Activity Monitoring System Research Articles

Evaluation of Four Different Automated Activity Monitoring Systems to Identify Anovulatory Cows in Early Lactation.

The objective of this study was to evaluate four different automated activity monitoring (AAM) systems to identify anovulatory cows in early lactation. A total of 852 lactating Holstein cows (221 primiparous and 631 multiparous cows) from four commercial dairy herds were enrolled. On each farm, cows were equipped with a respective AAM system (SB: Smartbow; HT: Heatime; DP: Delpro; and CM: CowManager). Each cow was sampled three times within the voluntary waiting period (VWP) in a two-week interval to detect the blood progesterone (P4) concentration. Cows were classified based on the concentration of P4 as follows: (1) none of the three blood P4 concentrations exceeded 1.0 ng/mL (anovulatory); (2) at least one of the three blood P4 concentrations was above 1.0 ng/mL (ovulatory). Cows were classified based on estrus alerts as follows: (1) no estrus alert was detected by an AAM system from 7 to 60 DIM (anestrus); (2) at least one estrus alert was detected by an AAM system from 7 to 60 DIM (estrus). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each AAM system for anovulatory cows [(SB: 77.6%; 26.8%; 89.3%; 36.7%; and 84.1%); (HT: 79.2%; 63.6%; 83.6%; 52.8%; and 88.9%); (DP: 47.2%; 78.8%; 41.5%; 19.5%; and 91.6%); (CM: 80.5%; 23.7%; 92.7%; 39.1%; and 85.3%)].

Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

Online monitoring of methane transfer rates unveils nitrogen fixation dynamics in Methylococcus capsulatus.

This study explores methane utilization by the methanotrophic microorganism Methylococcus capsulatus (Bath) for biomass production, presenting a promising approach to mitigate methane emissions and foster the development sustainable biomaterials. Traditional screening methods for gas cultivations involve either serum flasks without online monitoring or costly, low-throughput fermenters. To address these limitations, the Respiration Activity MOnitoring System was augmented with methane sensors for real-time methane transfer rate (MTR) monitoring in shake flasks. Utilizing online monitoring of the MTR in shake flasks results in enhanced throughput and cost-effectiveness for cultivating M. capsulatus. Simultaneous monitoring of transfer rates for oxygen, methane, and carbon dioxide was conducted in up to eight shake flasks, ensuring the success of the cultivation process. Alterations in methane-to-oxygen transfer rate ratios and carbon fixation rates reveal the impact of transfer limitations on microbial growth. Detection of gas transfer limitations, exploration of process parameter influences, and investigations of medium components were enabled by the introduced method. Optimal nitrogen concentrations could be determined to ensure optimal growth. This streamlined approach accelerates the screening process, offering efficient investigations into metabolic effects, limitations, and parameter influences in gas fermentations without the need for elaborate offline sampling, significantly reducing costs and enhanced reproducibility.

WiLife: Long-term Daily Status Monitoring and Habit Mining of the Elderly Leveraging Ubiquitous Wi-Fi Signals

The global aging demographic underscores the imperative for continuous in-home monitoring of the empty-nest elderly, ensuring their safety and well-being. The widespread deployment of Wi-Fi infrastructure has paved the way to monitor the elderly in a non-intrusive and privacy-preserving manner. Numerous studies have explored the potential of utilizing Wi-Fi signals to address urgent life safety concerns such as fall detection and vital sign monitoring. However, apart from these acute safety issues, the early detection of potential disease symptoms and managing the progression of chronic diseases are also crucial for elderly care, which calls for long-term and continuous monitoring of the elderly’s daily routines. Unfortunately, challenges like continuous activity segmentation and location/orientation dependencies have hindered the implementation of a long-term, around-the-clock activity monitoring system for the elderly. This work introduces ”WiLife”, a cutting-edge Wi-Fi based framework for continuous monitoring of the elderly’s spatio-temporal daily status information. Specifically, WiLife adopts a strategy of partitioning living spaces into functional areas and categorizing daily activities into atomic states . By encapsulating daily life status into a unique series of triple unit format: \(\left\langle{{Time, Area, State}}\right\rangle\) , WiLife is able to offer valuable insights into when, where, and how activities occur. Field implementations spanning 1,080 hours ( \(45days\times 24hours\) ) in real-world home environments highlight WiLife’s exceptional capability in understanding individual living habits and timely detection of irregularities.

Neuroinflammation and Neurometabolomic Profiling in Fentanyl Overdose Mouse Model Treated with Novel β-Lactam, MC-100093, and Ceftriaxone.

Opioid-related deaths are attributed to overdoses, and fentanyl overdose has been on the rise in many parts of the world, including the USA. Glutamate transporter 1 (GLT-1) has been identified as a therapeutic target in several preclinical models of substance use disorders, and β-lactams effectively enhance its expression and function. In the current study, we characterized the metabolomic profile of the nucleus accumbens (NAc) in fentanyl-overdose mouse models, and we evaluated the protective effects of the functional enhancement of GLT-1 using β-lactams, ceftriaxone, and MC-100093. BALB/c mice were divided into four groups: control, fentanyl, fentanyl/ceftriaxone, and fentanyl/MC-100093. While the control group was intraperitoneally (i.p.) injected with normal saline simultaneously with other groups, all fentanyl groups were i.p. injected with 1 mg/kg of fentanyl as an overdose after habituation with four repetitive non-consecutive moderate doses (0.05 mg/kg) of fentanyl for a period of seven days. MC-100093 (50 mg/kg) and ceftriaxone (200 mg/kg) were i.p. injected from days 5 to 9. Gas chromatography-mass spectrometry (GC-MS) was used for metabolomics, and Western blotting was performed to determine the expression of target proteins. Y-maze spontaneous alternation performance and the open field activity monitoring system were used to measure behavioral manifestations. Fentanyl overdose altered the abundance of about 30 metabolites, reduced the expression of GLT-1, and induced the expression of inflammatory mediators IL-6 and TLR-4 in the NAc. MC-100093 and ceftriaxone attenuated the effects of fentanyl-induced downregulation of GLT-1 and upregulation of IL-6; however, only ceftriaxone attenuated fentanyl-induced upregulation of TRL4 expression. Both of the β-lactams attenuated the effects of fentanyl overdose on locomotor activities but did not induce significant changes in the overall metabolomic profile. Our findings revealed that the exposure to a high dose of fentanyl causes alterations in key metabolic pathways in the NAc. Pretreatment with ceftriaxone and MC-100093 normalized fentanyl-induced downregulation of GLT-1 expression with subsequent attenuation of neuroinflammation as well as the hyperactivity, indicating that β-lactams may be promising drugs for treating fentanyl use disorder.

Comparison of two intravaginal progesterone-releasing devices in lactating Holstein cows synchronized with a 5-d GnRH-based timed-AI protocol

The objective of the study was to compare the effectiveness of CIDR vs. PRID-Delta devices for use in a 5-day Ovsynch protocol for TAI in lactating Holstein cows that were either not in estrus after the end of the voluntary waiting period or non-pregnant and not returning to estrus following the previous AI. Cows fitted with a collar-mounted automated activity monitoring system (Alta Cow Watch) were subjected to a standard 5-d Ovsynch protocol [100 μg of gonadorelin (GnRH) on Day 0 and 500 μg of cloprostenol on Days 5 and 6] and allocated randomly to receive either an intravaginal device containing 1.35 g (CIDR; n = 304) or 1.55 g (PRID ® DELTA; n = 304) of progesterone between Day 0 and 5. All cows received a second administration of GnRH at approximately 56 h and timed-AI (TAI) 72 h after intravaginal device removal. Inseminations were done using conventional frozen-thawed semen. Estrus events prior to TAI were recorded and transrectal ultrasonography was done on Day 0 to determine presence of a corpus luteum (CL) and 33 and 61 d post-TAI, respectively, to diagnose and confirm pregnancy. Cows had an average of 2.2 lactations, 124.3 days in milk, and a milk yield of 43.6 kg/d at enrollment. The overall percentage of cows with a CL at initiation of treatment was 68.8 % and did not differ between treatment groups. Cows with a CL had greater pregnancy per AI (P/AI) at 33 and 61 d post-TAI than cows without a CL (P < 0.01; 46.9 and 42.3 % vs. 32.1 and 27.4 %, respectively). The overall percentage of cows that expressed estrus prior to TAI was 24.8 % and did not differ between treatment groups; however, estrus expression prior to TAI affected P/AI at 33 and 61 d post-TAI (P < 0.01; 53.6 and 49.0 % vs. 38.5 and 33.9 % for those expressing or not expressing estrus, respectively). Pregnancy per AI at 33 d post-TAI tended to differ between treatment groups (P = 0.08; 46.1 vs. 38.5 % for PRID and CIDR groups, respectively) and P/AI at 61 d post-TAI was greater (P < 0.01) for PRID-treated cows (43.8 %) compared to CIDR-treated cows (31.6 %). Thus, PRID-treated cows had lower pregnancy loss than CIDR-treated cows (P < 0.01; 5.0 vs. 17.9 %). Also, treatment with a PRID tended (P = 0.08) to result in fewer twin pregnancies (7.9 vs. 14.5 % for PRID and CIDR treated cows, respectively). In conclusion, lactating dairy cows subjected to a 5-d Ovsynch TAI protocol plus a PRID-Delta had greater P/AI at 61 d post-TAI, lower pregnancy loss between 33 and 61 d post-TAI, and fewer twin pregnancies compared to cows subjected to a 5-d Ovsynch protocol plus a CIDR.

Self-Assessment of Cochlear Health by New Cochlear Implant Recipients: Daily Impedance, Electrically Evoked Compound Action Potential and Electrocochleography Measurements Over the First Three Postoperative Months.

In newly implanted cochlear implant (CI) users, electrically evoked compound action (eCAPs) and electrocochleography (ECochGs) will remain stable over time. Electrode impedances will increase immediately postimplantation due to the initial inflammatory response, before decreasing after CI switch-on and stabilizing thereafter. The study of cochlear health (CH) has several applications, including explaining variation in CI outcomes, informing CI programming strategies, and evaluating the safety and efficacy of novel biological treatments for hearing loss. Very early postoperative CH patterns have not previously been intensively explored through longitudinal daily testing. Thanks to technological advances, electrode impedances, eCAPs, and ECochGs can be independently performed by CI users at home to monitor CH over time. A group of newly implanted CI users performed daily impedances, eCAPs, and ECochGs for 3 months at home, starting from the first day postsurgery (N = 7) using the Active Insertion Monitoring system by Advanced Bionics. Measurement validity of 93.5, 93.0, and 81.6% for impedances, eCAPs, and ECochGs, respectively, revealed high participant compliance. Impedances increased postsurgery before dropping and stabilizing after switch-on. eCAPs showed good stability, though statistical analyses revealed a very small but significant increase in thresholds over time. Most ECochG thresholds did not reach the liberal signal-to-noise criterion of 2:1, with low threshold stability over time. Newly implanted CI recipients can confidently and successfully perform CH recordings at home, highlighting the valuable role of patients in longitudinal data collection. Electrode impedances and eCAPs are promising objective measurements for evaluating CH in newly implanted CI users.

Stray light analysis and suppression of high-resolution camera line-of-sight variation real-time monitoring system (LoS Var RTMS).

Due to environmental disturbances during emission and operation processes, the line-of-sight of a high-resolution camera is highly prone to variation, resulting in a significant decrease in image positioning accuracy. To address this issue, we propose a line-of-sight variation real-time monitoring system (LoS Var RTMS). A full-link active optical monitoring system is established using laser, and a high-resolution camera is coupled to a star tracker, thereby enhancing the on-orbit calibration and image positioning accuracy of the camera. However, the high-resolution camera LoS Var RTMS belongs to a high stray light sensitivity system. Stray light entering the star tracker from the high-resolution camera through the relay reflective system can significantly affect the imaging of the star tracker, leading to the failure of the monitoring system. Therefore, a comprehensive and accurate analysis of the stray light propagation path in the monitoring system is necessary to ensure its normal operation. In response to this issue, this article proposes a stray light analysis method based on the surface subdivision method, and utilizes inverse ray tracing to meticulously analyze the propagation path of stray light. Additionally, to address the impact of stray light on laser light in the LoS Var RTMS, an inner baffle polarization filtering system is designed to effectively suppress the influence of external strong stray light on the monitoring system. The results demonstrate that the stray light analysis method based on the surface subdivision method not only enhances the integrity and accuracy of the analysis of stray light propagation paths within the monitoring system, but also significantly improves the precision of stray light analysis. It provides a reference for the analysis of stray light in high stray light sensitive systems. Moreover, the design of the inner baffle polarization filtering system effectively distinguishes between lasers and stray light, suppressing stray light within the high-resolution camera LoS Var RTMS and ensuring its normal operation.

Research on entertainment robots based on artificial intelligence interaction for human posture recognition and sports activity monitoring

With the application of artificial intelligence technology in sports activity monitoring, new technologies such as interactive robots and the Internet of Things are being more widely used, which can provide entertainment and interactive guidance for sports training. This article analyzes the research on human pose recognition and sports activity monitoring of entertainment robots based on artificial intelligence interaction. This paper designs and develops an entertainment robot system that can recognize human posture and monitor physical activities. Users can interact with the robot and get real-time feedback about their posture and activities, thereby improving motor skills and physical fitness. After in-depth analysis of the theory of dynamic Bayesian network, we used EM algorithm to build a dynamic Bayesian network model, carried out detailed research on the random variables represented by the nodes of the dynamic model, and used the Internet of Things human posture recognition technology to extract the features of human behavior according to the characteristics of human behavior. The results show that the attitude recognition accuracy can be improved by adding the dynamic Bayesian network based on the random forest algorithm. The entertainment robot can accurately identify human posture and monitor sports activities, and has good real-time and stability. Through interaction with the robot, users can get personalized suggestions for individual postures and activities, improving the training effect and enjoyment of physical activities. Therefore, through the in-depth research and analysis of the dynamic Bayesian network and the human posture recognition of the Internet of Things, it is found that the sports activity monitoring system proposed in this paper can comprehensively monitor students’ sports activities, so as to achieve the goal of comprehensively improving students’ physical fitness.

Highlighting Unseen Activity Through 48-Hour Continuous Measurement in Subacute Stroke Rehabilitation: Preliminary Cohort Study.

Motor impairments not only lead to a significant reduction in patient activity levels but also trigger a further deterioration in motor function due to deconditioning, which is an issue that is particularly pronounced during hospitalization. This deconditioning can be countered by sustaining appropriate activity levels. Activities that occur outside of scheduled programs, often overlooked, are critical in this context. Wearable technology, such as smart clothing, provides a means to monitor these activities. This study aimed to observe activity levels in patients who had strokes during the subacute phase, focusing on both scheduled training sessions and other nontraining times in an inpatient rehabilitation environment. A smart clothing system is used to simultaneously measure heart rate and acceleration, offering insights into both the amount and intensity of the physical activity. In this preliminary cohort study, 11 individuals undergoing subacute stroke rehabilitation were enrolled. The 48-hour continuous measurement system, deployed at admission and reassessed 4 weeks later, monitored accelerometry data for physical activity (quantified with a moving SD of acceleration [MSDA]) and heart rate for intensity (quantified with percent heart rate reserve). The measurements were performed using a wearable activity monitoring system, the hitoe (NTT Corporation and Toray Industries, Inc) system comprising a measuring garment (wear or strap) with integrated electrodes, a data transmitter, and a smartphone. The Functional Independence Measure was used to assess the patients' daily activity levels. This study explored factors such as differences in activity during training and nontraining periods, correlations with activities of daily living (ADLs) and age, and changes observed after 4 weeks. A significant increase was found in the daily total MSDA after the 4-week program, with the average percent heart rate reserve remaining consistent. Physical activity during training positively correlated with ADL levels both at admission (ρ=0.86, P<.001) and 4 weeks post admission (ρ=0.96, P<.001), whereas the correlation between age and MSDA was not significant during training periods at admission (ρ=-0.41, P=.21) or 4 weeks post admission (ρ=-0.25, P=.45). Conversely, nontraining activity showed a negative correlation with age, with significant negative correlations with age at admission (ρ=-0.82, P=.002) and 4 weeks post admission (ρ=-0.73, P=.01). Inpatient rehabilitation activity levels were positively correlated with ADL levels. Further analysis revealed a strong positive correlation between scheduled training activities and ADL levels, whereas nontraining activities showed no such correlation. Instead, a negative correlation between nontraining activities and age was observed. These observations suggest the importance of providing activity opportunities for older patients, while it may also suggest the need for adjusting the activity amount to accommodate the potentially limited fitness levels of this demographic. Future studies with larger patient groups are warranted to validate and further elucidate these findings.

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data.

Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings-8 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature-humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.

Mobile Activity Monitoring System Using Android Spy

Now a days Android mobiles are everywhere in the world, but if we consider the area such as IT industry, organizations, educational institutes, businesses in these sectors all the employee with their android smart phone performs much activities. Every company, organization having their own policies, rules, future projects so in such cases the privacy, security and confidentiality must be maintained by the employee of the organization. So it’s very important to track their mobile phones whether they are leaking the confidential data or they are doing wrong call, wrong SMS, or crossing out the organization’s geographical area in working hours. After considering all these factors, we implemented the system “Mobile Activity Monitoring System Using Android Spy” This system is implemented for tracking the daily activity of the users with their android mobiles. The information such as call log, SMS along with its date and time will be tracked and updated to the server this server will be monitored by the administrator. This information can be maintained for security purpose of the organization such as leaking the confidential data and maintaining policies of organization. Key Words: Android, Monitoring, Tracking, Call log, Date, SMS

Live Cell Monitoring of Separase Activity, a Key Enzymatic Reaction for Chromosome Segregation, with Chimeric FRET-Based Molecular Sensor upon Cell Cycle Progression.

Separase is a key cysteine protease in the separation of sister chromatids through the digestion of the cohesin ring that inhibits chromosome segregation as a trigger of the metaphase-anaphase transition in eukaryotes. Its activity is highly regulated by binding with securin and cyclinB-CDK1 complex. These bindings prevent the proteolytic activity of separase until the onset of anaphase. Chromosome missegregation and aneuploidy are frequently observed in malignancies. However, there are some difficulties in biochemical examinations due to the instability of separase in vitro and the fact that few spatiotemporal resolution approaches exist for monitoring live separase activity throughout mitotic processes. Here, we have developed FRET-based molecular sensors, including GFP variants, with separase-cleavable sequences as donors and covalently attached fluorescent dyes as acceptor molecules. These are applicable to conventional live cell imaging and flow cytometric analysis because of efficient live cell uptake. We investigated the performance of equivalent molecular sensors, either localized or not localized inside the nucleus under cell cycle control, using flow cytometry. Synchronized cell cycle progression rendered significant separase activity detections in both molecular sensors. We obtained consistent outcomes with localized molecular sensor introduction and cell cycle control by fluorescent microscopic observations. We thus established live cell separase activity monitoring systems that can be used specifically or statistically, which could lead to the elucidation of separase properties in detail.

Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree

A system has been developed to remotely, continuously, and quantitatively measure the physiological activity of trees. The developed tree physiological activity monitoring (TPAM) system is equipped with electrical impedance, temperature, and light intensity measurement functions. In the two-contact impedance measurement method used in the previous plant impedance measurement, errors due to the polarization impedance of the electrodes could not be avoided. The developed TPAM system adopted a four-contact measurement method that could avoid polarization impedance errors, and, with it, the long-term monitoring of zelkova trees was performed. The monitoring of seasonal changes was conducted from July to November, and an impedance change pattern that repeated on a daily basis was observed in the short term, and an overall increase in the impedance was observed in the long term. Impedance changes related to daily temperature changes were observed even after all the tree leaves had fallen, meaning that this effect should be excluded when using impedance to evaluate tree vitality. For this reason, the influence of temperature fluctuations was excluded by using only the impedance values at the same daily temperature of 25 degrees from July to November. The analysis results at 25 degrees showed that the tree impedance value increased linearly by 8.7 Ω per day. The results of this series of long-term monitoring and analysis revealed that the ambient temperature must be taken into account in the evaluation of tree physiological activity based on electrical impedance.

Identification and In Vivo Evaluation of Myelination Agent PIPE-3297, a Selective Kappa Opioid Receptor Agonist Devoid of β-Arrestin-2 Recruitment Efficacy.

Structure-activity relationship studies led to the discovery of PIPE-3297, a fully efficacious and selective kappa opioid receptor (KOR) agonist. PIPE-3297, a potent activator of G-protein signaling (GTPγS EC50 = 1.1 nM, 91% Emax), did not elicit a β-arrestin-2 recruitment functional response (Emax < 10%). Receptor occupancy experiments performed with the novel KOR radiotracer [3H]-PIPE-3113 revealed that subcutaneous (s.c.) administration of PIPE-3297 at 30 mg/kg in mice achieved 90% occupancy of the KOR in the CNS 1 h post dose. A single subcutaneous dose of PIPE-3297 in healthy mice produced a statistically significant increase of mature oligodendrocytes (P < 0.0001) in the KOR-enriched striatum, an effect that was not observed in animals predosed with the selective KOR antagonist norbinaltorphimine. An equivalent dose given to mice in an open-field activity-monitoring system revealed a small KOR-independent decrease in total locomotor activity versus vehicle measured between 60 and 75 min post dose. Daily doses of PIPE-3297 at both 3 and 30 mg/kg s.c. reduced the disease score in the mouse experimental autoimmune encephalomyelitis (EAE) model. Visually evoked potential (VEP) N1 latencies were also significantly improved versus vehicle in both dose groups, and latencies matched those of untreated animals. Taken together, these findings highlight the potential therapeutic value of functionally selective G-protein KOR agonists in demyelinating disease, which may avoid the sedating side effects typically associated with classical nonbiased KOR agonists.

Knowledge, Attitude and Practice on Adverse Drug Reactions and its Reporting Among Nursing Students

Adverse Drug Reaction (ADR) is defined as ‘a response to a medicine which is noxious and unintended, and which occurs at doses normally used in man’. ADR reporting does not yet appear to be regarded by healthcare practitioners as an integral element of standard professional practice. This is mainly because there isn't a robust and active ADR monitoring system or a culture of reporting among medical personnel. If given sufficient knowledge and skills during their undergraduate training careers, medical students could play a significant role and shift the paradigm in the successful implementation of a Pharmacovigilance program. At the moment, they don't have any significant roles due to insufficient training they received regarding ADR reporting. Thus it can be reasoned that effective educational intervention regarding ADR reporting improves Spontaneous Reporting. This study improves the Knowledge Attitude and Practice on ADRs among nursing students as they are the next generation of healthcare workers who will be the first point of contact in case a patient develops an adverse drug reaction in hospital settings. The goal of the study is to assess and improve the Knowledge, Attitude and Practice on Adverse Drug Reactions and its reporting among nursing students. The study also aimed to identify the barriers associated with reporting of Adverse Drug Reactions among the study subjects. An educational interventional study was carried out among 150 samples in selected nursing colleges of Bengaluru. The data was collected by using self designed content validated questionnaire and responses were recorded. All data obtained was processed and analyzed by using Microsoft excel. It was found that most of the student’s knowledge towards Adverse Drug Reactions and its reporting were found to be improved after suitable intervention. Lack of training, less exposure, absence of professional confidence was identified as barriers associated with reporting of ADR. With the right coaching and guidance provided by means of educational programs, this study was able to further enhance nursing student’s knowledge, attitude and practice regarding Adverse Drug Reactions and its reporting.

Supporting Community First Responders in Aging in Place: An Action Design for a Community-Based Smart Activity Monitoring System

To maintain elderly individuals’ ability to live independently in their own homes, technologies such as sensors and the Internet of Things can be used to facilitate real-time monitoring and automate emergency detection and alerts to enable first responders to deliver help on time. This study examines the design problems associated with such activity monitoring systems, particularly how to support the situational awareness of community first responders who are located remotely and do not have professional healthcare knowledge. Based on a three-year action design research study that investigated a sensor-based monitoring system for elderly people deployed in a natural community setting, we develop abstract design knowledge. Four design principles aimed at enhancing responders’ perceptions, comprehension, and projection of information are proposed to promote informed decision-making and timely responses by drawing on the situational awareness model. In addition to the nascent design knowledge regarding remote activity monitoring in different instances, this study extends our knowledge of the community-based model by foregrounding its multiplicity and the active roles of community stakeholders in providing care to elderly people.

Smarter Aging: Developing A Foundational Elderly Activity Monitoring System with AI and GUI Interface

Smarter Aging: Developing A Foundational Elderly Activity Monitoring System with AI and GUI Interface

Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

Elderly and visually impaired indoor activity monitoring based on Wi-Fi and Deep Hybrid convolutional neural network

A drop in physical activity and a deterioration in the capacity to undertake daily life activities are both connected with ageing and have negative effects on physical and mental health. An Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system that keeps tabs on a person’s routine and steps in if a change in behaviour or a crisis might greatly help an elderly person or a visually impaired. These individuals may find greater freedom with the help of an EVHAM system. As the backbone of human-centric applications like actively supported living and in-home monitoring for the elderly and visually impaired, an EVHAM system is essential. Big data-driven product design is flourishing in this age of 5G and the IoT. Recent advancements in processing power and software architectures have also contributed to the emergence and development of artificial intelligence (AI). In this context, the digital twin has emerged as a state-of-the-art technology that bridges the gap between the real and virtual worlds by evaluating data from several sensors using artificial intelligence algorithms. Although promising findings have been reported by Wi-Fi-based human activity identification techniques so far, their effectiveness is vulnerable to environmental variations. Using the environment-independent fingerprints generated from the Wi-Fi channel state information (CSI), we introduce Wi-Sense. This human activity identification system employs a Deep Hybrid convolutional neural network (DHCNN). The proposed system begins by collecting the CSI with a regular Wi-Fi Network Interface Controller. Wi-Sense uses the CSI ratio technique to lessen the effect of noise and the phase offset. The t- Distributed Stochastic Neighbor Embedding (t-SNE) is used to eliminate unnecessary data further. The data dimension is decreased, and the negative effects on the environment are eliminated in this process. The resulting spectrogram of the processed data exposes the activity’s micro-Doppler fingerprints as a function of both time and location. These spectrograms are put to use in the training of a DHCNN. Based on our findings, EVHAM can accurately identify these actions 99% of the time.