Human Activity Patterns Research Articles

Conceptual Organization is Revealed by Consumer Activity Patterns

Computational models using text corpora have proved useful in understanding the nature of language and human concepts. One appeal of this work is that text, such as from newspaper articles, should reflect human behaviour and conceptual organization outside the laboratory. However, texts do not directly reflect human activity, but instead serve a communicative function and are highly curated or edited to suit an audience. Here, we apply methods devised for text to a data source that directly reflects thousands of individuals’ activity patterns. Using product co-occurrence data from nearly 1.3-m supermarket shopping baskets, we trained a topic model to learn 25 high-level concepts (or topics). These topics were found to be comprehensible and coherent by both retail experts and consumers. The topics indicated that human concepts are primarily organized around goals and interactions (e.g. tomatoes go well with vegetables in a salad), rather than their intrinsic features (e.g. defining a tomato by the fact that it has seeds and is fleshy). These results are consistent with the notion that human conceptual knowledge is tailored to support action. Individual differences in the topics sampled predicted basic demographic characteristics. Our findings suggest that human activity patterns can reveal conceptual organization and may give rise to it.

Geospatial Big Data: New Paradigm of Remote Sensing Applications

The rapid development of information technology and location techniques not only leads to an increasing growth of massive geospatial big data but also raises the attention of using these data to complement with remote sensing images. Many efforts have been made to utilize geospatial big data to identify human activity patterns and carry out urban and environmental researches, integrating with remote sensing images. Nonetheless, there are still many issues, including the representativeness and locality of geospatial big data, as well as the fusion methods, remain to be further explored. In this article, we first reviewed the innovation and proceedings of data mining and analyzing techniques, as well as remote sensing applications driven by geospatial big data. Besides, two popular concepts, namely, “Social Sensing” and “Urban Computing,” were briefly introduced. Then, we highlighted the role of geospatial big data in mining human activity dynamics and socioeconomic characteristics, and the feasibility of combining with remote sensing data for various studies. Lastly, we presented some empirical case studies on the confluence of remote sensing and geospatial big data in land use extraction, environmental and disaster monitoring, as well as socioeconomic dynamics sensing. The provided examples and discussion demonstrated the high efficiency and complementarity of the integration of remote sensing and geospatial big data, which benefits decision making from multiple perspectives and scales.

Physiological stress and behavioural responses of European Rollers and Eurasian Scops Owls to human disturbance differ in farming habitats in the south of Spain

SummaryHuman activities are altering ecosystems and threatening the well-being of wildlife. The study of the stressful effects of human disturbances on animal distribution, physiology and behaviour can provide fundamental insights for wildlife conservation. Here, we assess how two declining birds, the European Roller Coracias garrulus and the European Scops Owl Otus scops, cope with alteration by human activities in farming habitats of the south of Spain. We studied nest distribution, quantified nestling physiology (corticosterone levels in plasma and feathers and body weight close to fledgling) and parental behaviour (feeding rates) of both species along a human alteration gradient. Rollers and Scops Owls used the same type of habitat and their spatial distribution was not determined by individual quality. In Rollers, nestlings raised in scrubland areas had high stress-induced corticosterone levels, possibly due to high predation risk in this habitat. In addition, Rollers and Scops Owls showed opposite relationships with farming activity and human disturbance. Nestling Rollers showed the highest corticosterone levels in feathers, weight and parental feeding rates in areas with intense farming activity. These results suggest that despite the disturbance produced by farming activities, inducing a higher stress in these areas, cultivated areas may, simultaneously, provide parents with a higher abundance of prey which would trigger increased feeding rates and, hence, higher nestling weights. Furthermore, nestling Scops Owls showed the highest stress-induced corticosterone levels in areas close to roads, suggesting that they would be affected by human disturbance due to infrastructures that disturb also at night when Scops Owls are active. Therefore, susceptibility to human disturbance may vary between species, probably due to variation in the daily pattern of human activities and the species’ activity rhythm, buffering or exacerbating the effects, which should be considered in future studies on human alterations and birds.

Mapping remote rural settlements at 30 m spatial resolution using geospatial data-fusion

Accurate and timely population distribution maps are critical to addressing health epidemics, coordinating natural disaster response, tracking global changes for environmental conservation, confronting human rights issues, and more. Satellite imagery has opened numerous ways for mapping population distribution – previously unattainable without conducting a resource intensive census. While great advances have been made in urban population mapping, large gaps still exist for rural and remote populations which tend to be the most vulnerable to environmental, economic, and socio-political change. Moderate resolution remote sensing data, such as Landsat, has generally been viewed as insufficient for rural mapping given its coarseness relative to the size of a rural dwelling. This paper presents an approach for detecting small rural settlements within Ann Township of Rakhine State, a remote region of Myanmar with a highly mobile population, by combining Landsat data with publicly available auxiliary geospatial data. The key to the success of detecting small settlements at the Landsat resolution was the inclusion of multi-temporal auxiliary data sensitive to human activity patterns in the regions, such as distance to water, change in seasonal vegetation signals, and distance to a recent active fire. The use of active fire in this context is novel and provided a key metric that increased the accuracy of the resultant map. The final dataset has a classification accuracy of 86.5% on a per-pixel basis and 93.1% on a location identification basis. Numerous small settlements (on the order of 2–3 structures in some cases) not previously mapped by other datasets are identified, revealing that the population of Ann Township is far more dispersed and isolated than previously mapped. This study concludes that by incorporating regionally specific characteristics, moderate resolution remotely sensed data can be used to successfully map geographically marginalized communities so that services and aid are better able to reach them.

Investigating Road-Constrained Spatial Distributions and Semantic Attractiveness for Area of Interest

An area of interest (AOI) refers to an urban area that attracts people’s attention within different urban functions through cities. The wide availability of big geo-data that are able to capture human activities and environmental socioeconomics enable a more nuanced identification of AOIs. Current research has proposed various approaches to delineate continuous AOI patterns using big geo-data. However, these approaches ignore the effects of urban structures such as road networks on reshaping AOIs, and fail to investigate the attractiveness and certain functions within AOIs. To fill this gap, this paper proposes a systematic framework to investigate the spatial distribution of road-constrained AOIs and analyze the semantic attractiveness. First, we propose an Epanechnikov-based kernel density estimation (KDE) with a bandwidth selection strategy to extract road-constrained AOIs. Then, we establish semantic attractiveness indices regarding AOIs based on the textual information and the number of review data. Finally, we investigate in detail the spatial distribution and semantic attractiveness of AOIs in Yuexiu, Guangzhou. The results show that road-constrained AOIs can not only effectively capture the human activity patterns influenced by urban structures, but also depict certain urban functions including entertainment, public, service, hotel, education, and food functions. This method provides a quantitative reference to monitor urban structures and human activities to support city planning.

Human Activity Patterns in Big Data for Healthcare Applications

Nowadays, there is a consistently growing migration of people to urban domains. Therapeutic administrations organizations are a champion among the most testing viewpoints that is massively impacted by the colossal surge of people to downtown territories. In this manner, urban zones far and wide are placing enthusiastically in cutting edge change with a ultimate objective to give progressively profitable organic network to people. In such change, a large number homes are being equipped with canny contraptions (for example splendid meters, sensors, etc.) which produce massive volumes of fine-grained and indexical data that can be penniless down to help sharp city organizations. In this paper, we propose a model that utilizations splendid home huge data as techniques for picking up and finding human development structures for restorative administrations applications. We propose the use of normal model mining, bunch examination and gauge to measure and dismember essentialness use changes begun by occupants' direct. Since people's penchants are generally recognized by standard timetables, finding these calendars empowers us to see strange activities that may exhibit people's inconveniences in taking oversee to themselves, for instance, not arranging sustenance or not using shower/shower. Our places of business the need to analyze transient imperativeness use structures at the machine level, which is clearly related to human activities. The data from sharp meters is recursively mined in the quantum/data cut of 24 hours, and the results are kept up across over dynamic mining works out. [1,2,3,4,5]

Epidemiology, ecology and human perceptions of snakebites in a savanna community of northern Ghana.

BackgroundWorldwide, snakebite envenomations total ~2.7 million reported cases annually with ~100,000 fatalities. Since 2009, snakebite envenomation has intermittently been classified as a very important ‘neglected tropical disease’ by the World Health Organisation. Despite this emerging awareness, limited efforts have been geared towards addressing the serious public health implications of snakebites, particularly in sub-Saharan Africa, where baseline epidemiological and ecological data remain incomplete. Due to poverty as well as limited infrastructure and public health facilities, people in rural Africa, including Ghana, often have no other choice than to seek treatment from traditional medical practitioners (TMPs). The African ‘snakebite crisis’ is highlighted here using regionally representative complementary data from a community-based epidemiological and ecological study in the savanna zone of northern Ghana.Methodology and findingsOur baseline study involved two data collection methods in the Savelugu-Nanton District (in 2019 the district was separated into Savelugu and Nanton districts) in northern Ghana, comprising a cross-sectional study of 1,000 residents and 24 TMPs between December 2008 and May 2009. Semi-structured interviews, as well as collection of retrospective snakebite and concurrent rainfall records from the Savelugu-Nanton District Hospital and Ghana Meteorological Authority respectively over 10-years (1999–2008) were used in the study. Variables tested included demography, human activity patterns, seasonality, snake ecology and clinical reports. Complementary data showed higher snakebite prevalence during the rainy season, and a hump-shaped correlation between rainfall intensity and snakebite incidences. Almost 6% of respondents had experienced a personal snakebite, whereas ~60% of respondents had witnessed a total of 799 snakebite cases. Out of a total of 857 reported snakebite cases, 24 (~2.8%) died. The highest snakebite prevalence was recorded for males in the age group 15–44 years during farming activities, with most bites occurring in the leg/foot region. The highest snakebite rate was within farmlands, most severe bites frequently caused by the Carpet viper (Echis ocellatus).ConclusionThe relatively high community-based prevalence of ~6%, and case fatality ratio of ~3%, indicate that snakebites represent an important public health risk in northern Ghana. Based on the high number of respondents and long recording period, we believe these data truly reflect the general situation in the rural northern savanna zone of Ghana and West Africa at large. We recommend increased efforts from both local and international health authorities to address the current snakebite health crisis generally compromising livelihoods and productivity of rural farming communities in West Africa.

Affordance-based Design Method: A Case Study of University Campus

Development of a built environment encompasses urban design, land use, transportation system, and accommodates patterns of human activity within the physical environment. Holistic development of built environment requires a multidisciplinary design team of urban planners, architects and sustainability advisors from an early design stage. Literature and industry-practices show growth of designer’s techniques and competencies, sustainable development capabilities, and user-centric prospective in the early design. However, there is a lack of systematic method and all-inclusive approach to design a built environment. Thus, proposed research aims to develop a design method which is user-inspired, stakeholder conducive and environmentally conscious from an early design stage. To achieve this aim, an affordance-based design method is proposed and demonstrated through a case study of university campus. Affordance-based design method has been used for design of complex systems by capturing user needs, stakeholder ideas and generating design options. The proposed design method provides decision-making guidelines to designers, design space to incorporate stakeholders, and affordances to achieve sustainability. The proposed design method has potential to shift design and development of built environment from designer-controlled process to systematically organized process.

Calibrating an agent-based model of longitudinal human activity patterns using the Consolidated Human Activity Database.

Patterns of human behavior over extended periods of time are important for characterizing human exposure to hazardous chemicals. Because longitudinal behavior patterns for an individual are difficult to obtain, exposure-assessors have characterized such patterns by linking daily records from multiple individuals. In an earlier publication, we developed an alternative strategy that was based on agent-based simulation modeling. Specifically, we created a software program, Agent-Based Model of Human Activity Patterns (ABMHAP), that generates year-long longitudinal behavior patterns. In this paper, we both calibrate and evaluate ABMHAP using human behavior data from the U.S. Environmental Protection Agency's Consolidated Human Activity Database (CHAD). We use the longitudinal data (data on individuals'activities over multiple days) in CHAD to parameterize ABMHAP, and we use single-day behavior data from CHAD to evaluate ABMHAP predictions. We evaluate ABMHAP's ability to simulate sleeping, eating, commuting, and working (or attending school) for four populations: working adults, nonworking adults, school-age children, and preschool children. The results demonstrate that ABMHAP, when parameterized with empirical data, can capture both interindividual and intraindividual variation in behaviors in different types of individuals. We propose that simulating annual activity patterns via ABMHAP may allow exposure-assessors to characterize exposure-related behavior in ways not possible with traditional survey methods.

A Combination of Indoor Localization and Wearable Sensor-Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results.

BackgroundHealth care, in recent years, has made great leaps in integrating wireless technology into traditional models of care. The availability of ubiquitous devices such as wearable sensors has enabled researchers to collect voluminous datasets and harness them in a wide range of health care topics. One of the goals of using on-body wearable sensors has been to study and analyze human activity and functional patterns, thereby predicting harmful outcomes such as falls. It can also be used to track precise individual movements to form personalized behavioral patterns, to standardize the concept of frailty, well-being/independence, etc. Most wearable devices such as activity trackers and smartwatches are equipped with low-cost embedded sensors that can provide users with health statistics. In addition to wearable devices, Bluetooth low-energy sensors known as BLE beacons have gained traction among researchers in ambient intelligence domain. The low cost and durability of newer versions have made BLE beacons feasible gadgets to yield indoor localization data, an adjunct feature in human activity recognition. In the studies by Moatamed et al and the patent application by Ramezani et al, we introduced a generic framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and extracting indoor localization using BLE beacons, in concert.ObjectiveThe study aimed to examine the ability of combination of physical activity and indoor location features, extracted at baseline, on a cohort of 154 rehabilitation-dwelling patients to discriminate between subacute care patients who are re-admitted to the hospital versus the patients who are able to stay in a community setting.MethodsWe analyzed physical activity sensor features to assess activity time and intensity. We also analyzed activities with regard to indoor localization. Chi-square and Kruskal-Wallis tests were used to compare demographic variables and sensor feature variables in outcome groups. Random forests were used to build predictive models based on the most significant features.ResultsStanding time percentage (P<.001, d=1.51), laying down time percentage (P<.001, d=1.35), resident room energy intensity (P<.001, d=1.25), resident bed energy intensity (P<.001, d=1.23), and energy percentage of active state (P=.001, d=1.24) are the 5 most statistically significant features in distinguishing outcome groups at baseline. The energy intensity of the resident room (P<.001, d=1.25) was achieved by capturing indoor localization information. Random forests revealed that the energy intensity of the resident room, as a standalone attribute, is the most sensitive parameter in the identification of outcome groups (area under the curve=0.84).ConclusionsThis study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features at baseline, a subset of which can better distinguish between at-risk patients that can gain independence versus the patients that are rehospitalized.

Prediction of Human Activity Patterns for Human–Robot Collaborative Assembly Tasks

It is widely agreed that future manufacturing environments will be populated by humans and robots sharing the same workspace. However, the real collaboration can be sporadic, especially in the case of assembly tasks, which might involve autonomous operations to be executed by either the robot or the human worker. In this scenario, it might be beneficial to predict the actions of the human in order to control the robot both safely and efficiently. In this paper, we propose a method to predict human activity patterns in order to early infer when a specific collaborative operation will be requested by the human and to allow the robot to perform alternative autonomous tasks in the meanwhile. The prediction algorithm is based on higher-order Markov chains and is experimentally verified in a realistic scenario involving a dual-arm robot employed in a small part collaborative assembly task.

Climatic and environmental conditions in the Western Galilee, during Late Middle and Upper Paleolithic periods, based on speleothems from Manot Cave, Israel

Early Ahmarian, Levantine Aurignacian and Post-Levantine Aurignacian archeological assemblages show that the karstic Manot Cave, located 5 km east of the Mediterranean coast in the Western Galilee region of Israel, was intensively occupied during the Early Upper Paleolithic. The coexistence of these rich archaeological layers with speleothems in Manot Cave provides a window of opportunity for determining the relationships between climatic conditions and the nature of human activity and mobility patterns in the Western Galilee region during the Early Upper Paleolithic period. This study, based on four stalagmites that grew almost continuously from ∼75 to 26.5 ka, covers most of the last glacial, and overlaps with the human occupation of the cave. The speleothems oxygen (δ18O) and carbon (δ13C) isotopic records indicate that climate and environmental conditions fluctuated during the last glacial, some of which correspond with Dansgaard–Oeschger (D-O) cycles 12, 10, 7 and Heinrich (H) events VI and V. Consistent with independent evidence from botanic and faunal remains, these climatic shifts brought about significant environmental changes in the region, ranging from dominant thick Mediterranean forest to more open landscape. A good correlation with less negative δ13C values is most pronounced during the Early Ahmarian time period, but there was also a change to less negative δ13C values during the Levantine Aurignacian and Post-Levantine Aurignacian industries in the Levant. These positive δ13C shifts suggest that environmental transformation towards a more open grassy landscape dominated by C4 vegetation might have played an important role in the development of these cultural entities (mainly the Early Ahmarian) in Manot Cave region.

Privacy preserving semantic trajectory data publishing for mobile location-based services

The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k − 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.

Understanding Urban Dynamics From Massive Mobile Traffic Data

Understanding the patterns of mobile data consumption is extremely valuable to reveal human activities and ecology in urban areas. This task is nontrivial in terms of three challenges: the complexity of mobile data consumption in large urban environment, the disturbance of abnormal events, and lack of prior knowledge for urban traffic patterns. We propose a novel approach to design a powerful system that consists of three subsystems: time series decomposing of mobile traffic data, extracting patterns from different components of the original traffic, and detecting anomalous events from noises. Our investigation involving the mobile traffic records of 6,400 cellular towers in Shanghai reveals three important observations. First, among all the 6,400 cellular towers, we identify five daily patterns corresponding to different human daily activity patterns. Second, we find that two natural patterns can be extracted from the weekly trend of mobile traffic consumption, which reflects modes of human activities. Last but not least, besides the regular patterns, we investigate how irregular activities affect mobile traffic consumption, and exploit this knowledge to successfully detect unusual events like concerts and soccer matches. Our proposed methodology therefore will aid a comprehensive understanding of large-scale mobile traffic consumption in urban areas.

A survey on Large Scale Data Analysis on Human Activity Patterns for health prediction

A survey on Large Scale Data Analysis on Human Activity Patterns for health prediction

Mining Non-Zero-Rare Sequential Patterns On Activity Recognition

Discovering rare human activity patterns—from triggered motion sensors deliver peculiar information to notify people about hazard situations. This study aims to recognize rare human activities using mining non-zero-rare sequential patterns technique. In particular, this study mines the triggered motion sensor sequences to obtain non-zero-rare human activity patterns—the patterns which most occur in the motion sensor sequences and the occurrence numbers are less than the pre-defined occurrence threshold. This study proposes an algorithm to mine non-zero-rare pattern on human activity recognition called Mining Multi-class Non-Zero-Rare Sequential Patterns (MMRSP). The experimental result showed that non-zero-rare human activity patterns succeed to capture the unusual activity. Furthermore, the MMRSP performed well according to the precision value of rare activities.

From edit distance to augmented space-time-weighted edit distance: Detecting and clustering patterns of human activities in Puget Sound region

Considering and measuring the similarity of human activities remains challenging. Existing studies of similarity measures based on traditional edit distance (ED), specifically on activity patterns, do not reflect the spatiotemporal characteristics in the measurement model. Additionally, interdependence between activities is ignored in existing multidimensional sequence alignment methods. To address the gap, we initially extend the traditional edit distance to a space-time-weighted edit distance (STW-ED). Specifically, differences in distance and time between activities are considered cost functions in the operation cost calculation (insertion, deletion, and substitution). We advance STW-ED to an augmented space-time-weighted edit distance method (ASTW-ED) that integrates an optimum-trajectory-based multidimensional sequence alignment method (OT-MDSAM) with STW-ED, treating the nonspatiotemporal dimensions as augment factors. In addition, ontology is considered for the similarity measure for nonspatiotemporal dimensions.To show the feasibility of our proposed approach, we conduct an empirical study based on an activity-based travel survey in the Puget Sound Region. Eight clusters (homemakers, regular workers with a colorful life, regular workers with a monotonous life, part-time workers, recreation travelers, senior travelers, no-job travelers, and night owl adventurers) are identified based on ASTW-ED and ontology. To cluster the similarity matrix derived from the introduced methods, the affinity propagation (AP) clustering method is employed because it is free of prior knowledge for clustering and can produce exemplars of the clusters. The empirical study indicates that, relative to existing methods for multidimensional activity similarity measurement and clustering, ASTW-ED performs better in terms of within-group homogeneity and between-group heterogeneity of clusters. In addition, the results reveal that ontology can improve clustering performance if it is considered for nonspatiotemporal dimensions provide better understanding of human behavior for urban governance..

Sensing multiple semantics of urban space from crowdsourcing positioning data

Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data.

Determination of influencing factors on historical concentration variations of PAHs in West Taihu Lake, China

The adsorption of polycyclic aromatic hydrocarbons (PAHs) by components such as elemental carbon (EC), total organic carbon (TOC), and particles is different, and EC and PAHs are good materials for reconstructing historical human activity patterns and pollution conditions. In this study, the effects of EC (soot and char), TOC and particles of different grain size on PAHs in surface sediments were quantitatively analysed, and their historical concentrations in a sediment core from western Taihu Lake were reconstructed. The contents of soot, TOC, clay, EC and char explained 57.2%, 27.6%, 26.0%, 24.0% and 16.4%, respectively, of the PAH concentrations in surface sediments. The correlation between the soot and PAH levels was significantly higher than that between the char, TOC, and clay contents and PAH levels, and PAHs were mainly affected by the local economic development and human activity, as indicated by metrics of population, highway mileage, coal burning, and industrial output. With the development of the economy of the Taihu Lake Basin, the composition of PAHs in the sediments has changed: the proportion of low-molecular-weight PAHs decreased from 42.4% to 17.5%, and that of high-molecular-weight PAHs increased from 58.7% to 82.5%. The concentration of PAHs in pore water from Taihu Lake over the past 100 years was reconstructed and ranged from 43.1 to 961.2 μg L−1, with an average of 180.7 μg L−1. After China's reform and opening up, the concentrations of various PAHs in Taihu Lake changed from safe to chronic pollution levels. The ratios of lead (Pb) isotopes and the diagnostic ratios of PAHs showed that the main sources of PAHs in western Taihu Lake sediments were human activities such as coal and petroleum combustion.

Daily Activity Recognition Using Pyroelectric Infrared Sensors and Reference Structures

Pyroelectric infrared (PIR) sensor is widely used as motion detector, due to its sensitivity to the infrared radiation changes (IRC) induced by human motion. In particular, it is also possible to determine the human activity patterns based on the IRC signal by improving the feature-specific sensitivity of the PIR sensors. In this paper, the feature-specific sensitivity is enhanced by visibility modulation at the geometric sensing layer. At the cooperative sensing layer, a novel stereo configuration is used in developing the infrared sensing system, which aims to handle the difficulties caused by occlusion and limited coverage area. The experimental results demonstrate the effectiveness of the developed system in daily activity recognition.