Continuous Activity Monitoring Research Articles

Activity Recognition Using Different Sensor Modalities and Deep Learning

In recent years, human activity monitoring and recognition have gained importance in providing valuable information to improve the quality of life. A lack of activity can cause health problems including falling, depression, and decreased mobility. Continuous activity monitoring can be useful to prevent progressive health problems. With this purpose, this study presents a wireless smart insole with four force-sensitive resistors (FSRs) that monitor foot contact states during activities for both indoor and outdoor use. The designed insole is a compact solution and provides walking comfort with a slim and flexible structure. Moreover, the inertial measurement unit (IMU) sensors designed in our previous study were used to collect 3-axis accelerometer and 3-axis gyroscope outputs. Smart insoles were located in the shoe sole for both right and left feet, and two IMU sensors were attached to the thigh area of each leg. The sensor outputs were collected and recorded from forty healthy volunteers for eight different gait-based activities including walking uphill and descending stairs. The obtained datasets were separated into three categories; foot contact states, the combination of acceleration and gyroscope outputs, and a set of all sensor outputs. The dataset for each category was separately fed into deep learning algorithms, namely, convolutional long–short-term memory neural networks. The performance of each neural network for each category type was examined. The results show that the neural network using only foot contact states presents 90.1% accuracy and provides better performance than the combination of acceleration and gyroscope datasets for activity recognition. Moreover, the neural network presents the best results with 93.4% accuracy using a combination of all the data compared with the other two categories.

Activity in Group-Housed Home Cages of Mice as a Novel Preclinical Biomarker in Oncology Studies.

Improving experimental conditions in preclinical animal research is a major challenge, both scientifically and ethically. Automated digital ventilated cages (DVC®) offer the advantage of continuous monitoring of animal activity in their home-cage. The potential utility of this technology remains understudied and deserves investigation in the field of oncology. Using the DVC® platform, we sought to determine if the continuous assessment of locomotor activity of mice in their home cages can serve as useful digital readout in the monitoring of animals treated with the reference oncology compounds cisplatin and cyclophosphamide. SCID mice of 14 weeks of age were housed in DVC® cages in groups of four and followed with standard and digital examination before and after treatment over a 17-day total period. DVC® detected statistically significant effects of cisplatin on the activity of mice in the short and long term, as well as trends for cyclophosphamide. The activity differences between the vehicle- and chemotherapy-treated groups were especially marked during the nighttime, a period when animals are most active and staff are generally not available for regular checks. Standard clinical parameters, such as body weight change and clinical assessment during the day, provided additional and complementary information. The DVC® technology enabled the home cage monitoring of mice and non-invasive detection of animal activity disturbances. It can easily be integrated into a multimodal monitoring approach to better capture the different effects of oncology drugs on anti-tumor efficacy, toxicity, and safety and improve translation to clinical studies.

In-ear integrated sensor array for the continuous monitoring of brain activity and of lactate in sweat

Owing to the proximity of the ear canal to the central nervous system, in-ear electrophysiological systems can be used to unobtrusively monitor brain states. Here, by taking advantage of the ear’s exocrine sweat glands, we describe an in-ear integrated array of electrochemical and electrophysiological sensors placed on a flexible substrate surrounding a user-generic earphone for the simultaneous monitoring of lactate concentration and brain states via electroencephalography, electrooculography and electrodermal activity. In volunteers performing an acute bout of exercise, the device detected elevated lactate levels in sweat concurrently with the modulation of brain activity across all electroencephalography frequency bands. Simultaneous and continuous unobtrusive in-ear monitoring of metabolic biomarkers and brain electrophysiology may allow for the discovery of dynamic and synergetic interactions between brain and body biomarkers in real-world settings for long-term health monitoring or for the detection or monitoring of neurodegenerative diseases.

Usability and ease of use of long-term remote monitoring of physical activity for individuals with acquired brain injury in community: a qualitative analysis.

Objective and continuous monitoring of physical activity over the long-term in the community is perhaps the most important step in the paradigm shift toward evidence-based practice and personalized therapy for successful community integration. With the advancement in technology, physical activity monitors have become the go-to tools for objective and continuous monitoring of everyday physical activity in the community. While these devices are widely used in many patient populations, their use in individuals with acquired brain injury is slowly gaining traction. The first step before using activity monitors in this population is to understand the patient perspective on usability and ease of use of physical activity monitors at different wear locations. However, there are no studies that have looked at the feasibility and patient perspectives on long-term utilization of activity monitors in individuals with acquired brain injury. This pilot study aims to fill this gap and understand patient-reported aspects of the feasibility of using physical activity monitors for long-term use in community-dwelling individuals with acquired brain injury. This pilot study found that patients with acquired brain injury faced challenges specific to their functional limitations and that the activity monitors worn on the waist or wrist may be better suited in this population. The unique wear location-specific challenges faced by individuals with ABI need to be taken into account when selecting wearable activity monitors for long term use in this population.

Continuous home cage monitoring of activity and sleep in mice during repeated paroxetine treatment and discontinuation.

Non-invasive home cage monitoring is emerging as a valuable tool to assess the effects of experimental interventions on mouse behaviour. A field in which these techniques may prove useful is the study of repeated selective serotonin reuptake inhibitor (SSRI) treatment and discontinuation. SSRI discontinuation syndrome is an under-researched condition that includes the emergence of sleep disturbances following treatment cessation. We used passive infrared (PIR) monitoring to investigate changes in activity, sleep, and circadian rhythms during repeated treatment with the SSRI paroxetine and its discontinuation in mice. Male mice received paroxetine (10mg/kg/day, s.c.) for 12days, then were swapped to saline injections for a 13day discontinuation period and compared to mice that received saline injections throughout. Mice were continuously tracked using the Continuous Open Mouse Phenotyping of Activity and Sleep Status (COMPASS) system. Repeated paroxetine treatment reduced activity and increased behaviourally-defined sleep in the dark phase. These effects recovered to saline-control levels within 24h of paroxetine cessation, yet there was also evidence of a lengthening of sleep bouts in the dark phase for up to a week following discontinuation. This study provides the first example of how continuous non-invasive home cage monitoring can be used to detect objective behavioural changes in activity and sleep during and after drug treatment in mice. These data suggest that effects of paroxetine administration reversed soon after its discontinuation but identified an emergent change in sleep bout duration, which could be used as a biomarker in future preclinical studies to prevent or minimise SSRI discontinuation symptoms.

Impact of a Hands-On Continuous Glucose Monitoring Activity for Third-Year Pharmacy Students

Impact of a Hands-On Continuous Glucose Monitoring Activity for Third-Year Pharmacy Students

IoT-Integrated System for Continuous Assessment of Elementary School Martial Arts Education with Automated Classifier

The IoT system would facilitate the continuous assessment of students' martial arts development by tracking their progress over time. The integration of Internet of Things (IoT) technology in elementary school martial arts education has the potential to revolutionize the learning experience and promote holistic development. This paper proposed an IoT-integrated system for the assessment of elementary school martial arts education. The proposed architecture incorporates an Automated Ranking Parallel Classifier (ARPC) model. The proposed ARPC system incorporates physical health monitoring and assessment, providing real-time data on students' physical activity, heart rate, and other relevant health metrics. The IoT system enables continuous monitoring of students' physical activity, allowing instructors to track their progress, level of engagement, and effort. the system monitors students' physical health parameters, such as heart rate, respiration rate, and sleep patterns, enabling early detection of health issues and supporting overall well-being. The ARPC model comprises the ranking of the features in the IoT data with parallel data processing. With ARPC the activities of the students are monitored and martial arts education in elementary schools is examined. Simulation analysis stated that the ARPC model achieves a higher classification accuracy of 99.73% compared with the conventional state-of-art techniques.

A Multi-Day Wearable Surface EMG E-Tattoo for Fatigue Monitoring.

Surface electromyography (sEMG) is a commonly used technique for the non-invasive measurement of muscle activity. However, the traditional electrodes used for sEMG often have limitations regarding their long-term wearability. This study explored the feasibility of a wearable platform using a tattoo-like epidermal electrode (e-tattoo) for multi-day sEMG monitoring. Our sEMG e-tattoo provided stable and reliable sEMG signals over three days of application comparable to conventional gel electrodes. In addition, the e-tattoo has great resistance to motion artifacts and, therefore, maintains a high signal-to-noise ratio (SNR) and signal-to-motion ratio (SMR) during dynamic activities such as cycling. This robust wearable platform opens up new avenues for developing future wearable sEMG devices and advancing dynamic muscle fatigue research.Clinical relevance- The proposed wearable sEMG system can provide continuous and non-invasive monitoring of muscle activity, providing insights for improving rehabilitation and EMG-based prosthesis development for patients.

Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas.

Introduction of Amplitude-Integrated Electroencephalography (aEEG) Monitoring in a Level 2 NICU: Improving the Quality of Care for Neurologically At-Risk Newborns.

Amplitude-integrated electroencephalography (aEEG) is a bedside tool for continuous monitoring of brain activity with the possibility of real-time interpretation. Amplitude-integrated electroencephalography is routinely used in Canadian tertiary NICUs; however, its use in Level 2 NICUs has been limited. A bedside aEEG program was introduced in a Level 2 NICU in order to help facilitate the timely transfer of neurologically compromised infants and keep mother-infant dyads together where reassurance of appropriate neurological status could be attained. A monitoring guideline and educational program were developed. The introduction of aEEG monitoring enhanced the care provided to neurologically at-risk newborns. This experience can be used as a framework for other Level 2 NICUs who may wish to embark upon a similar initiative.

Environmental pollution loads on surface water chemistry and potentially ecological risks of inland aquatic ecosystem in South-Eastern State, Nigeria

The study assessed water chemistry of Nworie inland aquatic ecosystem in South eastern, Nigeria from January, 2020 to December 2022. Water samples were taken from the upper, middle, and lower river at 200 m interval. Data collected were subjected to multivariate analyses. Results of physicochemical tracers indicated mean CaCO3, Ca2+&Chemical Oxygen Demand (COD) to be 172.8 mg/l, 103.1 mg/l, 16.50 mg/l respectively above the World Health Organisation (WHO) critical limits, while turbidity (NTU), dissolved oxygen (DO) mgl−1, chloride (Cl−) mg/l, Magnesium (Mg) mg/l, Sodium (Na) mg/l, sulphate (SO42+) mg/l, nitrate (NO3−) mg/l, potassium (K) mg/l and phosphate (PO34) were below the limits. Heavy metals (mg/l) showed mean values viz: Cd 2+(0.053), Cr2+(0.049), Fe2+(0.443), Ni2+(0.024), and Pb (0.787) which were all above some critical global limits, except for Cu2+ and Zn2+ and in decreasing abundance: Pb2+ ≥ Fe2+ ≥ Cu2+ ≥ Zn2+ ≥ Cd2+ ≥ Cr2+ ≥ Ni2. Physicochemical tracers and elements in water correlated in both positive and negative directions. Principal component analysis (PCA) showed significant loads of PC3; CaCO3, NO3, COD, DO, Ca, PO4, Cu, Fe, Zn, PC2; moderate loads of SO4, Cl, & Na originating from complex human activities. Contamination factor (Cf), recorded high for cadmium while pollution load & ecological risk recorded low in decreasing order: Cr2+ ≥ Zn2+ ≥ Ni2+ ≥ Cd2+ ≥ Pb2+ ≥ Cu2+ ≥ Fe2+ with contamination degree in order of: Lower River (LR) ≥ Upper River(UR) ≥ Middle River(MR) and low risk index. Continuous monitoring of human activities along the whole River is important to mitigate any aquatic ecosystem damage for water quality sustainability.

Continuous monitoring of patients in and after the acute admission ward to improve clinical pathways: study protocol for a randomized controlled trial (Optimal-AAW)

BackgroundBecause of high demand on hospital beds, hospitals seek to reduce patients’ length of stay (LOS) while preserving the quality of care. In addition to usual intermittent vital sign monitoring, continuous monitoring might help to assess the patient’s risk of deterioration, in order to improve the discharge process and reduce LOS. The primary aim of this monocenter randomized controlled trial is to assess the effect of continuous monitoring in an acute admission ward (AAW) on the percentage of patients who are discharged safely.MethodsA total of 800 patients admitted to the AAW, for whom it is equivocal whether they can be discharged directly after their AAW stay, will be randomized to either receive usual care without (control group) or with additional continuous monitoring of heart rate, respiratory rate, posture, and activity, using a wearable sensor (sensor group). Continuous monitoring data are provided to healthcare professionals and used in the discharge decision. The wearable sensor keeps collecting data for 14 days. After 14 days, all patients fill in a questionnaire to assess healthcare use after discharge and, if applicable, their experience with the wearable sensor. The primary outcome is the difference in the percentage of patients who are safely discharged home directly from the AAW between the control and sensor group. Secondary outcomes include hospital LOS, AAW LOS, intensive care unit (ICU) admissions, Rapid Response Team calls, and unplanned readmissions within 30 days. Furthermore, facilitators and barriers for implementing continuous monitoring in the AAW and at home will be investigated.DiscussionClinical effects of continuous monitoring have already been investigated in specific patient populations for multiple purposes, e.g., in reducing the number of ICU admissions. However, to our knowledge, this is the first Randomized Controlled Trial to investigate effects of continuous monitoring in a broad patient population in the AAW.Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT05181111. Registered on 6 January 2022. Start of recruitment: 7 December 2021.

0748 Using daily wearable cardio fitness and sleep data to predict obesity in early adolescence

Abstract Introduction Obesity, and its co-morbidities, is a serious health risk currently facing adolescents in the US, with increased risk for adult obesity and cardiovascular health problems. Sleep is crucial for energy restorative processes and insufficient sleep influences food intake and appetite regulation. Adolescents are at risk for poor/insufficient sleep, which could, therefore, contribute to obesity risk. Given multifactorial risk factors, there is a need to identify the most salient predictors of adolescent obesity. Predicting adolescent obesity using objective Fitbit data has not previously been investigated and is the aim of the current study. Methods We used data collected with the Fitbit Charge 2 device from 2491 participants (Year 2, Mean-age=11.95 years, SD=0.65, 49.5% female) in the Adolescent Brain Cognitive Development (ABCD) Study®. We trained machine learning models (Gradient Boosted Trees and Regularized Logistic Regression) to identify the most important measures that predict obesity (>95 BMI percentile). In addition to sleep (timing, efficiency, duration, and regularity across the week) and cardio fitness (step counts, resting heart rate) Fitbit measures, averaged across seven days, we included socio-demographic characteristics. We ran a grid search embedded in a 2x5 fold cross-validation and we evaluated the performance based on the area under the curve-AUC and accuracy-ACC metrics. Results 15% percent of the sample were classified as having obesity. Both classification models performed acceptably high, with the LogReg having better performance (AUC-LogReg=0.76, ACC=85%) than the GBT model (AUC-GBT=0.75, ACC=0.69). Shorter sleep duration, more day-to-day variability in sleep measures, low number of steps with low variability over the week, and higher resting heart rate, with high day-to-day variability, were important predictors. Sociodemographic factors (Hispanic/Latinx ethnicity, African American race, low access to food) were also important predictors. Conclusion Results show that there are socio-economic disparities in obesity risk and that shorter sleep duration and more variable sleep patterns, as detected with a wearable, are important predictors of obesity risk. Results highlight the clinical utility of wearable measures and the importance of continuous monitoring of sleep, physical activity, and cardio fitness in adolescents. Support (if any) NIH U01DA041022

Non-invasive detection of narcolepsy type I phenotypical features and disease progression by continuous home-cage monitoring of activity in two mouse models: the HCRT-KO and DTA model.

Narcolepsy type 1 (NT1) is a neurological disorder caused by disruption of hypocretin (HCRT; or orexin) neurotransmission leading to fragmented sleep/wake states, excessive daytime sleepiness, and cataplexy (abrupt muscle atonia during wakefulness). Electroencephalography and electromyography (EEG/EMG) monitoring is the gold standard to assess NT1 phenotypical features in both humans and mice. Here, we evaluated the digital ventilated home-cage (DVC®) activity system as an alternative to detect NT1 features in two NT1 mouse models: the genetic HCRT-knockout (-KO) model, and the inducible HCRT neuron-ablation hcrt-tTA;TetO-DTA (DTA) model, including both sexes. NT1 mice exhibited an altered dark phase activity profile and increased state transitions, compared to the wild-type (WT) phenotype. An inability to sustain activity periods >40 min represented a robust activity-based NT1 biomarker. These features were observable within the first weeks of HCRT neuron degeneration in DTA mice. We also created a nest-identification algorithm to differentiate between inactivity and activity, inside and outside the nest as a sleep and wake proxy, respectively, showing significant correlations with EEG/EMG-assessed sleep/wake behavior. Lastly, we tested the sensitivity of the activity system to detect behavioral changes in response to interventions such as repeated saline injection and chocolate. Surprisingly, daily consecutive saline injections significantly reduced activity and increased nest time of HCRT-WT mice. Chocolate increased total activity in all mice, and increased the frequency of short out-of-nest inactivity episodes in HCRT-KO mice. We conclude that the DVC® system provides a useful tool for non-invasive monitoring of NT1 phenotypical features, and has the potential to monitor drug effects in NT1 mice.

Real-time monitoring of honeybee colony daily activity and bee loss rates can highlight the risk posed by a pesticide

Information on honeybee foraging performance and especially bee loss rates at the colony level are crucial for evaluating the magnitude of effects due to pesticide exposure, thereby ensuring that protection goals for honeybee colonies are met (i.e. threshold of acceptable effects). However, current methods for monitoring honeybee foraging activity and mortality are very approximate (visual records) or are time-limited and mostly based on single cohort analysis. We therefore assess the potential of bee counters, that enable a colony-level and continuous monitoring of bee flight activity and mortality, in pesticide risk assessment.After assessing the background activity and bee loss rates, we exposed colonies to two concentrations of sulfoxaflor (a neurotoxic insecticide) in sugar syrup: a concentration that was considered to be field realistic (0.59 μg/ml) and a higher concentration (2.36 μg/ml) representing a worst-case exposure scenario. We did not find any effect of the field-realistic concentration on flight activity and bee loss rates. However, a two-fold decrease in daily flight activity and a 10-fold increase in daily bee losses were detected in colonies exposed to the highest sulfoxaflor concentration as compared to before exposure. When compared to the theoretical trigger values associated with the specific protection goal of 7 % colony-size reduction, the observed fold changes in daily bee losses were often found to be at risk for colonies. In conclusion, the real-time and colony-level monitoring of bee loss rates, combined with threshold values indicating at which levels bee loss rates threaten the colony, have great potential for improving regulatory pesticide risk assessments for honeybees under field conditions.

149 Case Study: The Impact of a Fogging System on Dairy Cow Comfort in Cows Housed in a Barn with Tunnel Ventilation and an Automatic Milking System

Abstract Fogging systems control microclimatic parameters of barns to provide immediate cooling to dairy cows during heat stress conditions. However, the benefits of implementing a fogging system to cow comfort behaviors have not been thoroughly investigated in lactating cows. Therefore, the objective of this case study was to evaluate cow comfort behaviors and milk productivity in cows managed in a tunnel-ventilated barn in a commercial dairy farm that uses an automatic milk system. Sixty Holstein lactating dairy cows (45-90 days in milk; DIM) were monitored and housed in group pens (6 pens; n = 10 cows/pen). Four weeks before the fogging system installation, cows had pedometers (IceQube, IceRobotics, Inc., United Kingdom) and SCR rumination collars (SCR by Allflex; Israel) placed for continuous daily activity and rumination levels monitoring, respectively. Productivity information was acquired using automatic robotic milking technology (Lely Astronaut A5; Iowa, United States), and all variables were measured until four weeks after fogger system installation. Activity behavior (lying duration, daily step count, and number of transitions) and productivity (daily milk yield and milk speed) were evaluated before (PRE; 20-day pre-installation period), during (DURING; 2-day period), and after fogger installation (POST; 35-day post-installation period). Data were analyzed using PROC GLM in SAS 9.4 with cow as the experimental unit and temperature-humidity index (THI) included in the model as a covariate. Overall, cows spent more time lying (P < 0.01) in the POST period versus PRE and DURING. Cows performed fewer daily transitions between lying and standing (P < 0.01) in the POST versus PRE period, but no differences were detected (P = 0.67) among the step counts observed PRE, DURING, and POST fogger installation. Average milk speed and average milk yield were decreased (P < 0.01) in the POST period compared with the PRE and DURING periods. Temperature-humidity index significantly influenced (P < 0.01) lying time but did not have an effect on any other variables measured. In summary, installation of a fogger system in a commercial tunnel-ventilated barn improved cow comfort behaviors in lactating cows managed with an automated robotic milking system but did not offer immediate production performance benefits.

Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation.

Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated. The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings. Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set. The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE. In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models.

Testing three seismic hazard models for Italy via multi-site observations.

Probabilistic seismic hazard analysis (PSHA) is widely employed worldwide as the rational way to quantify the uncertainty associated to earthquake occurrence and effects. When PSHA is carried out for a whole country, its results are typically expressed in the form of maps of ground motion intensities that all have the same exceedance return period. Classical PSHA relies on data that continuously increase due to instrumental seismic monitoring, and on models that continuously evolve with the knowledge on each of its many aspects. Therefore, it can happen that different, equally legitimate, hazard maps for the same region can show apparently irreconcilable differences, sparking public debate. This situation is currently ongoing in Italy, where the process of governmental enforcement of a new hazard map is delayed. The discussion is complicated by the fact that the events of interest to hazard assessment are intentionally rare at any of the sites the maps refer to, thus impeding empirical validation at any specific site. The presented study, pursuing a regional approach instead, overcoming the issues of site specific PSHA validation, evaluated three different authoritative PSHA studies for Italy. Formal tests were performed directly testing the output of PSHA, that is probabilistic predictions, against the observed ground shaking exceedance frequencies, obtained from about fifty years of continuous monitoring of seismic activities across the country. The bulk of analyses reveals that, apparently alternative hazard maps are, in fact, hardly distinguishable in the light of observations.

Abstract 1426: Sox-based sensor phosphopeptides for continuous, homogeneous and quantitative monitoring of protein phosphatase activity

Abstract Introduction: The aberrant activation of oncogenic signaling pathways can drive tumorigenesis and malignant transformation, where deregulation of the activity of protein kinases or phosphatases can play a critical role. Several protein tyrosine phosphatases (PTPs) that are overexpressed in human cancers have recently been shown to activate signaling pathways and promote tumor development and progression (Sivaganesh et al., 2021. Protein Tyrosine Phosphatases: Mechanisms in Cancer. Int J Mol Sci. 22(23):12865). This reinforces the need for protein phosphatase inhibitors for cancer therapy, which requires robust assays to monitor PTP activity. Experimental Procedures: We harnessed chelation-enhanced fluorescence by combining next generation sulfonamido-oxine (Sox) chromophore technology with high-throughput solid-phase peptide synthesis methods to identify optimized sequences based on known physiological substrates. PTP enzyme activity was monitored using the PhosphoSens® platform in kinetic mode (a progress curve in every well) using fluorescence intensity (Ex/Em 360/485 nm) or in endpoint mode with Europium and time-resolved fluorescence (Ex/Em 360/620 nm). Results: Using commercially available phosphatases, we demonstrated the ability to rapidly identify novel Sox-based phosphopeptide substrates for each phosphatase. Performance measures included initial reaction rates, Km's, signal/background, linear reaction kinetics, absence of initial lags, assay sensitivity and specificity. Final optimized assay conditions were established for 28 different phosphatases. Each phosphatase was titrated to determine the lowest optimal assay concentration, demonstrating linearity and detection of low nM enzyme. Potency assessments using a set of compounds revealed sub nM inhibitor IC50 values and this included using sodium orthovanadate as a positive control. Finally, we demonstrated the ability to profile a range of novel phosphatase inhibitors to determine selectivity. Conclusions: The generation of robust activity-based Sox-sensor phosphopeptide substrates opens new areas for effective drug discovery with protein phosphatases as an emerging target class. The PhosphoSens-Kinetic assay format is ideal for assessing enzyme regulation and elucidating drug mechanism of action, potency, and profiling. The PhosphoSens-Red endpoint format is ideal for HTS and high-throughput SAR. Together, these formats can be applied across the entire target discovery and drug development workflow, providing a dramatic improvement in performance and productivity needed to address the challenges and opportunities of next generation protein kinase and phosphatase inhibitors. Citation Format: Susan Cornell-Kennon, Erik Schaefer, Earl May, Matthew Hakar. Sox-based sensor phosphopeptides for continuous, homogeneous and quantitative monitoring of protein phosphatase activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1426.

Construction worker productivity evaluation using action recognition for foreign labor training and education: A case study of Taiwan

Importing workers from countries with more abundant, lower-cost labor supplies has been a solution in developed countries to address the labor shortages. The continuous monitoring of worker activities is a widely used approach to measuring productivity and determining the root causes of inefficiency. The evaluation of column formwork activities was developed and tested to compare the relative productivity of foreign and domestic workers. An innovative framework integrating a deep learning method, namely You Only Watched Once (YOWO), was applied to recognize discrete actions of the workers. The YOWO model identified these actions with an average F1 score of 78.3%. The time required to perform the “assemble” action was identified as the most significantly different, with foreign workers requiring 36 s more than their domestic counterparts. Hence improving the efficacy of foreign workers in completing the assemble action should be prioritized in training and education.