Activity Monitoring System Research Articles

All-polymeric fibrous triboelectric nanogenerator for self-powered intelligent active motions monitoring system

The emergence of fibrous triboelectric nanogenerators (FTENGs) provides a promising candidate for self-powered wearable electronics, and developing the all-polymeric FTENGs is beneficial to further improve the wearing comfort of the devices. However, it remains a crucial challenge to fabricate the all-polymeric FTENGs with ideal energy output performances. In this work, an all-polymeric stretchable FTENG (PEDOT/FSiR-FTENG) with high triboelectric outputs is fabricated through a simple rolling method and applied in the human active motions monitoring. The wrinkled poly(3,4-ethylenedioxythiophene): poly(4-styrenesulfonate) (PEDOT:PSS) electrode layer is constructed to endow the PEDOT/FSiR-FTENG with high stretchability, enabling it to stably output electrical signals under 50% strain extension. In addition, the configuration and constituent of the electrode are adjusted to further enhance its conductivity, thereby significantly optimizing the triboelectric performance of the PEDOT/FSiR-FTENG. The obtained PEDOT/FSiR-FTENG can output the open-circuit voltage and short-circuit current of 30 V and 0.16 μA respectively under the external force of 60 N, and the electric signals show an obvious variation with the change of the force between 0.5 and 220 N, demonstrating its high sensitivity and responsiveness to mechanical stimuli. Taking advantage of these outstanding properties, the PEDOT/FSiR-FTENG is applied as a flexible self-powered sensor in the precise physiological detection, including large human motions (steps and joints bending) and slight human vital signs (expression, pulse, and phonation). In addition, a self-powered intelligent active motion monitoring system with wireless data transmission ability is developed based on the PEDOT/FSiR-FTENG, presenting great application potential in the remote patient care and the real-time communication between deaf-mutes.

Toward standardized solid medium cultivations: Online microbial monitoring based on respiration activity.

Cultivating microorganisms on solid agar media is a fundamental technique in microbiology and other related disciplines. For the evaluation, most often, a subjective visual examination is performed. Crucial information, such as metabolic activity, is not assessed. Thus, time-resolved monitoring of the respiration activity in agar cultivations is presented to provide additional insightful data on the metabolism. A modified version of the Respiration Activity MOnitoring System (RAMOS) was used to determine area-specific oxygen and carbon dioxide transfer rates and the resulting respiratory quotients of agar cultivations. Therewith, information on growth, substrate consumption, and product formation was obtained. The validity of the presented method was tested for different prokaryotic and eukaryotic organisms on agar, such as Escherichia coli BL21, Pseudomonas putida KT2440, Streptomyces coelicolor A3(2), Saccharomyces cerevisiae WT, Pichia pastoris WT, and Trichoderma reesei RUT-C30. Furthermore, it is showcased that several potential applications, including the determination of colony forming units, antibiotic diffusion tests, quality control for spore production or for pre-cultures and media optimization, can be quantitatively evaluated by interpretation of the respiration activity.

Effect of simulated heat waves on the behaviour of two mirid predators

AbstractExtreme climatic events, including heat waves with high‐temperature peaks during the day, are expected to occur more often in many parts of the world due to climate change and may exert negative effects on existing biological control strategies. To assess the effects of high‐temperature peaks on two commonly used, and naturally co‐occurring mirid predators, adults and nymphs of Nesidiocoris tenuis and Macrolophus pygmaeus were exposed either to constant 25°C or daily mean temperatures of 25°C with cycles rising up to 30 or 40°C. Preferred location of the two mirid species on different strata of tomato plants was investigated when they were exposed to the different temperature regimens alone or combined. Activity of mirids under the different temperature regimens was continuously monitored for 48 h using a Drosophila Activity Monitor system. Finally, the efficacy of the mirids preying on Tuta absoluta eggs was measured at the different temperature regimens in a Petri dish assay. Heat waves reaching 30 and 40°C in some instances affected the location of the mirids either when they were on the plant alone or under competition conditions. Locomotory activity of M. pygmaeus in the 40°C treatment was strongly reduced, whereas it remained high in N. tenuis. Effects of temperature peaks on prey consumption were visible in M. pygmaeus nymphs, N. tenuis females and males of both species. We suggest that the different responses of the two species to high‐temperature peaks may reduce their competition and support sustained control when both species are present simultaneously. Moreover, both species were found to be less susceptible to heat peaks when compared to previously reported results for their prey T. absoluta.

Estrous activity and pregnancy outcomes in Holstein heifers subjected to a progesterone based 5-d CO-Synch protocol with or without administration of initial GnRH.

The objectives of this study were to investigate whether estrous activity and its impact on pregnancy outcomes would differ in heifers subjected to a 5d CO-Synch plus progesterone releasing intravaginal device (PRID) protocol with or without an initial GnRH treatment. Holstein Heifers (n=308) were fitted with a collar-mounted automated activity monitoring system approximately 1 week prior to the initiation of the synchronization protocol (Day-7). Heifers were assigned randomly to a 5d CO-Synch plus PRID protocol either with (GnRH; n=154) or without (NGnRH; n=154) an initial administration of 100μg of GnRH at the time of PRID insertion (Day 0). Heifers received a single administration of 500μg of cloprostenol (PGF) at the time of PRID removal (Day 5) and again 24h later (Day 6). Approximately 72h after PRID removal (Day 8), heifers were timed-inseminated (TAI) and concurrently 100μg of GnRH was administered to those not exhibiting estrus. All inseminations were done by one of two technicians using either sex-sorted (n=252) or conventional (n=56) frozen-thawed semen. Transrectal ultrasonography was done on Day 0 to determine ovarian cyclicity and normalcy of the reproductive tract and 30 and 45d post-TAI to determine and confirm pregnancy, respectively. The percentage of heifers determined to be in estrus following PRID removal was greater in the GnRH than in the NGnRH group (94 vs. 82%, respectively; P<0.01). The mean interval from PRID removal to the onset of estrus was shorter for GnRH- than NGnRH-treated heifers (50.8 vs. 59.2h, respectively; P<0.01). Pregnancy per AI (P/AI) at 30d post-TAI tended to be greater for GnRH than NGnRH heifers (68 vs. 59%, respectively; P=0.1). However, P/AI at 45d post-TAI (65 vs. 57%, respectively) and pregnancy loss between 30 and 45d post-TAI (6 vs. 4.5%, respectively) did not differ. The association between the interval from PRID removal to the onset of estrus and P/AI at 30d post-TAI was linearly negative for GnRH heifers; for every 1h increase in the interval from PRID removal to the onset of estrus, the predicted probability of P/AI at 30d post-TAI tended (P=0.08) to be reduced by 2.7%. The association between the interval from PRID removal to the onset of estrus and P/AI at 30d post-TAI was not significant for NGnRH heifers. In addition, the interval from TAI to subsequent estrus, in non-pregnant heifers, was approximately 3d longer for the GnRH than the NGnRH group (20.7 vs. 17.5d, respectively). In summary, the initial GnRH treatment in a 5d CO-Synch plus PRID protocol increased estrus expression and reduced the interval from PRID removal to the onset of estrus in Holstein heifers and tended to increase P/AI at 30d, but did not affect P/AI at 45d post-TAI.

An Efficient System for Human activity recognition and monitoring for elderly people using Machine Learning

An Efficient System for Human activity recognition and monitoring for elderly people using Machine Learning

A full-scale wind turbine blade monitoring campaign: detection of damage initiation and progression using medium-frequency active vibrations

This work is concerned with a structural health monitoring campaign of a 52-m wind turbine blade. Multiple artificial damages are introduced in the blade sequentially, and fatigue testing is conducted with each damage in sequence. Progressive fatigue-driven damage propagation is achieved, enabling investigations concerning detection of initiation and propagation of damage in the blade. Using distributed accelerometers, operational modal analysis is performed to extract the lower-order natural vibration modes of the blade, which are shown to not be sensitive to small damages in the blade. To enable monitoring of small damages, an active vibration monitoring system is used, comprised of an electrodynamic vibration shaker and distributed accelerometers. From the accelerometer data, frequency domain methods are used to extract features. Using the extracted features, outlier detection is performed to investigate changes in the measurements resulting from the introduced damages. Capabilities of using features based on the active vibration data for detection of initiation and progression of damage in a wind turbine blade during fatigue testing are investigated, showing good correlation between the observed damage progression and the calculated changes in the damage index.

Prioritization of Potential Native Plants from Arabian Peninsula Based on Economic and Ecological Values: Implication for Restoration

Land degradation is one of the most important environmental problems worldwide, including in the Arabian Peninsula. In arid climatic conditions (i.e., high temperature, high evaporation, scanty rainfall and high salinity), anthropogenic factors (i.e., grazing, camping, infrastructure development, etc.) are the major causes of land degradation. Therefore, restoration of degraded lands is urgently needed to achieve sustainable development goals. Moreover, countries in the Arabian Peninsula are suffering from a lack of natural freshwater resources. Therefore, using halophytes could be an environmentally and economically viable option to overcome limited availability of fresh water by substituting the demand of portable water for irrigation as well as restoring salt-affected lands. Saline soils are common in the Arabian Peninsula, therefore, exploring the ecological and economic potential of halophytes and incorporating them in restoration projects could be a sustainable option. In this study, an attempt was made to document the uses of Arabian halophytes through a survey of the literature and prioritizing them based on their use value. Out of the 107 species studied, 4 species, namely Arthrocnemum macrostachyum (Moric.) K.Koch., Alhagi graecorum Boiss., Bassia muricata (L.) Asch. and Phragmites australis (Cav.) Trin. Ex Steud., were categorized as high priority followed by 36 species under moderate priority. However, when the priority and life form of species was considered for prioritization, three species, namely Alhagi graecorum, Arundo donax L. and Phragmites australis, ranked at the top in the priority list among perennials and Bassia muricata ranked at the top among annuals. This information could be useful for land restoration specialists to use appropriate halophyte species to achieve for different restoration objectives in salt-affected lands. However, there is a need to develop an active monitoring system that strictly concentrates on the recycling of plants that are used in phytoremediation.

Effect of transition cow health and estrous expression detected by an automated activity monitoring system within 60 days in milk on reproductive performance of lactating Holstein cows

The objective of this observational study was to evaluate the association of transition cow health and estrous expression, detected by an automated activity monitoring system (Smarttag Neck, Nedap Livestock Management), with reproductive performance in lactating Holstein cows. A total of 3,750 lactating Holstein cows (1,563 primiparous cows and 2,187 multiparous cows) from a commercial dairy farm in Slovakia calving from January 2020 until July 2021 were enrolled on an ongoing basis. Activity data were recorded from d 7 until d 60 postpartum. Within this observational period, cows were classified into 3 categories: (1) no estrus event (Estrus0), (2) 1 estrus event (Estrus1), or (3) 2 or more estrus events (Estrus2+). Transition cow health was assessed by farm personnel within the first 30 d in milk (DIM) using standard operating procedures. Generalized linear mixed models were used to analyze continuous and categorical data. Cox proportional hazard models were used for time to event data. The overall prevalence of anestrus was 20.8%. Multiparous cows had a greater risk for anestrus compared with primiparous cows [odds ratio (OR) = 1.4]. Cows with stillbirth (OR = 1.76), retained placenta (OR = 2.19), puerperal metritis (OR = 1.48), or subclinical ketosis (OR = 1.51) had a greater risk for anestrus. In addition, cows calving in summer (OR = 0.82), autumn (OR = 0.38), or winter (OR = 0.56) had a higher incidence of anestrus than cows calving in spring. Estrous expression from d 7 until d 60 postpartum was associated with estrous duration (DU) and estrous intensity at first artificial insemination (AI). Cows in Estrus0 had the shortest DU at first postpartum AI (9.4 ± 0.18 h) compared with cows in Estrus1 (10.5 ± 0.13 h) and Estrus2+ (11.4 ± 0.12 h). Cows in Estrus2+ had a longer DU at first postpartum AI compared with cows in Estrus1. For Estrus0, Estrus1, and Estrus2+ cows, pregnancy per AI at first service was 42.5%, 50.9%, and 55.4%, respectively. Estrous expression from d 7 until d 60 postpartum was associated with time to first AI and time to pregnancy. Compared with Estrus0 cows, Estrus1 [hazard ratio (HR) = 1.43] and Estrus2+ cows (HR = 1.62) had an increased hazard of being inseminated within 100 DIM. Compared with Estrus2+, Estrus1 cows had a reduced hazard of being inseminated within 100 DIM (HR = 0.89). Compared with Estrus0 cows, Estrus1 (HR = 1.24) and Estrus2+ cows (HR = 1.46) had an increased hazard of becoming pregnant within 200 DIM. Median DIM to pregnancy were 121, 96, and 92 for Estrus0, Estrus1, and Estrus2+ cows, respectively. In conclusion, cows with transition cow disorders (i.e., stillbirth, retained placenta, puerperal metritis, or subclinical ketosis) had a greater chance for anestrus compared with healthy cows. Cows in Estrus0 had reduced estrous expression at first AI and inferior reproductive performance compared with cows that displayed estrous activity from d 7 until d 60.

Forest Emissions Reduction Assessment Using Optical Satellite Imagery and Space LiDAR Fusion for Carbon Stock Estimation

Forests offer significant climate mitigation benefits, but existing emissions reduction assessment methodologies in forest-based mitigation activities are not scalable, which limits the development of carbon offset markets. In this study, we propose a measurement method using optical satellite imagery and space LiDAR data fusion to assess forest emissions reduction. Compared with the ALS-based carbon stock density estimation method, our approach presented a strong scalability for mapping 10 m-resolution carbon stock at a large scale. It was observed that dense canopy top height estimated by combining GEDI and Sentinel-2 could accurately predict forest carbon stock measurements estimated by the ALS-based method (R2 = 0.72). By conducting an on-site experiment of an ongoing forest carbon project in China, we found the consistency between the emissions reduction assessed by the data fusion measurement method (589,169 tCO2e) and the official ex post-monitored emissions reduction in the monitoring report (598,442 tCO2e). Our results demonstrated that forest carton stock estimation using optical satellite imagery and space LiDAR data fusion is efficient and economical for forest emissions reduction assessment. The acquisition of the data was more efficient over large areas with high frequencies using space-based technology. We further discussed the challenge of building a near-real-time monitoring system for forest-based mitigation activities by utilizing optical satellite imagery and space LiDAR data and pointed out that a quality control framework should be established to help us understand the sources of uncertainty in LiDAR-based models and improve carbon stock estimation from individual trees to forest carbon projects to meet the requirements of carbon standards better.

Activity Monitoring and Location Sensory System for People With Mild Cognitive Impairments

Cognitive impairment diseases are becoming more and more prevalent mainly due to population aging and the increase in life expectancy. Sensory and monitoring systems may allow people with mild cognitive impairments (MCIs) or at early stages of dementia to live at home for longer with more independence and security. This work presents a wireless sensor network (WSN) based on wearables that obtains indoor and outdoor location and step information, reporting them over a long-range WAN (LoRaWAN) network. Each wireless wearable sensor (WWS) uses a global navigation satellite system (GNSS) module for outdoor positioning, a proposed indoor room-level localization system based on infrared sensors, an accelerometer for a step detector algorithm, and a long-range (LoRa) radio link to send the measured information with low-power consumption achieving a large coverage range. These sensory data are recorded in a database and presented to the medical services and caregivers through a user web application. This can be used to detect anomalous changes in daily patients’ routines, as well as to know the user’s position in cases where the patient may be disoriented. In addition, alerts are launched in caregivers’ smartphones to report about any risky situation, such as the patient leaving an allowed area or staying in one place for too long. Therefore, the proposed sensory system may support and extend the ability of people with MCI or at early stages of dementia to live independently, it helps detect behavioral changes and it keeps caregivers’ peace of mind.

On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers

Advanced driver assistance systems (ADAS) present promising benefits in mitigating road collisions. However, these benefits are limited when risky drivers continue engaging in distraction events. While there is evidence that real-time warnings help improve driving behaviour, the sustained benefits of warning-based ADAS on reducing driving distraction in light commercial vehicle (LCV) drivers remain unclear. This research determines the effect of receiving instant distraction warnings over two years using a naturalistic driving dataset comprising around one million trips from 373 LCV drivers in the Republic of Ireland. Furthermore, the study applies Association Rule Mining (ARM) to find the contextual variables (e.g., speed limit, road type, traffic conditions) that increase the likelihood of distraction events. The results show that warning-based ADAS providing real-time warnings helps reduce distraction events triggering driver inattention, forward collision, and lane departure warnings. Over half of the studied fleet reduced these warnings by at least 50% – lane departure after two months and driver inattention and forward collision after six months. It is found that both passive and active monitoring systems, coupled with coaching and rewards, significantly reduce aggressive driving behaviours tied to harsh acceleration (by 76%) and harsh braking (by 65%). The results of ARM show that the driving context introduces explanatory information for road safety programs. Low-speed urban roads and the summer season increase the likelihood of driver inattention and forward collision warnings. In contrast, high-speed rural roads increase the likelihood of lane departure warnings. These research findings support road safety stakeholders in developing risk assessments based on warning-based ADAS, targeted campaigns to reduce driving distraction, and driving coaching programs.

Nutritional geometry framework of sleep

AimsSleep is a fundamental physiological function and is essential for all animals. Sleep is affected by diet compositions including protein (P) and carbohydrates (C), but there has not been a systematic investigation on the effect of dietary macronutrient balance on sleep. Main methodsWe used the nutritional geometry framework (NGF) to explore the interactive effects on sleep of protein (P) and carbohydrates (C) in the model organism Drosophila. Both female and male flies were fed various diets containing seven ratios of protein-to-carbohydrates at different energetic levels for 5 days and sleep was monitored by the Drosophila Activity Monitor (DAM) system. Key findingsOur results showed that the combination of low protein and high carbohydrates (LPHC) prolonged sleep time and sleep quality, with fewer sleep episodes and longer sleep duration. We further found that the effects of macronutrients on sleep mirrored levels of hemolymph glucose and whole-body glycogen. Moreover, transcriptomic analyses revealed that a high-protein, low-carbohydrate (HPLC) diet significantly elevated the gene expression of metabolic pathways when compared to the LPHC diet, with the glycine, serine, and threonine metabolism pathway being most strongly elevated. Further studies confirmed that the contents of glycine, serine, and threonine affected sleep. SignificanceOur results demonstrate that sleep is affected by the dietary balance of protein and carbohydrates possibly mediated by the change in glucose, glycogen, glycine, serine, and threonine.

A hybrid deep learning for patient activity recognition (PAR): Real time body wearable sensor network from healthcare monitoring system (HMS)

Today’s healthcare sectors are driven and work to rescue patients as soon as possible by giving them the right care and treatment. A healthcare monitoring system works in two ways: by keeping track of the patient’s activities and overall health. For prompt treatment, such as giving the right and suitable medication, administering an injection, and providing additional medical help, nursing supervision is required. Wearable sensors are fixed or connected to the patient’s body and can follow their health. These IoT medical gadgets let clinicians diagnose patients and comprehend the processes from remote. However, the amount of data produced by IoT devices is so large that it cannot be handled manually. A model for automated analysis is required. Convolution Neural Network with Long-Short Term Memory (CNN-LSTM) was therefore suggested in this study as a Hybrid Deep Learning Framework (HDLF) for a Patient Activity Monitoring System (PAMS) that brings all healthcare activities with its classes. To incorporate medical specialists from all over the world and enhance treatment outcomes, the framework offers an advanced model where patient activities, health conditions, medications, and other activities are distributed in the cloud. An effective architecture for Wearable Sensor Network-based Human Action Recognition that combines neural network Simple Recurrent Units (SRUs) and Gated Recurrent Units (GRUs). For assessing the multimodal data input sequence, deep SRUs and a variety of internal memory states is utilized in this research. Furthermore, for addressing the concerns about accuracy oscillations or instability with decreasing gradients, a deep GRUs to store and learn the knowledge is conveyed to the future state. The analysis suggests that CNN-LSTM is then contrasted with some of the currently used algorithms, and it is found that the new system has a 99.53% accuracy rate. The difference between this accuracy result and the current value is at least 4.73%.

A Comprehensive Review on Angara Disease in Poultry

Hepatitis-hydropericardium syndrome (HHS) or Angara disease is an immunosuppression and contagious disease that often occurs in broiler poultry farms with a high mortality rate and sudden death. Furthermore, weight loss and sudden death have a significant and apparent economic impact on poultry farms, especially in fast-growing countries. Fowl adenovirus (FAdV) is a causative agent of HHS found in various animals, such as chicken. It causes rapid death and outbreak in young broiler farms, with an accumulation of jelly-like and straw-colored liquid in the heart pericardium, swollen and pale liver, heart muscles petechial and ecchymosis hemorrhages, swollen and pale kidneys, and atrophy thymus seen in gross pathology. Age, host, farm management, biosecurity plan, co-infectious, and level of immunity are risk factors for HHS. Angara is mainly found in Asian countries in Central and South America. The disease is transmitted vertically, like egg-transmitted, and horizontally by oral-fecal route. Rapid diagnostics and active monitoring systems in poultry farms can prevent and control Angara disease in farms. Vaccination and farm-specific biosecurity strategies reduce the initiation of outbreaks in farms. Treatment is based on activating humoral immunity against the virus, similar to vaccination.

Using precision tools to manage and evaluate the effects of mineral and protein/energy supplements fed to grazing beef heifers.

Our objectives were to develop a Mobile Cow Command Center (MCCC) capable of precision monitoring of grazing heifers to 1) examine the relationship between supplement intake on concentrations of liver mineral and blood metabolites and 2) examine activity, reproductive, and health behavior. Yearling crossbred Angus heifers (N = 60; initial BW = 400.4 ± 6.2 kg) were fitted with radio frequency identification ear tags that allowed access to electronic feeders (SmartFeed system; C-Lock Inc., Rapid City, SD), and with activity monitoring tags (CowManager B.V., the Netherlands) that monitored reproductive, feeding, and health-associated behaviors. Heifers were assigned randomly to one of three treatments for a 57-day monitoring period: 1) no supplement (CON; N = 20), 2) free choice mineral (MIN; Purina Wind and Rain Storm [Land O'Lakes, Inc.], N = 20), or 3) free choice energy and mineral supplement (NRG; Purina Accuration Range Supplement 33 with added MIN [Land O'Lakes, Inc.], N = 20). Consecutive day body weights, blood, and liver biopsies were collected at pasture turnout and final day of monitoring. By design, mineral intake was greatest in MIN heifers (49 ± 37 g/d) and energy supplement intake was greatest in NRG heifers (1,257 ± 37 g/d). Final BW and ADG were similar among treatments (P > 0.42). Concentrations of glucose on day 57 were greater (P = 0.01) in NRG compared with CON and MIN heifers. Liver concentrations of Se and Fe on day 57 were greater (P < 0.05) in NRG heifers than CON, with MIN being intermediate. Activity tags reported NRG heifers spent less time eating (P < 0.0001) and more time (P < 0.0001) being "highly active" than MIN with CON heifers being intermediate. Data retrieved from activity tags identified 16 of 28 pregnant heifers exhibiting some type of estrus-associated behavior even after confirmation of established pregnancy. The activity monitoring system triggered a total of 146 health alerts from 34 of the 60 heifers monitored, but only 3 heifers of the heifers initiating an electronic health alert needed clinical treatment. However, animal care staff identified nine additional heifers that required treatment for which no electronic health alert was generated. The electronic feeders successfully controlled intake of individual heifers managed in groups pastures; however, the activity monitoring system misrepresented estrus and health events.

AHMERS: Active Health Monitoring and Emergency Response System

AHMERS, active health monitoring and emergency response system, is a mobile application-based system that wirelessly connects to a smartwatch to constantly monitor the human body and respond to sudden changes in vital data, in case of emergency. This app monitors heartbeat rate, blood oxygen saturation, body temperature, and compares them with pre-set normal values. If the data deviates or the user presses one of the emergency switches, the app immediately asks the person if he/she is ok. If the person fails to respond within a few seconds to the “Are you ok?” message, the app continues to send out distress signals to pre-set phone numbers and server along with the person's altitude, latitude and longitude, and current location on the map so that help can be sent quickly. It can determine whether a person is suffering from a health-related problem such as heart failure, Corona virus infection, hypothermia, etc.

Review of modern demand control solutions and technologies for HVAC operation

HVAC systems, which use traditional control strategies with fixed ventilation rates or with ventilation rate schedules, do not adjust according to the required IAQ and thermal comfort. As a result, building spaces are being over or under-ventilated. In this paper, the latest modern solutions for demand-controlled HVAC system operation are analyzed, based on the review of existing studies. Such modern technologies as human detection systems, computer vision, and neural network applications are looked at. Different types of human presence detection are presented based on the applied technology. The most common ones are indirect detection based on the usage data of existing IT equipment, and direct detection through the use of passive infrared sensors, wearable tags, and vision sensors. Also, the potential solutions of human activity monitoring, skin temperature, and clothing level detection systems are examined. The studies discussed in this paper show real application examples and prove the benefits of using the technologies for the control of ventilation systems in various building types. Research has shown that such technologies have a favorable effect on both indoor air quality and system energy consumption. In the future, the ventilation system should be equipped with cameras for a more accurate analysis of the room and occupancy. Also, the systems must consider occupant behavior, activity, and other information, which can be used for indoor environment quality improvement. Based on the gained knowledge a sensor capable of human detection, accounting, and location marking is developed.

Daily activity monitoring system designed for elderly people using hidden Markov models based on real world datasets

Daily activity monitoring system designed for elderly people using hidden Markov models based on real world datasets

Daily activity monitoring system designed for elderly people using hidden Markov models based on real world datasets

Daily activity monitoring system designed for elderly people using hidden Markov models based on real world datasets

MODIS e OLI/TIRS Para Estimar TVDI e a Umidade da Superfície em Programas de Monitoramento Agrícola

Abstract One of the major challenges for effective agricultural activity monitoring systems is defining robust indicators of spatial and temporal variability for the main risk factors associated with crop production. In this context, this study aimed to analyze the potential of the Temperature-Vegetation Dryness Index (TVDI), obtained by terrestrial and orbital sensors from soybean production areas in southern Brazil, in generating spatial and temporal patterns of the main risk factor, surface moisture, to be incorporated in operational agricultural monitoring systems. For this purpose, MODIS Terra and Landsat-8 OLI/TIRS sensor images were used, as well as data from surface positioned sensors to serve as a reference. The study area encompassed one soybean crop area, soybean mapped crop areas near the experimental area, and the municipality of Carazinho-RS. The experimental area was analyzed during the soybean growing season. As the TVDI data estimated by OLI/TIRS and MODIS sensors were coherent and robust, both sensors can be used in conjunction for agricultural risk monitoring. Its main features are continuous monitoring of large production regions by TVDIMODIS and spatial distribution detailing by TVDIOLI/TIRS in critical periods to water deficit.