Passive Monitoring Technology Research Articles

A service evaluation of passive remote monitoring technology for patients in a high-secure forensic psychiatric hospital: a qualitative study

BackgroundTechnology has the potential to remotely monitor patient safety in real-time that helps staff and without disturbing the patient. However, staff and patients’ perspectives on using passive remote monitoring within an inpatient setting is lacking. The study aim was to explore stakeholders’ perspectives about using Oxehealth passive monitoring technology within a high-secure forensic psychiatric hospital in the UK as part of a wider mixed-methods service evaluation.MethodsSemi-structured interviews were conducted with staff and patients with experience of using Oxehealth technology face-to-face within a private room in Broadmoor Hospital. We applied thematic analysis to the data of each participant group separately. Themes and sub-themes were integrated, finalised, and presented in a thematic map. Design, management, and analysis was meaningfully informed by both staff and patients.ResultsTwenty-four participants were interviewed (n = 12 staff, n = 12 patients). There were seven main themes: detecting deterioration and improving health and safety, “big brother syndrome”, privacy and dignity, knowledge and understanding, acceptance, barriers to use and practice issues and future changes needed. Oxehealth technology was considered acceptable to both staff and patients if the technology was used to detect deterioration and improve patient’s safety providing patient’s privacy was not invaded. However, overall acceptance was lower when knowledge and understanding of the technology and its camera was limited. Most patients could not understand why both physical checks through bedroom windows, and Oxehealth was needed to monitor patients, whilst staff felt Oxehealth should not replace physical checks of patients as reassures staff on patient safety.ConclusionsOxehealth technology is considered viable and acceptable by most staff and patients but there is still some concern about its possible intrusive nature. However, more support and education for new patients and staff to better understand how Oxehealth works in the short- and long-term could be introduced to further improve acceptability. A feasibility study or pilot trial to compare the impact of Oxehealth with and without physical checks may be needed.

VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China.

Passive acoustic monitoring technology is widely used to monitor the diversity of vocal animals, but the question of how to quickly extract effective sound patterns remains a challenge due to the difficulty of distinguishing biological sounds within multiple sound sources in a soundscape. In this study, we address the potential application of the VGGish model, pre-trained on Google's AudioSet dataset, for the extraction of acoustic features, together with an unsupervised clustering method based on the Gaussian mixture model, to identify various sound sources from a soundscape of a subtropical forest in China. The results show that different biotic and abiotic components can be distinguished from various confounding sound sources. Birds and insects were the two primary biophony sound sources, and their sounds displayed distinct temporal patterns across both diurnal and monthly time frames and distinct spatial patterns in the landscape. Using the clustering and modeling method of the general sound feature set, we quickly depicted the soundscape in a subtropical forest ecosystem, which could be used to track dynamic changes in the acoustic environment and provide help for biodiversity and ecological environment monitoring.

Combining passive acoustic data from a towed hydrophone array with visual line transect data to estimate abundance and availability bias of sperm whales (Physeter macrocephalus).

Visual line transect (VLT) surveys are central to the monitoring and study of marine mammals. However, for cryptic species such as deep diving cetaceans VLT surveys alone suffer from problems of low sample sizes and availability bias where animals below the surface are not available to be detected. The advent of passive acoustic monitoring (PAM) technology offers important opportunities to observe deep diving cetaceans but statistical challenges remain particularly when trying to integrate VLT and PAM data. Herein, we present a general framework to combine these data streams to estimate abundance when both surveys are conducted simultaneously. Secondarily, our approach can also be used to derive an estimate of availability bias. We outline three methods that vary in complexity and data requirements which are (1) a simple distance sampling (DS) method that treats the two datasets independently (DS-DS Method), (2) a fully integrated approach that applies a capture-mark recapture (CMR) analysis to the PAM data (CMR-DS Method) and (3) a hybrid approach that requires only a subset of the PAM CMR data (Hybrid Method). To evaluate their performance, we use simulations based on known diving and vocalizing behavior of sperm whales (Physeter macrocephalus). As a case study, we applied the Hybrid Method to data from a shipboard survey of sperm whales and compared estimates to a VLT only analysis. Simulation results demonstrated that the CMR-DS Method and Hybrid Method reduced bias by >90% for both abundance and availability bias in comparison to the simpler DS -DS Method. Overall, the CMR-DS Method was the least biased and most precise. For the case study, our application of the Hybrid Method to the sperm whale dataset produced estimates of abundance and availability bias that were comparable to estimates from the VLT only analysis but with considerably higher precision. Integrating multiple sources of data is an important goal with clear benefits. As a step towards that goal we have developed a novel framework. Results from this study are promising although challenges still remain. Future work may focus on applying this method to other deep-diving species and comparing the proposed method to other statistical approaches that aim to combine information from multiple data sources.

Key informant perceptions of challenges and facilitators to implementing passive remote monitoring technology for home care clients

Key informant perceptions of challenges and facilitators to implementing passive remote monitoring technology for home care clients

Study on feeding behavior and biological sound of Sebastes schlegelii

The construction of marine ranches can enrich and conserve the fishery resources and improve the marine ecosystem, which helps realize the sustainable utilization of these resources. Sebastes schlegelii is a major breeding and releasing fish species in the marine ranches of North China. Its behavioral characteristics can be understood better by researching its vocalization, which will provide data support for constructing acoustic taming marine ranches with S. schlegelii as the target fish species. However, there are few studies focusing on its sounds and behaviors. Therefore, based on the passive acoustic monitoring technology, the audios of underwater noises made by S. schlegelii were extracted using an AQH hydrophone. The high-definition internet protocol camera was used to monitor the behavior change of S. schlegelii. Then, by collating and replaying the collected audios and videos, the feeding behavior and biological noises of S. schlegelii were matched to analyze their relationship. Results are as follows: (1) The noise of chewing settling granular baits (Φ5.0 mm) has a main frequency band and sound pressure level of 2000~4500 Hz and 96.53 ± 0.65 dB, respectively; in this feeding process, the main frequency band and sound pressure level of the swimming noise are 25~400 Hz and 95.63 ± 0.38 dB, respectively; the values are 500~700 Hz and 97.34 ± 4.91 dB, respectively, for the noise of flapping the water with the tail. (2) The sound signals emitted by S. schlegelii are mostly presented as single pulses during normal habitation or ingestion of baits on the surface of the water tank. However, S. schlegelii will attack and fight against each other when scrambling for baits, during which these signals are presented as continuous pulses. To sum up, the vocalization of S. schlegelii is closely related to feeding activities, and the sounds produced under different behaviors have specific biological significance.

Syllable clustering analysis-based passive acoustic monitoring technology and its application in bird monitoring

Syllable clustering analysis-based passive acoustic monitoring technology and its application in bird monitoring

Stakeholder Perspectives on In-home Passive Remote Monitoring to Support Aging in Place in the Province of New Brunswick, Canada: Rapid Qualitative Investigation.

BackgroundThe province of New Brunswick (NB) has one of the oldest populations in Canada, providing an opportunity to develop and test innovative strategies to address the unique health challenges faced by older adults. Passive remote monitoring technology has the potential to support independent living among older adults. Limited research has examined the benefits of and barriers to the adoption of this technology among community-dwelling older adults.ObjectiveThis study aimed to explore perceptions of in-home passive remote monitoring technology designed to support aging in place from the perspective of older adults, their family or friend caregivers, social workers, and government decision-makers in the province of NB, Canada.MethodsBetween October 2018 and March 2020, a rapid qualitative investigation of 28 one-on-one interviews was conducted in person or via telephone. Participants included 2 home support services clients and 11 family or friend caregivers who had used passive remote monitoring technology in their homes; 8 social workers who had worked as case managers for home support services clients; and 7 individuals who were key government decision-makers in the adoption, policy development, and use of the technology in the province of NB. The interviews focused on the following topics: decision to adopt the passive remote monitoring system, barriers to adopting the passive remote monitoring system, benefits of the passive remote monitoring system, impact on client health outcomes, and privacy concerns. The interviews were audio recorded, transcribed, and analyzed by a team of 6 researchers. Data analysis was conducted using a rapid assessment process approach that included matrix analysis.ResultsParticipants reported that the use of the remote monitoring system allowed older adults to live at home longer and provided caregiver relief. Stakeholders were invested in meeting the home support (home care) needs of older adults. However, when it came to the use of remote monitoring, there was a lack of consensus about which clients it was well-suited for and the role that social workers should play in informing clients and caregivers about the service (role ambiguity, gatekeeping, and perceived conflicts of interest).ConclusionsOur findings highlight many benefits and challenges of the adoption of passive remote monitoring for clients, their family or friend caregivers, and public provincial health and social services systems. Passive remote monitoring is a valuable tool that can provide support to older adults and their family or friend caregivers when it is a good fit with client needs. Further work is needed in NB to increase public and social workers’ awareness of the service and its benefits.

The dynamical complexity of seasonal soundscapes is governed by fish chorusing

Passive acoustic monitoring technology can depict underwater soundscapes, yet phenological and seasonal patterns and shifts in soundscapes are still poorly understood. Here we analyse the seasonal soundscape in the Eastern Taiwan Strait in 2017 using nonlinear dynamical complexity modelling. We find an annual phenology of fish chorusing that begins in spring, and peaks in summer before beginning to subside in autumn and becoming silent in winter. During spring and summer, the soundscape exhibited significantly higher complexity and predictability than in autumn and winter, due to the presence of regular fish chorusing. Soundscape dynamics shifted from being nonlinear in spring and summer to being linear and stochastic in autumn and winter. Our findings suggest that soundscapes could be used to measure phenological patterns and seasonal shifts in marine species behaviour. We propose that monitoring soundscapes could help assess the long-term health of marine ecosystems under environmental and climatic change.

Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach.

BackgroundLanguage use and social interactions have demonstrated a close relationship with cognitive measures. It is important to improve the understanding of language use and behavioral indicators from social context to study the early prediction of cognitive decline among healthy populations of older adults.ObjectiveThis study aimed at predicting an important cognitive ability, working memory, of 98 healthy older adults participating in a 4-day-long naturalistic observation study. We used linguistic measures, part-of-speech (POS) tags, and social context information extracted from 7450 real-life audio recordings of their everyday conversations.MethodsThe methods in this study comprise (1) the generation of linguistic measures, representing idea density, vocabulary richness, and grammatical complexity, as well as POS tags with natural language processing (NLP) from the transcripts of real-life conversations and (2) the training of machine learning models to predict working memory using linguistic measures, POS tags, and social context information. We measured working memory using (1) the Keep Track test, (2) the Consonant Updating test, and (3) a composite score based on the Keep Track and Consonant Updating tests. We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting.ResultsFor all three prediction routines, models comprising linguistic measures, POS tags, and social context information improved the baseline performance on the validation folds. The best model for the Keep Track prediction routine comprised linguistic measures, POS tags, and social context variables. The best models for prediction of the Consonant Updating score and the composite working memory score comprised POS tags only.ConclusionsThe results suggest that machine learning and NLP may support the prediction of working memory using, in particular, linguistic measures and social context information extracted from the everyday conversations of healthy older adults. Our findings may support the design of an early warning system to be used in longitudinal studies that collects cognitive ability scores and records real-life conversations unobtrusively. This system may support the timely detection of early cognitive decline. In particular, the use of a privacy-sensitive passive monitoring technology would allow for the design of a program of interventions to enable strategies and treatments to decrease or avoid early cognitive decline.

Association Between Mild Cognitive Impairment and Seasonal Rest-Activity Patterns of Older Adults.

Seasonal variation in rest-activity patterns has been observed in healthy adult populations. This study examined seasonal variation in total time spent overnight in the bedroom by cognitively intact older adults and older adults with mild cognitive impairment (MCI). We hypothesize that seasonal variation in rest-activity patterns is observed in the cognitively intact group and that this variation is disturbed in those with MCI. Study participants were 128 older adults; mean age 85.2 years. Ninety-eight were cognitively intact, and 30 had been diagnosed with MCI. All were enrolled in an ongoing longitudinal study using in-home passive monitoring technology. Infrared presence sensors were placed throughout each participant's home to monitor movement and presence in each room of the home. Activity data was collected from the sensors over a period of up to 527 days. Overnight time in bedroom was found to vary seasonally for the cognitively intact group, with longer times spent overnight in the bedroom during the winter months. This seasonal variation was not observed for those with non-amnestic MCI. MCI is associated with an attenuation of seasonal variation in total time spent in the bedroom at night. Detection of changes in infradian sleep patterns may be an early marker of cognitive decline. Which key determinants are driving these disturbed rhythms, such as features intrinsic to changes in the brain or to environmental factors or external cues, remains an important question for ongoing and future studies.

How does passive remote monitoring technology affect perceived outcomes for older adults, their family and friend caregivers, and the healthcare system?

How does passive remote monitoring technology affect perceived outcomes for older adults, their family and friend caregivers, and the healthcare system?

Clearing a Path to Commercialization of Marine Renewable Energy Technologies Through Public–Private Collaboration

Governments are increasingly turning toward public–private partnerships to bring industry support to improving public assets or services. Here, we describe a unique public–private collaboration where a government entity has developed mechanisms to support public and private sector advancement and commercialization of monitoring technologies for marine renewable energy. These support mechanisms include access to a range of skilled personnel and test facilities that promote rapid innovation, prove reliability, and inspire creativity in technology development as innovations move from concept to practice. The ability to iteratively test hardware and software components, sensors, and systems can accelerate adoption of new methods and instrumentation designs. As a case study, we present the development of passive acoustic monitoring technologies customized for operation in energetic waves and currents. We discuss the value of testing different systems together, under the same conditions, as well as the progression through different test locations. The outcome is multiple, complementary monitoring technologies that are well suited to addressing an area of high environmental uncertainty and reducing barriers to responsible deployment of low-carbon energy conversion systems, creating solutions for the future.

Assessing ecological and environmental influences on boreal chorus frog (Pseudacris maculata) spring calling phenology using multimodal passive monitoring technologies

Although anuran reproductive behavior follows a phenological pattern, environmental factors may influence the timing of anuran activity. To characterize the spring calling phenology of Pseudacris maculata (boreal chorus frog) we installed acoustic recorders during three spring breeding seasons (2015–2017) within wet meadow and forested slough habitats in central Nebraska near the Platte River. The recorders were paired with time-lapse camera systems that were used to measure vegetation change and wet meadow inundation. We examined the relationship between environmental variables, including temperature, precipitation, and hydropattern, and P. maculata calling activity using random forest models. Calling activity exhibited diel and seasonal variation with differences observed between habitats. Hourly call activity was highest between 20:00 and 01:00, with diurnal and sometimes near-continuous calling observed at the wet meadow but not at the forested slough. Random forest models explained 73% of calling activity variance at the wet meadow and 45% at the forested slough, suggesting additional factors, such as predation, may be influential at the forested slough, as it exhibited near permanent hydrology. P. maculata was predicted to increase chorusing at the 70th day of the year at the wet meadow but not until the 100th at the forested slough. When average air temperatures exceeded 5 °C, calling activity was predicted to increase at both wetlands, and calling declined below this temperature. Hydropattern and weekly precipitation accumulation were important predictors of P. maculata calling activity. Our findings highlight the importance of climatic factors such as temperature and precipitation regimes, in addition to habitat conditions such as water-availability, on P. maculata calling activity. Furthermore, this study demonstrates the utility of multimodal passive monitoring technologies, including both visual and auditory tools, for understanding the environmental factors that influence biological activity. Multimodal approaches can be especially useful for documenting and measuring vocal-taxa diversity, activity, and drivers of change.

Application of passive acoustic monitoring technology in the monitoring of western black crested gibbons

Application of passive acoustic monitoring technology in the monitoring of western black crested gibbons

Incorporating local habitat heterogeneity and productivity measures when modelling vertebrate richness

SummaryDeclining species richness is a global concern; however, the coarse-scale metrics used at regional or landscape levels might not accurately represent the important habitat characteristics needed to estimate species richness. Currently, there exists a lack of knowledge with regard to the spatial extent necessary to correlate remotely sensed habitat metrics to species richness and animal surveys. We provide a protocol for determining the best scale to use when merging remotely sensed habitat and animal survey data as a step towards improving estimates of vertebrate species richness on broad scales. We test the relative importance of fine-resolution habitat heterogeneity and productivity metrics at multiple spatial scales as predictors of species richness for birds, frogs and mammals using a Bayesian approach and a combination of passive monitoring technologies. Model performance was different for each taxonomic group and dependent on the scale at which habitat heterogeneity and productivity were measured. Optimal scales included a 20-m radius for bats and frogs, an 80-m radius for birds and a 180-m radius for terrestrial mammals. Our results indicate that optimal scales do exist when merging remotely sensed habitat measures with ground-based surveys, but they differ between vertebrate groups. Additionally, the selection of a measurement scale is highly influential to our understanding of the relationships between species richness and habitat characteristics.

Predicting Inpatient Aggression in Forensic Services Using Remote Monitoring Technology: Qualitative Study of Staff Perspectives.

BackgroundMonitoring risk of imminent aggression in inpatient forensic mental health services could be supported by passive remote monitoring technology, but staff attitudes toward the relevance and likelihood of engagement with this technology are unknown.ObjectiveThis study aimed to explore staff views, specifically potential benefits and implementation barriers, on using this technology for monitoring risk of inpatient aggression.MethodsWe conducted semistructured focus groups with nurses in an inpatient forensic mental health service. We used thematic analysis with two independent raters to identify themes and subthemes related to staff attitudes toward passive remote monitoring. We subsequently checked with members to ensure the validity of the themes identified by the raters.ResultsFrom January to March 2019, a total of 25 nurses took part in five focus groups. We identified five main themes, one of which concerned the potential benefits that passive remote monitoring could provide for monitoring risk of aggression. Staff suggested it could provide an early warning of impending aggression and enable support to be provided earlier. The remaining themes concerned implementation barriers, including risks to the users’ physical and mental well-being; data security concerns and potential access by third parties; the negative impact of a constant stream of real-time data on staff workload; and design characteristics and user awareness of the benefits of passive remote monitoring.ConclusionsPassive remote monitoring technology could support existing methods of monitoring inpatient aggression risk, but multiple barriers to implementation exist. Empirical research is required to investigate whether these potential benefits can be realized, and to identify ways of addressing these barriers to ensure acceptability and user engagement.

Economic evaluation of passive monitoring technology for seniors

BackgroundAdvances such as passive monitoring technology (PMT), which provides holistic supervision of chronically ill and elderly patients, enable and support improved monitoring and observation, thus empowering the growing population of older adults to live more independently while lowering health care expenses.AimsThis study develops a conceptual model to estimate the potential savings associated with PMT.MethodsWe first develop a conceptual model to identify the main cost variables associated with independent living, focusing on three pathways: (1) PMT, (2) independent living supported by the current standard of care, and (3) facility-based care. We examined the impact on three outcomes [i.e., health care costs, institutional costs, and health-related quality of life (HRQoL)] along each of the three care pathways (i.e., PMT, independent living supported by the standard of care, and facility-based care) and developed a cost-benefit model to calculate the net costs and benefits associated with each care pathway.ResultsThe cost–benefit model showed savings between approximately $425 per-member per-month (PMPM) for those using PMT compared to those on the standard of care pathway. Sensitivity analysis demonstrated that a 5% increase in nursing home utilization generates cost savings of more than 30% PMPM.DiscussionThe total projected cost savings for individuals on the PMT arm are projected to be more than $425 PMPM, with annual savings of $5069 per-person per-year, and over $5.1 million for a target population of 1000 individuals.ConclusionsThe cost calculations in our cost–benefit simulation model clearly demonstrate the value of PMT and show the potential value to payers and integrated delivery systems in offering PMT to individuals who are likely to benefit the most from the services.

Medicaid Becomes the First Third-Party Payer to Cover Passive Remote Monitoring for Home Care: Policy Analysis.

BackgroundRecent years have seen an influx of location-tracking, activity-monitoring sensors, and Web-cameras to remotely monitor the safety of older adults in their homes and to reduce reliance on in-person assistance. The state of research on these monitoring technologies leaves open crucial financial, social, and ethical cost-benefit questions, which have prevented widespread use. Medicaid is now the first large third-party payer in the United States to pay for these technologies, and their use is likely to increase as states transition to managed long-term services and supports (MLTSS).ObjectivesThis is the first study to examine how state Medicaid programs are treating passive remote monitoring technologies. This study identifies (1) which states allow location tracking, sensor systems, and cameras; (2) what policies are in place to track their use; (3) what implementation processes and program monitoring mechanisms are in place; and (4) what related insights Medicaid program stakeholders would like to learn from researchers.MethodsInterviews were conducted with 43 state, federal, and managed care organization (MCO) Medicaid program stakeholders about how these technologies are used in state waivers serving community-dwelling older adults in 15 states, and what policies are in place to regulate them. The interviews were analyzed by the research team using the framework analysis method for applied policy research.ResultsTwo-thirds of the states cover location tracking and activity-monitoring sensors and one-third cover cameras, but only 3 states have specific service categories that allow them to track when they are paying for any of these technologies, impeding regulation and understanding of their use at the state and federal level. Consideration of ethical and social risks is limited, and states struggle to understand which circumstances warrant use. They are further challenged by extreme resource restrictions and transitions to MLTSS by MCOs inexperienced in serving this growing “high-need, high-cost” population.ConclusionsDecisions about Medicaid reimbursement of technologies that have the potential to dramatically alter the way older adults receive supportive services are being made without research on their use, social and ethical implications, or outcomes. At a minimum, new service categories are needed to enable oversight. Participants prioritized 3 research aims to inform practice: (1) determine cost-effectiveness; (2) identify what type of information beneficiaries want to be generated and whom they want it to be shared with; and (3) understand how to support ethical decision making for beneficiaries with cognitive impairment. These findings provide direction for future research and reveal that greater interaction between policy makers and researchers in this field is needed.

"You sound ill, take the day off": Automatic recognition of speech affected by upper respiratory tract infection.

A combination of passive, non-invasive and nonintrusive smart monitoring technologies is currently transforming healthcare. These technologies will soon be able to provide immediate health related feedback for a range of illnesses and conditions. Such tools would be game changing for serious public health concerns, such as seasonal cold and flu, for which early diagnosis and social isolation play a key role in reducing the spread. In this regard, this paper explores, for the first times, the automated classification of individuals with Upper Respiratory Tract Infections (URTI) using recorded speech samples. Key results presented indicate that our classifiers can achieve similar results to those seen in related health-based detection tasks indicating the promise of using computational paralinguistic analysis for the detection of URTI related illnesses.

DISRUPTED INFRADIAN RHYTHMS IN MILD COGNITIVE IMPAIRMENT

Sleep quality is closely linked with cognitive function. Monitoring sleep patterns may offer insight into the early prognosis of aging adults at risk for dementia. Disrupted sleep has been documented in studies of adults with mild cognitive impairment. Sleep patterns are strongly influenced by circadian rhythms on a daily scale and also vary seasonally depending on day length and environmental temperature, with longer sleep periods observed during the winter months. In a uniquely longitudinal cohort study using in-home passive monitoring, we evaluated whether normal seasonal (infradian) rhythms are disrupted in individuals with MCI. Participants were 128 older adults; mean age, 85 years. Ninety-eight were cognitively intact and 30 had been diagnosed with MCI (22 non-amnestic MCI; 8 amnestic MCI) using standard psychometric and clinical criteria. All were enrolled in an ongoing longitudinal study using in-home passive monitoring technology. Infrared sensors were placed throughout each participant's home to monitor movement and presence in each room of the home. Algorithms were developed to estimate rest-activity patterns during the night, which were then extrapolated to variables such as total time asleep, sleep latency, and time awake after sleep onset. Activity data was collected from the sensors for more than five hundred days. Sleep patterns were found to vary seasonally for the cognitively intact group with longer sleep periods during the winter months. This seasonal variation was not observed for those with non-amnestic mild cognitive impairment. Total time asleep was associated with the seasonal length of the night for the cognitively intact group and more weakly for the group with amnestic MCI. The cognitively intact group was found to sleep an average of 18 minutes longer in winter, while the MCI group slept 9 minutes less in winter.