Comparing video observation to electronic topography device as a method for measuring cigarette puffing behavior.

BACKGROUND: Smoking topography, or puffing behavior, is an important measure of how consumers may use tobacco products. However, numerous issues may prevent collection of this data via in-person, electronic topography device (e.g., CReSS). This study compared cigarette topography measures collected by video observation and electronic device. METHODS: Laboratory smoking sessions were video recorded and scored for 96 cigarettes collected from 34 daily, adult non-treatment-seeking smokers (73.5 % male, 82.4 % White). Participants smoked three of their preferred brand cigarettes using an electronic topography device, providing carbon monoxide (CO) samples before and after each cigarette. Analyses compared measures from both assessment methods and examined associations with device-obtained total puff volume and CO boost. RESULTS: Agreement analyses indicated robust similarity between methods for measures of puff count and total interpuff interval (Intraclass Correlation Coefficient [ICC]'s > 0.96,p's < 0.001; Bland-Altman [B-A] plotted differences within a priori limit of clinical significance) but diverged on total duration (ICC's > .93, p's < .001, yet B-A plots outside a priori limits). Regardless of assessment method, total duration and puff count (but not total interpuff interval) predicted total puff volume (p's < .001). None predicted CO boost (p's = .07-.90)." CONCLUSIONS: Although some topography outcomes (e.g., total puff volume) cannot be assessed via video observation, video-observed measures of puff count, total duration, and total interpuff interval are generally interchangeable with their topography device-obtained counterparts. Thus, video observation is likely a sufficient substitute method for assessing cigarette topography when using an electronic device is not possible.

Organ-specific, multimodal, wireless optoelectronics for high-throughput phenotyping of peripheral neural pathways.

The vagus nerve supports diverse autonomic functions and behaviors important for health and survival. To understand how specific components of the vagus contribute to behaviors and long-term physiological effects, it is critical to modulate their activity with anatomical specificity in awake, freely behaving conditions using reliable methods. Here, we introduce an organ-specific scalable, multimodal, wireless optoelectronic device for precise and chronic optogenetic manipulations in vivo. When combined with an advanced, coil-antenna system and a multiplexing strategy for powering 8 individual homecages using a single RF transmitter, the proposed wireless telemetry enables low cost, high-throughput, and precise functional mapping of peripheral neural circuits, including long-term behavioral and physiological measurements. Deployment of these technologies reveals an unexpected role for stomach, non-stretch vagal sensory fibers in suppressing appetite and demonstrates the durability of the miniature wireless device inside harsh gastric conditions.

Validation of a life-logging wearable camera method and the 24-h diet recall method for assessing maternal and child dietary diversity.

Accurate and timely data are essential for identifying populations at risk for undernutrition due to poor-quality diets, for implementing appropriate interventions and for evaluating change. Life-logging wearable cameras (LLWC) have been used to prospectively capture food/beverage consumed by adults in high-income countries. This study aimed to evaluate the concurrent criterion validity, for assessing maternal and child dietary diversity scores (DDS), of a LLWC-based image-assisted recall (IAR) and 24-h recall (24HR). Direct observation was the criterion method. Food/beverage consumption of rural Eastern Ugandan mothers and their 12-23-month-old child (n 211) was assessed, for the same day for each method, and the IAR and 24HR DDS were compared with the weighed food record DDS using the Bland-Altman limits of agreement (LOA) method of analysis and Cohen's κ. The relative bias was low for the 24HR (-0·1801 for mothers; -0·1358 for children) and the IAR (0·1227 for mothers; 0·1104 for children), but the LOA were wide (-1·6615 to 1·3012 and -1·6883 to 1·4167 for mothers and children via 24HR, respectively; -2·1322 to 1·8868 and -1·7130 to 1·4921 for mothers and children via IAR, respectively). Cohen's κ, for DDS via 24HR and IAR, was 0·68 and 0·59, respectively, for mothers, and 0·60 and 0·59, respectively, for children. Both the 24HR and IAR provide an accurate estimate of median dietary diversity, for mothers and their young child, but non-differential measurement error would attenuate associations between DDS and outcomes, thereby under-estimating the true associations between DDS - where estimated via 24HR or IAR - and outcomes measured.

Multiple observations of Bigfin Squid (Magnapinna sp.) in the Great Australian Bight reveal distribution patterns, morphological characteristics, and rarely seen behaviour.

One of the most remarkable groups of deep-sea squids is the Magnapinnidae, known for their large fins and strikingly long arm and tentacle filaments. Little is known of their biology and ecology as most specimens are damaged and juvenile, and in-situ sightings are sparse, numbering around a dozen globally. As part of a recent large-scale research programme in the Great Australian Bight, Remotely Operated Vehicles and a towed camera system were deployed in depths of 946-3258 m resulting in five Magnapinna sp. sightings. These represent the first records of Bigfin Squid in Australian waters, and more than double the known records from the southern hemisphere, bolstering a hypothesis of cosmopolitan distribution. As most previous observations have been of single Magnapinna squid these multiple sightings have been quite revealing, being found in close spatial and temporal proximity of each other. Morphological differences indicate each sighting is of an individual rather than multiple sightings of the same squid. In terms of morphology, previous in-situ measurements have been roughly based on nearby objects of known size, but this study used paired lasers visible on the body of a Magnapinna squid, providing a more accurate scaling of size. Squid of a juvenile size were also recorded and are confirmed to possess the long distal filaments which have thus far been mostly missing from specimens due to damage. We have described fine-scale habitat, in-situ colouration, and behavioural components including a horizontal example of the 'elbow' pose, and coiling of distal filaments: a behaviour not previously seen in squid. These sightings add to our knowledge of this elusive and intriguing genus, and reinforce the value of imagery as a tool in deep-sea squid research.

Assessment of nest building and social interaction behavior in mice exposed to acute social defeat stress using a three-dimensional depth camera.

Nest building is an instinctive behavior toward protection from predators, body temperature regulation, and courtship. Previously, we discovered that acute and chronic social defeat stress suppresses the onset of nest-building behavior in male mice (C57BL/6J). Here, we analyzed nest building and other behavioral deficits induced by acute social defeat stress (ASDS). We utilized a customized cage and specifically developed observational programs for nest building, social avoidance, and other behaviors using an infrared depth camera to acquire three-dimensional (3D) data of animal behavior (Negura system). We determined the volume of nesting materials from these 3D depth images. Mice exposed to ASDS showed increased spontaneous activities, decreased rearing, and delayed nest building; however, nest-building activity was gradually recovered during the dark period of the 24 hr observation interval. At the endpoint following 24 hr, the ASDS and control groups showed no differences in nest volumes. Furthermore, we observed the time courses of both nest building and social avoidance behaviors and their relationship using the Negura system. Our data demonstrated a weak positive correlation between nest-building delay and social avoidance in ASDS mice. The Negura system can observe various behaviors that reflect the effects of social defeat stress.

Behavioral Observation of Infants With Life-Threatening or Life-Limiting Illness in the Neonatal Intensive Care Unit.

BACKGROUND: Infants in the neonatal intensive care unit experience aversive stimuli that cause pain and distress. Maintaining adequate relief from pain and distress is challenging because of infants' varying ages and stages of development and their nonverbal status. Thus, pain and distress must be interpreted by a healthcare provider or other proxy from their own observations or perceptions. There is no standard research or clinical measure for pain and distress in infants. OBJECTIVE: The purpose of this analysis was to evaluate the use of the COMFORT Behavior (COMFORT-B) Scale as a measure of pain and distress in infants diagnosed with life-threatening or life-limiting illnesses in the neonatal intensive care unit in comparison with the nurse-documented Neonatal Pain, Agitation and Sedation Scale; the infants' Technology Dependence Scale; and the mothers' report of total perceived symptom scores. METHODS: Infants diagnosed with life-threatening or life-limiting illnesses and hospitalized in a Level IV neonatal intensive care unit in the Midwestern United States and their parents were included. Measurement of pain and distress in infants was collected weekly from enrollment through 12 weeks or until discharge, whichever occurred first. Observations for the COMFORT-B Scale were conducted before and after standard caregiving activities. Pearson r correlations were used to compare means between pain and distress, technology dependence, and mothers' total perceived symptom scores over time. RESULTS: Data from 78 infants (46 male and 32 female infants) of ages 23-41 weeks of gestation at birth were analyzed. No correlations were found among the COMFORT-B Scale; the Neonatal Pain, Agitation and Sedation Scale; and mothers' total perceived symptom scores. Moderate correlations were found among the Technology Dependence Scale; mothers' total perceived symptom scores; and the Neonatal Pain, Agitation and Sedation Scale. DISCUSSION: Performing COMFORT-B observations can be challenging, and it is unclear whether the information obtained from the COMFORT-B Scale added to the assessment of the infant's pain and distress that is typically recorded in the health record or from parents. Further evaluation is needed to determine if it is more reliable to collect the Neonatal Pain, Agitation and Sedation Scale scores rather than conduct observations using the COMFORT-B Scale in studies of infants with life-threatening and life-limiting illnesses in the neonatal intensive care unit.

Automated detection of the head-twitch response using wavelet scalograms and a deep convolutional neural network.

Hallucinogens induce the head-twitch response (HTR), a rapid reciprocal head movement, in mice. Although head twitches are usually identified by direct observation, they can also be assessed using a head-mounted magnet and a magnetometer. Procedures have been developed to automate the analysis of magnetometer recordings by detecting events that match the frequency, duration, and amplitude of the HTR. However, there is considerable variability in the features of head twitches, and behaviors such as jumping have similar characteristics, reducing the reliability of these methods. We have developed an automated method that can detect head twitches unambiguously, without relying on features in the amplitude-time domain. To detect the behavior, events are transformed into a visual representation in the time-frequency domain (a scalogram), deep features are extracted using the pretrained convolutional neural network (CNN) ResNet-50, and then the images are classified using a Support Vector Machine (SVM) algorithm. These procedures were used to analyze recordings from 237 mice containing 11,312 HTR. After transformation to scalograms, the multistage CNN-SVM approach detected 11,244 (99.4%) of the HTR. The procedures were insensitive to other behaviors, including jumping and seizures. Deep learning based on scalograms can be used to automate HTR detection with robust sensitivity and reliability.

Comparison of the Zurich Observation Pain Assessment with the Behavioural Pain Scale and the Critical Care Pain Observation Tool in nonverbal patients in the intensive care unit: A prospective observational study.

OBJECTIVES: To determine the concordance of Zurich Observation Pain Assessment (ZOPA) with the behavioural Pain Scale (BPS) and the Critical Care Pain Observation Tool (CPOT) to detect pain in nonverbal ICU patients. DESIGN: Prospective observational study [BASEC-Nr. PB_2016-02324]. SETTING: A total of 49 ICU patients from cardiovascular, visceral and thoracic surgery and neurology and neurosurgery were recruited. Data from 24 patients were analyzed. MAIN OUTCOME MEASUREMENTS: Three independent observers assessed pain with the BPS, the CPOT or ZOPA prior, during and after a potential painful nursing intervention. Tools were randomized concerning the pain management after each pain assessment. Frequency of nine additional pain indicating items from a previous qualitative, explorative study was calculated. RESULTS: ZOPA was positive in 32 of 33 measuring cycles (97.0%; 95%CI: 84.2-99.9%), followed by the CPOT (28/33 cycles, 84.8%; 95%CI: 68.1-94.9%) and the BPS (23/33 cycles, 67.0%; 95%CI: 51.3-84.4%). In 22/33 cycles all tools were concordant (66.7%; 95%CI: 48.2-82.0%). Analgesics were provided in 29 out of 33 cycles (87.9%; 95%CI: 71.8-96.6%). Additional pain indicating items were inconsistently reported. CONCLUSION: ZOPA is concordant with the BPS and the CPOT to indicate pain but detects pain earlier due to the low threshold value. Inclusion of further items does not improve pain assessment.

Close-range vocal interaction in the common marmoset (Callithrix jacchus).

Vocal communication in animals often involves taking turns vocalizing. In humans, turn-taking is a fundamental rule in conversation. Among non-human primates, the common marmoset is known to engage in antiphonal calling using phee calls and trill calls. Calls of the trill type are the most common, yet difficult to study, because they are not very loud and uttered in conditions when animals are in close proximity to one another. Here we recorded trill calls in captive pair-housed marmosets using wearable microphones, while the animals were together with their partner or separated, but within trill call range. Trills were exchanged mainly with the partner and not with other animals in the room. Animals placed outside the home cage increased their trill call rate and uttered more trills in response to their partner compared to strangers. The fundamental frequency, F0, of trills increased when animals were placed outside the cage. Our results indicate that trill calls can be monitored using wearable audio equipment and that minor changes in social context affect trill call interactions and spectral properties of trill calls.

IntelliCage as a tool for measuring mouse behavior - 20 years perspective.

Since the 1980s, we have witnessed the rapid development of genetically modified mouse models of human diseases. A large number of transgenic and knockout mice have been utilized in basic and applied research, including models of neurodegenerative and neuropsychiatric disorders. To assess the biological function of mutated genes, modern techniques are critical to detect changes in behavioral phenotypes. We review the IntelliCage, a high-throughput system that is used for behavioral screening and detailed analyses of complex behaviors in mice. The IntelliCage was introduced almost two decades ago and has been used in over 150 studies to assess both spontaneous and cognitive behaviors. We present a critical analysis of experimental data that have been generated using this device.

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology.

Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments.

Communication behavior of cognitively impaired older inpatients : A new setting for validating the CODEM instrument.

BACKGROUND AND OBJECTIVE: Acutely ill older patients with cognitive impairment represent a major subgroup in acute care hospitals. In this context, communication plays a crucial role for patients' well-being, healthcare decisions, and medical outcomes. As validated measures are lacking, we tested the psychometric properties of an observational instrument to assess Communication Behavior in Dementia (CODEM) in the acute care hospital setting. As a novel feature, we were also able to incorporate linguistic and social-contextual measures. MATERIAL AND METHODS: Data were drawn from a cross-sectional mixed methods study that focused on the occurrence of elderspeak during care interactions in two German acute care hospitals. A total of 43 acutely ill older patients with severe cognitive impairment (CI group, Mage ± SD = 83.6 ± 5.7 years) and 50 without cognitive impairment (CU group, Mage ± SD = 82.1 ± 6.3 years) were observed by trained research assistants during a standardized interview situation and rated afterwards by use of CODEM. RESULTS: Factor analysis supported the expected two-factor solution for the CI group, i.e., a verbal content and a nonverbal relationship aspect. Findings of the current study indicated sound psychometric properties of the CODEM instrument including internal consistency, convergent, divergent, and criterion validity. CONCLUSION: CODEM represents a reliable and valid tool to examine the communication behavior of older patients with CI in the acute care hospital setting. Thus, CODEM might serve as an important instrument for researcher and healthcare professionals to describe and improve communication patterns in this environment.

Operationally Relevant Behavior Assessment Using the Robotic On-Board Trainer for Research (ROBoT-r).

INTRODUCTION: Spaceflight can strain astronaut physical, physiological, and mental well-being, whereas maintaining astronaut operational performance remains an essential goal. Although various cognitive tests have been used for spaceflight assessment, these have been challenged on their lack of operational relevance.METHODS: To address this gap, we developed and characterized the Robotic On-Board Trainer for Research (ROBoT-r) system, based on the Robotic On-Board Trainer (ROBoT) currently used for astronaut training on Canadarm2 track-and-capture activities. The task requires use of dual hand-controllers (6 degrees of freedom) to grapple an incoming vehicle in free-drift in a time-limited setting. After developing a platform for conducting research studies, characterization testing of ROBoT-r was completed by 14 astronaut-like volunteers (35 ± 11 yr; N = 5 women) over 16 sessions each.RESULTS: We describe the design and capabilities of the ROBoT-r system for conducting operationally relevant research on human performance. Version 6.2 of the system supports H-II Transfer Vehicle track-and-capture operations within a multimillion component, physics-enabled 3D model using NASA's DOUG graphics platform. It has configurable task initialization and auto-run capabilities, saves 38 variables continuously at 20 Hz throughout each run, provides the user quantitative feedback after each run, and provides summaries after each session. Detailed performance characterization data is reported for future experimental planning purposes.DISCUSSION: ROBoT-r's range of performance variables enables detailed and quantitative performance assessment. Its use in spaceflight will help provide insight into operational performance, as well as allowing investigators to compare these results with more traditional cognitive tests to help better understand the interaction between individual cognitive abilities and operational performance.Ivkovic V, Sommers B, Cefaratti DA, Newman G, Thomas DW, Alexander DG, Strangman GE. Operationally relevant behavior assessment using the Robotic On-Board Trainer for Research (ROBoT-r). Aerosp Med Hum Perform. 2019; 90(9):819-825.

Feasibility of Automated Cameras to Measure Screen Use in Adolescents.

INTRODUCTION: The influence of screens and technology on adolescent well-being is controversial and there is a need to improve methods to measure these behaviors. This study examines the feasibility and acceptability of using automated wearable cameras to measure evening screen use in adolescents. METHODS: A convenience sample of adolescents (aged 13-17 years, n=15) wore an automated camera for 3 evenings from 5:00pm to bedtime. The camera (Brinno TLC120) captured an image every 15 seconds. Fieldwork was completed between October and December 2017, and data analyzed in 2018. Feasibility was examined by quality of the captured images, wear time, and whether images could be coded in relation to contextual factors (e.g., type of screen and where screen use occurred). Acceptability was examined by participant compliance to the protocol and from an exit interview. RESULTS: Data from 39 evenings were analyzed (41,734 images), with a median of 268 minutes per evening. The camera was worn for 78% of the evening on Day 1, declining to 51% on Day 3. Nearly half of the images contained a screen in active use (46%), most commonly phones (13.7%), TV (12.6%), and laptops (8.2%). Multiple screen use was evident in 5% of images. Within the exit interview, participants raised no major concerns about wearing the camera, and data loss because of deletions or privacy concerns was minimal (mean, 14 minutes, 6%). CONCLUSIONS: Automated cameras offer a feasible, acceptable method of measuring prebedtime screen behavior, including environmental context and aspects of media multitasking in adolescents.

Long-term imaging of dorsal root ganglia in awake behaving mice.

The dorsal root ganglia (DRG) contain the somas of first-order sensory neurons critical for somatosensation. Due to technical difficulties, DRG neuronal activity in awake behaving animals remains unknown. Here, we develop a method for imaging DRG at cellular and subcellular resolution over weeks in awake mice. The method involves the installation of an intervertebral fusion mount to reduce spinal movement, and the implantation of a vertebral glass window without interfering animals' motor and sensory functions. In vivo two-photon calcium imaging shows that DRG neuronal activity is higher in awake than anesthetized animals. Immediately after plantar formalin injection, DRG neuronal activity increases substantially and this activity upsurge correlates with animals' phasic pain behavior. Repeated imaging of DRG over 5 weeks after formalin injection reveals persistent neuronal hyperactivity associated with ongoing pain. The method described here provides an important means for in vivo studies of DRG functions in sensory perception and disorders.

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data.

BACKGROUND: Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. OBJECTIVE: The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. METHODS: Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. RESULTS: The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). CONCLUSIONS: Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals' health and well-being.

Video surveillance system against anti-terrorism by Using Adaptive Linear Activity Classification (ALAC) Technique.

Automated human activity analysis has been, and remains, a challenging problem. Security and surveillance are essential issues in today's world. Any behavior which is uncommon in occurrence and deviates from customarily understood action could be termed as suspicious. For different application regions, while identifying human exercises, fundamentally three angles are taking in worry for human movement recognition system: Segmentation, feature extraction, and activity classification. This model aims at automatic detection of abnormal behavior in surveillance videos. In this proposed work adaptive linear activity classification method and internet of things (IoT) frameworks are used to detection human activities as well as to find out who is doing unusual activities. The enhanced plan of the built environment condition will give a better observation. Such framework can be actualized in peoples in general places, for example, shopping centers, airports, and railway station or any private premises where security is the prime concern. The proposed ALAC method validated through simulation using MATLAB and VB.net software. Its ability to detect the activity of human the simulation result shows the effectiveness using ALAC method, Overall 97% efficiency achieved by using ALAC method.

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care.

BACKGROUND: Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others. OBJECTIVE: The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices. METHODS: A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week. RESULTS: The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones. CONCLUSIONS: This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant.

Quantifying ultrasonic mouse vocalizations using acoustic analysis in a supervised statistical machine learning framework.

Examination of rodent vocalizations in experimental conditions can yield valuable insights into how disease manifests and progresses over time. It can also be used as an index of social interest, motivation, emotional development or motor function depending on the animal model under investigation. Most mouse communication is produced in ultrasonic frequencies beyond human hearing. These ultrasonic vocalizations (USV) are typically described and evaluated using expert defined classification of the spectrographic appearance or simplistic acoustic metrics resulting in nine call types. In this study, we aimed to replicate the standard expert-defined call types of communicative vocal behavior in mice by using acoustic analysis to characterize USVs and a principled supervised learning setup. We used four feature selection algorithms to select parsimonious subsets with maximum predictive accuracy, which are then presented into support vector machines (SVM) and random forests (RF). We assessed the resulting models using 10-fold cross-validation with 100 repetitions for statistical confidence and found that a parsimonious subset of 8 acoustic measures presented to RF led to 85% correct out-of-sample classification, replicating the experts' labels. Acoustic measures can be used by labs to describe USVs and compare data between groups, and provide insight into vocal-behavioral patterns of mice by automating the process on matching the experts' call types.

Emotion analysis in children through facial emissivity of infrared thermal imaging.

Physiological signals may be used as objective markers to identify emotions, which play relevant roles in social and daily life. To measure these signals, the use of contact-free techniques, such as Infrared Thermal Imaging (IRTI), is indispensable to individuals who have sensory sensitivity. The goal of this study is to propose an experimental design to analyze five emotions (disgust, fear, happiness, sadness and surprise) from facial thermal images of typically developing (TD) children aged 7-11 years using emissivity variation, as recorded by IRTI. For the emotion analysis, a dataset considered emotional dimensions (valence and arousal), facial bilateral sides and emotion classification accuracy. The results evidence the efficiency of the experimental design with interesting findings, such as the correlation between the valence and the thermal decrement in nose; disgust and happiness as potent triggers of facial emissivity variations; and significant emissivity variations in nose, cheeks and periorbital regions associated with different emotions. Moreover, facial thermal asymmetry was revealed with a distinct thermal tendency in the cheeks, and classification accuracy reached a mean value greater than 85%. From the results, the emissivity variations were an efficient marker to analyze emotions in facial thermal images, and IRTI was confirmed to be an outstanding technique to study emotions. This study contributes a robust dataset to analyze the emotions of 7-11-year-old TD children, an age range for which there is a gap in the literature.