed. The method uses motion capture system and posture reconstruction system to extract five kinds of sliding characteristic data of skiers, and realizes the evaluation of skiers’ technical parameters through similarity measurement and linear fitting with high-level athletes’ motion parameters, so as to assist scientific training. Finally, experiments are carried out on the indoor Olymp simulated ski training bench to verify the effectiveness of the method.</p>

Dynamic-IMD (D-IMD): Introducing activity spaces to deprivation measurement in London, Birmingham and Liverpool

Indicators of deprivation intersect a variety of disciplinary contexts. In this article, we build a multi-space measure of deprivation by introducing urban mobilities derived from location footprints of nearly three million mobile phone users. Traditionally, experiences of deprivation have been estimated through a lens fixed to residential spaces, which overlooks the likelihood individuals experience deprivation beyond that implied by where they live. By quantifying how exposure to deprivation varies by human movement patterns across three English cities, we construct a Dynamic Index of Multiple Deprivation (D-IMD). Analysis of this measure highlights how deprivation exposure converges to a more homogenous middle ground, which implies the deprivation gap lessens as individuals across the socio-economic spectrum coalesce in spaces that exhibit similar environmental conditions. Using a hypothetical example, our D-IMD measure identifies 185 neighbourhoods that would enter England's eligibility criteria for funding opportunity intended to alleviate socio-economic inequality and hardship. These practical implications are extensible to international contexts that mobilise deprivation indices in similar ways to English institutions.

Human hand motion prediction based on feature grouping and deep learning: Pipe skid maintenance example

Human-robot collaboration has gained popularity in various construction applications. The key to a successful human-robot collaboration at the same construction workplace is the delicate algorithm for predicting human motions to strengthen the robot's situational awareness, i.e., robot-human awareness. Most existing approaches have focused on predicting human motions based on repetitive patterns of human behaviors in well-defined task contexts, such as specific object picking tasks, for a relatively short period of time. These methods can hardly capture the “pattern inconsistency” of human actions, i.e., the differences across people in terms of motion features and even for the same person at different time points of the task. This paper proposes an analytical pipeline that segments and clusters the human inconsistent behaviors into different pattern groups and builds separate human motion pattern prediction models correspondingly. The proposed method, Human Motion Feature Grouping and Prediction (HMFGP), quantifies the spatiotemporal relationship between gaze focus and hand movement trajectories, segments the raw data based on the detected gaze-hand relationship pattern changes, and clusters the matched gaze-hand data segments into several pattern groups based on the pattern similarity of the gaze-hand relationships. Then a time series Deep Learning method is used to predict hand motions based on gaze focus trajectories for each of the pattern groups. The gaze and hand motion data of a human subject experiment (n = 120) for pipe skid maintenance was used to test the prediction performance of HMFGP. The result shows that HMFGP can significantly improve the accuracy of human hand motion prediction and help quantity different patterns of human motions for specific analyses.

Design of Intelligent Perception Module Based on Wireless Sensor Network and Basketball Sports Attitude

In recent years, along with microelectromechanical (MEMS) technique and wireless body area network (WBAN), wearable health monitoring systems have emerged. With sustainability posture detection technique, people pay more and more attention to mankind posture detection. Human posture detection technique has been widely used in medical, film and television, industry, sports, and other fields. Motion capture is a technique for measuring the motion of moving objects in three-dimensional space with great accuracy. It is the most efficient method for producing computer 3D animation and collecting human motion data. The use of motion capture systems in animation is becoming increasingly common. The problem of a single sensor having a large error in monitoring human motion and attitude is addressed. The use of multisensor data fusion technology is used to propose a human motion pattern recognition method based on data fusion of accelerometer and gyroscope. The system must effectively integrate the information of various sensors in order to achieve the goal of accuracy, timeliness, and reliability processing, and multisensor information fusion systems for various complex application objects are constantly emerging.

Street network or functional attractors? Capturing pedestrian movement patterns and urban form with the integration of space syntax and MCDA

The space syntax approach has been widely applied in urban studies and practices to investigate urban form and predict pedestrian movement patterns. Prior research has identified the ‘circular causality’ associated with the ontology of space syntax, indicating that network structure and functional attractors are mutually reinforced by movement patterns in urban spaces. Using the city of Xi’an as a case study, this research aims to deepen the understanding of urban form and human movement patterns from a new perspective that integrates the space syntax approach with multi-criteria decision analysis (MCDA). To quantify the city’s attractors and gauge their impacts on its urban form, this research uses available open urban datasets, including street networks from OpenStreetMap (OSM) and Point of Interest (POI) data. Facilitated by sensitivity analysis (SA), the findings suggest that the functional distinctiveness of districts plays a nuanced role in attracting and generating pedestrian flow. It is therefore essential to introduce a ‘real-time’ representation of urban spaces to supplement our conventional understanding.

Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms.

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.

Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation?

ABSTRACT Many studies have proven that human movement patterns are strongly impacted by individual socioeconomic and demographic background. While many efforts have been made on exploring the influences of age and gender on movement patterns using social media, this study aims to analyse and compare the movement patterns among different racial-ethnic and economic groups using social media (i.e. geotagged tweets) from the U.S. top 50 populated cities. Results show that there are significant differences in number of activity zones and median travel distance across cities and demographic groups, and that power-laws tend to be captured in both spatial and demographic aspects. Additionally, the analysis of outbound-city travels demonstrates that some cities have slightly stronger interaction with others, and that economically disadvantaged populations and racial-ethnic minorities are more restricted in long distance travels, indicating that their spatial mobility is more limited to the local scale. Lastly, an economically-segregated movement pattern is discovered – upper-class neighbourhoods are mostly visited by the upper-class, while lower-class neighbourhoods are mainly accessed by the lower-class – but some racial-ethnic groups can diversify this segregated pattern in the local scale.

Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

Dinamika Pertumbuhan Kawasan Permukiman Kota Jambi

Urban Growth Dyanamics of Settlement Areas in Jambi City. The development of a city will follow trigger factors such as economy, service centers and population. This research tries to describe the growth of urban areas in Jambi City from 2000 to 2020 which is supported by several driving factors from human activity and movement patterns using time series image data from 2000, 2008, 2016 and 2020. The results show that there is a development in the urban area which leads to the Center Business District (CBD). Based on the growth area, there is an increase in the growth of the urban area which leads to expansion diffusion model with concentric shapes. Urban areas that always sustain growth are centered in the Central Business District: Pasar Jambi District, Jelutung District and Jambi Selatan District.

Dinamika Pertumbuhan Kawasan Permukiman Kota Jambi

Urban Growth Dyanamics of Settlement Areas in Jambi City. The development of a city will follow trigger factors such as economy, service centers and population. This research tries to describe the growth of urban areas in Jambi City from 2000 to 2020 which is supported by several driving factors from human activity and movement patterns using time series image data from 2000, 2008, 2016 and 2020. The results show that there is a development in the urban area which leads to the Center Business District (CBD). Based on the growth area, there is an increase in the growth of the urban area which leads to expansion diffusion model with concentric shapes. Urban areas that always sustain growth are centered in the Central Business District: Pasar Jambi District, Jelutung District and Jambi Selatan District.

Beyond The Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability

Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https://github.com/zcfinal/ContextTransitionPredictability.

Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network

Automatic detection of human-related anomalous events in surveillance videos is challenging, owing to unclear definition of anomalies and insufficiency of training data. Generally, the irregular human motion patterns can be regarded as human-related abnormal events. Therefore, we propose a novel method to operate directly on sequences of human skeleton graphs for discovering the normal patterns of human motion. The sequence of skeleton graphs is decomposed into two sub-components: global movement and local posture sequences. The global component is utilized to compute local component. The local component sequences are then input to our network for capturing normal spatial-temporal motion patterns of human skeleton. Our network is established on a Spatial-temporal Graph Convolutional Autoencoder (ST-GCAE) and embedded with Long Short-Term Memory (LSTM) network in hidden layers for exploring the temporal cues, which is thus called Spatial-temporal Graph Convolutional Autoencoder with Embedded Long Short-Term Memory Network (STGCAE-LSTM). Different from traditional autoencoder, STGCAE-LSTM owns a single-encoder-dual-decoder architecture, which is capable of reconstructing the input and predicting the unseen future simultaneously. Then, samples that deviate from normal patterns are detected as anomalies with fusion of reconstruction and prediction errors. Experimental results on four challenging datasets demonstrate advantages of our method over other state-of-the-art algorithms.