Stressing the accuracy: Wrist-worn wearable sensor validation over different conditions

Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial

Introduction

Forensic psychiatric patients receive treatment to address their violent and aggressive behavior with the aim of facilitating their safe reintegration into society. On average, these treatments are effective, but the magnitude of effect sizes tends to be small, even when considering more recent advancements in digital mental health innovations. Recent research indicates that wearable technology has positive effects on the physical and mental health of the general population, and may thus also be of use in forensic psychiatry, both for patients and staff members. Several applications and use cases of wearable technology hold promise, particularly for patients with mild intellectual disability or borderline intellectual functioning, as these devices are thought to be user-friendly and provide continuous daily feedback.

Method

In the current randomized crossover trial, we addressed several limitations from previous research and compared the (continuous) usability and acceptance of four selected wearable devices. Each device was worn for one week by staff members and patients, amounting to a total of four weeks. Two of the devices were general purpose fitness trackers, while the other two devices used custom made applications designed for bio-cueing and for providing insights into physiological reactivity to daily stressors and events.

Results

Our findings indicated significant differences in usability, acceptance and continuous use between devices. The highest usability scores were obtained for the two fitness trackers (Fitbit and Garmin) compared to the two devices employing custom made applications (Sense-IT and E4 dashboard). The results showed similar outcomes for patients and staff members.

Discussion

None of the devices obtained usability scores that would justify recommendation for future use considering international standards; a finding that raises concerns about the adaptation and uptake of wearable technology in the context of forensic psychiatry. We suggest that improvements in gamification and motivational aspects of wearable technology might be helpful to tackle several challenges related to wearable technology.

Investigating the association of anxiety disorders with heart rate variability measured using a wearable device

BACKGROUND

Reduced vagally-mediated heart rate variability (HRV) has been associated with anxiety disorders (AD). The aim of this study was to use a wearable device and remote study design to re-evaluate the association of HRV with ADs, anxiety-related traits, and confounders.

METHODS

240 individuals (AD = 120, healthy controls = 120) completed an at-home assessment of their short-term resting vagally-mediated HRV using a wristband, monitored over videoconference. Following quality control, analyses were performed investigating differences in HRV between individuals with AD (n = 119) and healthy controls (n = 116), associations of HRV with anxiety-related traits and confounders, and antidepressants effects on HRV in patients, including analyses stratified by ancestry (i.e., European, East Asian, African).

RESULTS

Among the confounders investigated, only age had a significant association with HRV. Patients with an AD had significantly lower vagally-mediated HRV than healthy controls in the European subsample, with a trend of significance in the whole sample. HRV was significantly associated with the Hamilton Anxiety Rating Scale (HAM-A) but not with antidepressant use in the European subsample.

LIMITATIONS

The study measures occurred in a non-standardized at-home setting, and the three ancestry group sample sizes were unequal.

CONCLUSIONS

This study demonstrates reduced vagally-mediated HRV among patients with ADs compared to healthy controls. Results also point to low HRV being related to more physical anxiety symptoms (measured via HAM-A), suggesting a possible anxiety subtype. Overall, this study highlights the feasibility of using wearables for patients and encourages exploration of the biological and clinical utility of HRV as a risk factor for ADs.

State of the science and recommendations for using wearable technology in sleep and circadian research

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings

Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18-64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = - .27, p = .03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98-1.00 vs. 0.75-0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95-0.99 vs. 0.85-0.98; concurrent validity: 0.95-1.00 vs. 0.75-0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99-1.00 vs. 0.89-0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest.

Quantifying the accuracy of inter-beat intervals acquired from consumer-grade photoplethysmography wristbands using an electrocardiogram-aided information-based similarity approach

Objective. Although inter-beat intervals (IBI) and the derived heart rate variability (HRV) can be acquired through consumer-grade photoplethysmography (PPG) wristbands and have been applied in a variety of physiological and psychophysiological conditions, their accuracy is still unsatisfactory.Approach.In this study, 30 healthy participants concurrently wore two wristbands (E4 and Honor 5) and a gold-standard electrocardiogram (ECG) device under four conditions: resting, deep breathing with a frequency of 0.17 Hz and 0.1 Hz, and mental stress tasks. To quantitatively validate the accuracy of IBI acquired from PPG wristbands, this study proposed to apply an information-based similarity (IBS) approach to quantify the pattern similarity of the underlying dynamical temporal structures embedded in IBI time series simultaneously recorded using PPG wristbands and the ECG system. The occurrence frequency of basic patterns and their rankings were analyzed to calculate the IBS distance from gold-standard IBI, and to further calculate the signal-to-noise ratio (SNR) of the wristband IBI time series.Main results.The accuracies of both HRV and mental state classification were not satisfactory due to the low SNR in the wristband IBI. However, by rejecting data segments of SNR < 25, the Pearson correlation coefficients between the wristbands' HRV and the gold-standard HRV were increased from 0.542 ± 0.235 to 0.922 ± 0.120 for E4 and from 0.596 ± 0.227 to 0.859 ± 0.145 for Honor 5. The average accuracy of four-class mental state classification increased from 77.3% to 81.9% for E4 and from 79.3% to 83.3% for Honor 5.Significance.Consumer-grade PPG wristbands are acceptable for HR and HRV monitoring when removing low SNR segments. The proposed method can be applied for quantifying the accuracies of IBI and HRV indices acquired via any non-ECG system.

Technology and physical activity for preventing cognitive and physical decline in older adults: Protocol of a pilot RCT

BACKGROUND

Cognitive frailty, defined as having both physical frailty and cognitive impairment that does not satisfy the criteria for Major Neurocognitive Disorder, represents an elevated risk for morbidity. Hence, it is crucial to mitigate such risks. Physical activity interventions have been found effective in protecting against physical frailty and cognitive deterioration. This pilot RCT examines if smartwatches and mobile phone applications can help to increase physical activity, thereby improving physical and cognitive outcomes.

METHODS

Older individuals (n = 60) aged 60 to 85 years old will have their physical activity tracked using a smartwatch. The subjects will be randomized into two arms: one group will receive daily notification prompts if they did not reach the recommended levels of PA; the control group will not receive prompts. Outcome variables of physical activity level, neurocognitive scores, and physical frailty scores will be measured at baseline, T1 (3 months), and T2 (6 months). Sleep quality, levels of motivation, anxiety, and depression will be controlled for in our analyses. We hypothesize that the intervention group will have higher levels of physical activity resulting in improved cognitive and physical outcomes at follow-up. This study was approved by the National University of Singapore's Institutional Review Board on 17 August 2020 (NUS-IRB Ref. No.: H-20-038).

DISCUSSION

Wearable sensors technology could prove useful by facilitating self-management in physical activity interventions. The findings of this study can justify the use of technology in physical activity as a preventive measure against cognitive frailty in older adults. This intervention also complements the rapidly rising use of technology, such as smartphones and wearable health devices, in our lives today.

REGISTRATION DETAILS

This study has been retrospectively registered on clinicaltrials.gov on 5th January 2021 (NCT Identifier: NCT04692974), after the first participant was recruited.

Effect of music therapy intervention on anxiety and pain during percutaneous renal biopsy: a randomized controlled trial

Background

Percutaneous renal biopsy (PRB) may subject patients to emotional distress and pain before and during the biopsy. The aim of this study was to evaluate the effects of complementary/non-pharmacological interventions such as music therapy (MT) on anxiety, pain and satisfaction in renal patients undergoing PRB.

Methods

A prospective, single-centre, single-blind, randomized controlled two-arm trial was conducted. Patients ≥18 years of age, hospitalized at the Nephrology, Dialysis and Transplantation Unit (Bari, Italy) and scheduled for PRB were screened. Participants were assigned to standard treatment (CG) or to the music therapy (MT) intervention group. Participants in the MT group received standard care and an MT intervention by a certified music therapist qualified in guided imagery and music. The CG patients received the standard of care. MT and CG patients were subjected to identical measurements (pre/post) of the parameters in the State Trait Anxiety Inventory Y1 (STAI-Y1), visual analogue scale for pain (VAS-P) and satisfaction (VAS-S) and heart rate variability.

Results

A statistically significant difference in the anxiety scores after PRB between MT and CG patients (STAI-Y1 35.4 ± 6.2 versus 42.9 ± 9.0) was observed. MT also had strong and significant effects on VAS-P compared with CG (5.0 ± 1.4 versus 6.3 ± 1.3, respectively; P < .001) and VAS-S (7.8 ± 1.0 versus 6.0 ± 0.9, respectively; P < .001). Decreased activity of the sympathetic nervous system and increased activity of the parasympathetic nervous system was observed after PRB in the MT group.

Conclusion

Our study supports the use of MT to mitigate the psychological anxiety, pain and sympathetic activation associated with PRB.

Validity and Efficacy of the Elite HRV Smartphone Application during Slow-Paced Breathing

Slow-paced breathing is a clinical intervention used to increase heart rate variability (HRV). The practice is made more accessible via cost-free smartphone applications like Elite HRV. We investigated whether Elite HRV can accurately measure and augment HRV via its slow-paced breathing feature. Twenty young adults completed one counterbalanced cross-over protocol involving 10 min each of supine spontaneous (SPONT) and paced (PACED; 6 breaths·min-1) breathing while RR intervals were simultaneously recorded via a Polar H10 paired with Elite HRV and reference electrocardiography (ECG). Individual differences in HRV between devices were predominately skewed, reflecting a tendency for Elite HRV to underestimate ECG-derived values. Skewness was typically driven by a limited number of outliers as median bias values were ≤1.3 ms and relative agreement was ≥very large for time-domain parameters. Despite no significant bias and ≥large relative agreement for frequency-domain parameters, limits of agreement (LOAs) were excessively wide and tended to be wider during PACED for all HRV parameters. PACED significantly increased low-frequency power (LF) for Elite HRV and ECG, and between-condition differences showed very large relative agreement. Elite HRV-guided slow-paced breathing effectively increased LF values, but it demonstrated greater precision during SPONT and in computing time-domain HRV.

Feasibility, Acceptability, and Usability of Physiology and Emotion Monitoring in Adults and Children Using the Novel Time2Feel Smartphone Application

The present study tests the feasibility, acceptability, and utility of the novel smartphone application-Time2Feel-to monitor family members' emotional experiences, at the experiential and physiological level, and their context. To our knowledge, Time2Feel is the first of its kind, having the capability to monitor multiple members' emotional experiences simultaneously and survey users' emotional experiences when experiencing an increase in physiological arousal. In this study, a total of 44 parents and children used Time2Feel along with the Empatica E4 wrist-wearable device for 10 days. Engagement rates were within the acceptable range and consistent with previous work using experience sampling methods. Perceived ease of use and satisfaction fell mostly in the moderate range, with users reporting challenges with connectivity. We further discuss how addressing connectivity would increase acceptability. Finally, Time2Feel was successful at identifying physiological deviations in electrodermal activity for parents and children alike, and even though responses to those deviation-generated surveys were largely consistent with random survey responses, some differences were noted for mothers and fathers. We discuss the implications of using Time2Feel for understanding families' emotional and stressful experiences day-to-day.

Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall

Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition.

Pulse rate variability: An alternative to heart rate variability in adults with spinal cord injury

Pulse rate variability (PRV) is often used as an alternative to heart rate variability (HRV) to measure psychophysiological function. However, its validity to do so is unclear, especially in adults with spinal cord injury (SCI). This study compared PRV and HRV in adults with higher-level SCI (SCI-H, n = 23), lower-level SCI (SCI-L, n = 22), and able-bodied participants (AB n = 44), in a seated position as a function of performance in a reactivity task (Oxford Sleep Resistance Test: OSLER). PRV and HRV was measured using reflective finger-based photoplethysmography (PPG) and electrocardiography, respectively, at baseline, immediately post-OSLER, and after five-minute recovery. Agreement between PRV and HRV was determined by Bland-Altman analysis and differences between PRV and HRV over time by linear mixed effects model (LMM) analysis. Concurrent validity was assessed through correlation analyses between PRV and HRV. Additional correlation analyses were performed with psychosocial factors. Results indicated insufficient to moderate agreement between PRV and HRV. LMM analyses indicated no differences over time for standard deviation of normal-to-normal intervals and low-frequency power but significant differences for root mean square of successive differences and high frequency power. Nevertheless, PRV and HRV were highly correlated (Median r = .878 (.675-.990)) during all assessment periods suggesting sufficient concurrent validity. Similar correlation patterns were also found for PRV and HRV with psychosocial outcomes. While differences existed, results suggest PRV derived from reflective finger-based PPG is a valid proxy of HRV in tracking psychophysiological function in adults with SCI and could therefore be used as a more accessible monitoring tool.

Neurobiological mechanisms of ECT and TMS treatment in depression: study protocol of a multimodal magnetic resonance investigation

BACKGROUND

Noninvasive neurostimulation treatments are increasingly being used to treat major depression, which is a common cause of disability worldwide. While electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) are both effective in treating depressive episodes, their mechanisms of action are, however, not completely understood. ECT is given under general anesthesia, where an electrical pulse is administered through electrodes placed on the patient's head to trigger a seizure. ECT is used for the most severe cases of depression and is usually not prescribed before other options have failed. With TMS, brain stimulation is achieved through rapidly changing magnetic fields that induce electric currents underneath a ferromagnetic coil. Its efficacy in depressive episodes has been well documented. This project aims to identify the neurobiological underpinnings of both the effects and side effects of the neurostimulation techniques ECT and TMS.

METHODS

The study will utilize a pre-post case control longitudinal design. The sample will consist of 150 subjects: 100 patients (bipolar and major depressive disorder) who are treated with either ECT (N = 50) or TMS (N = 50) and matched healthy controls (N = 50) not receiving any treatment. All participants will undergo multimodal magnetic resonance imaging (MRI) as well as neuropsychological and clinical assessments at multiple time points before, during and after treatment. Arterial spin labeling MRI at baseline will be used to test whether brain perfusion can predict outcomes. Signs of brain disruption, potentiation and rewiring will be explored with resting-state functional MRI, magnetic resonance spectroscopy and multishell diffusion weighted imaging (DWI). Clinical outcome will be measured by clinician assessed and patient reported outcome measures. Memory-related side effects will be investigated, and specific tests of spatial navigation to test hippocampal function will be administered both before and after treatment. Blood samples will be stored in a biobank for future analyses. The observation time is 6 months. Data will be explored in light of the recently proposed disrupt, potentiate and rewire (DPR) hypothesis.

DISCUSSION

The study will contribute data and novel analyses important for our understanding of neurostimulation as well as for the development of enhanced and more personalized treatment.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05135897.

Wrist-Worn Sensor Validation for Heart Rate Variability and Electrodermal Activity Detection in a Stressful Driving Environment

Wearable sensors are widely used to gather psychophysiological data in the laboratory and real-world applications. However, the accuracy of these devices should be carefully assessed. The study focused on testing the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Fourteen healthy subjects were recruited for the experimental campaign, where HRV and EDA were recorded over six experimental conditions (Baseline, Video Clip, Scream, No-Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The overall quality of the E4 data was investigated; agreement and reliability were assessed by performing a Bland-Altman analysis and by computing the Spearman's correlation coefficient. HRV time-domain parameters reported high reliability levels in Baseline (r > 0.72), Video Clip (r > 0.71), and No-Risk Driving (r > 0.67), while HRV frequency domain parameters were sufficient in Baseline (r > 0.58), Video Clip (r > 0.59), No-Risk (r > 0.51), and Low-Risk Driving (r > 0.52). As for the EDA parameters, no correlation was found. Further studies could enhance the HRV and EDA quality through further optimizations of the acquisition protocol and improvement of the processing algorithms.

Reduced heart rate variability during mania in a repeated naturalistic observational study

Background

Bipolar disorder (BD) is a chronic recurrent mood disorder associated with autonomic nervous system (ANS) dysfunction, indexed by heart rate variability (HRV). Changes in HRV between mood states are sparsely studied longitudinally. We aimed to compare HRV of hospitalized manic individuals with their own euthymic selves in a naturalistic observational study.

Methods

34 individuals were included, of which 16 were lost to follow-up. Ultimately 15 patients provided reliable heart rate data in both a manic and euthymic state, using photoplethysmography (PPG) sensor wristbands overnight. We calculated HRV measures Root Mean Square of Successive Differences (RMSSD), High-frequency (HF: 0.15-0.40 Hz), Low-frequency (LF: 0.40-0.15 Hz), Very low-frequency (VLF: 0.0033-0.04 Hz), Total power and Sample Entropy in 5-min night-time resting samples. We compared HRV measures by mood state within individuals using paired t-tests and linear regression to control for age and sex.

Results

HRV was lower in the manic state when compared to the euthymic state for all HRV metrics (p ≤ 0.02), with large to medium effect sizes (g = 1.24 to 0.65). HRV changes were not significantly affected by age or sex.

Conclusion

This longitudinal study provides evidence of lower HRV in manic states compared to euthymia, indicating an association between ANS dysregulation and changes in bipolar mood state. This corroborates previous cross-sectional studies, although the association may be less clear or reversed in hypomanic states. Further investigation in larger longitudinal samples is warranted.

Examining Participant Adherence with Wearables in an In-the-Wild Setting

Wearable devices offer a wealth of data for ubiquitous computing researchers. For instance, sleep data from a wearable could be used to identify an individual's harmful habits. Recently, devices which are unobtrusive in size, setup, and maintenance are becoming commercially available. However, most data validation for these devices come from brief, short-term laboratory studies or experiments which have unrepresentative samples that are also inaccessible to most researchers. For wearables research conducted in-the-wild, the prospect of running a study has the risk of financial costs and failure. Thus, when researchers conduct in-the-wild studies, the majority of participants tend to be university students. In this paper, we present a month-long in-the-wild study with 31 Japanese adults who wore a sleep tracking device called the Oura ring. The high device usage results found in this study can be used to inform the design and deployment of longer-term mid-size in-the-wild studies.

On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild

Wearable sensors are quickly making their way into psychophysiological research, as they allow collecting data outside of a laboratory and for an extended period of time. The present tutorial considers fidelity of physiological measurement with wearable sensors, focusing on reliability. We elaborate on why ensuring reliability for wearables is important and offer statistical tools for assessing wearable reliability for between participants and within-participant designs. The framework offered here is illustrated using several brands of commercially available heart rate sensors. Measurement reliability varied across sensors and, more importantly, across the situations tested, and was highest during sleep. Our hope is that by systematically quantifying measurement reliability, researchers will be able to make informed choices about specific wearable devices and measurement procedures that meet their research goals.

Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3

The capability of measuring specific neurophysiological and autonomic parameters plays a crucial role in the objective evaluation of a human's mental and emotional states. These human aspects are commonly known in the scientific literature to be involved in a wide range of processes, such as stress and arousal. These aspects represent a relevant factor especially in real and operational environments. Neurophysiological autonomic parameters, such as Electrodermal Activity (EDA) and Photoplethysmographic data (PPG), have been usually investigated through research-graded devices, therefore resulting in a high degree of invasiveness, which could negatively interfere with the monitored user's activity. For such a reason, in the last decade, recent consumer-grade wearable devices, usually designed for fitness-tracking purposes, are receiving increasing attention from the scientific community, and are characterized by a higher comfort, ease of use and, therefore, by a higher compatibility with daily-life environments. The present preliminary study was aimed at assessing the reliability of a consumer wearable device, i.e., the Fitbit Sense, with respect to a research-graded wearable, i.e., the Empatica E4 wristband, and a laboratory device, i.e., the Shimmer GSR3+. EDA and PPG data were collected among 12 participants while they performed multiple resting conditions. The results demonstrated that the EDA- and PPG-derived features computed through the wearable and research devices were positively and significantly correlated, while the reliability of the consumer device was significantly lower.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

Parameters Influencing the Accuracy of a Wrist Photoplethysmography Heart-Rate Monitor (Polar Unite) During Exercise

PURPOSE

The accuracy of heart rate measured with a wrist photoplethysmography monitor can be influenced by the tightening of the wristband, movement of arms, or kinetics of the signal (eg, steady-state exercise vs on- and off-transients). To test these hypotheses, photoplethysmographic and electrocardiographic (ECG) signals were compared.

METHODS

Thirty participants (50% female) randomly performed two 13' sequences (3' rest, 5' submaximal-intensity exercise, and 5' passive recovery) on a motorized treadmill and a bicycle ergometer. Heart rate was measured concomitantly with a 10-lead ECG, a chest-strap monitor, and 2 wrist photoplethysmography monitors (Polar Unite) with different tightening (free vs imposed at the maximum tolerable).

RESULTS

The level of association (r) and coefficient of variation (CV; ie, the error of measurement) of the Polar Unite versus the 10-lead ECG is affected by the tightness of the wristband (normal vs high; r = .83 and .96, CV = 16.1 and 8.1% for the treadmill, respectively; r = .71 and .97, CV = 20.3% and 6.2% for the bicycle, respectively) by the phase of the signal (transition vs steady state; r = .90 and .97, CV = 9.0% and 7.6% for the treadmill, respectively; r = .93 and .99, CV = 7.5% and 3.1% for the bicycle, respectively) and movement of arms (treadmill vs bicycle; r = .90 and .93, CV = 9.0% and 7.5% during the transition phase, respectively; r = .97 and .99, CV = 7.6% and 3.1% during the steady-state phase, respectively).

CONCLUSION

The accuracy of heart rate measured with a wrist photoplethysmography monitor is affected by the tightness of the wristband and the phase of the signal. A high tightening is required when high accuracy is expected.

A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability

BACKGROUND

Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions.

OBJECTIVE

We evaluate the accuracy of PPG signals-collected by the Samsung Gear Sport smartwatch in free-living conditions-in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor.

METHODS

We conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods.

RESULTS

We found a significantly high positive correlation between the smartwatch's and Shimmer ECG's HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch's and shimmer ECG's LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances.

CONCLUSION

The Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.

Comparing Heart Rate Analytical Methods to Examine Engagement During Imaginal Prolonged Exposure Therapy: Multicase Study

OBJECTIVE

As mobile health technologies proliferate, their use during exposure-based therapies has the potential to illuminate treatment mechanisms. The primary purpose of this study was to examine three approaches to using continuously collected physiological data of patients with posttraumatic stress disorder during prolonged exposure (PE) therapy, in an effort to examine physiological markers of treatment response.

METHODS

Photoplethysmogram-measured heart rates from three non-Hispanic White male veterans, during clinic-based PE therapy sessions, were analyzed to assess three potential therapeutic mechanisms: emotional engagement (examined via correlation analysis between self-reported peak distress ratings and objectively measured peak heart rate in the minute prior to distress ratings), initial emotion activation (examined through time to peak heart rate and peak self-reported distress), and extinction processes within and between therapy sessions (examined via multilevel modeling of within- and between-person changes in heart rate over time and across imaginal PE therapy sessions).

RESULTS

Results for each analytical approach with each patient are presented, and benefits and limitations of each approach are discussed. Treatment outcomes were as follows: one participant with overengagement did not benefit from PE, one participant with initial underengagement demonstrated clinical improvement, and one participant with optimal engagement had associated clinical improvements.

CONCLUSIONS

Mobile health technologies may provide a new avenue toward unveiling treatment mechanisms in psychotherapy. Use of standardized analytical approaches will enable cross-study comparison and greater understanding of treatment mechanisms, ultimately leading to increased treatment response.

A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices

We present a multi-device and multi-modal dataset, called WEEE, collected from 17 participants while they were performing different physical activities. WEEE contains: (1) sensor data collected using seven wearable devices placed on four body locations (head, ear, chest, and wrist); (2) respiratory data collected with an indirect calorimeter serving as ground-truth information; (3) demographics and body composition data (e.g., fat percentage); (4) intensity level and type of physical activities, along with their corresponding metabolic equivalent of task (MET) values; and (5) answers to questionnaires about participants' physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations, we believe that the dataset will enable the development of novel human energy expenditure (EE) estimation techniques for a diverse set of application scenarios. EE refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. A reliable estimate of people's EE thus enables computing systems to make inferences about users' physical activity and help them promoting a healthier lifestyle.

Early indicators of vulnerability to depression: The role of rumination and heart rate variability

BACKGROUND

Despite the evidence of increased levels of rumination and reduced heart rate variability (HRV) in depression, whether these measures can be considered early indicators of vulnerability to depression has yet to be investigated. Therefore, the present study aimed to investigate both levels of rumination and resting HRV in individuals with familial risk for depression that is the most reliable risk factor for the disorder.

METHODS

Rumination and vagally-mediated HRV were assessed using the Ruminative Response Scale and a smartphone-based photoelectric volumetric pulse wave assay, respectively, in 25 individuals who had family history of depression (but did not report current depressive symptoms), 15 individuals who reported depressive symptoms (but had no family history of depression), and 25 controls (without depressive symptoms and family history of depression).

RESULTS

Individuals with depressive symptoms and those with a family history of depression were characterized by higher levels of rumination and lower cardiac vagal control than controls.

LIMITATIONS

Given the small sample size, this study should be used to design larger confirmatory studies; the cross-sectional nature of the study does not allow discussing the results in terms of cause-effect relationships.

CONCLUSIONS

Our findings suggested that individuals at risk of developing depression, also in absence of depressive symptoms, are defined by defective self-regulation capacity that may lead to future depression episodes. Increased ruminative thoughts and reduced HRV may represent early indicators of vulnerability to depression. Effective prevention programs designed to reduce rumination and/or increase HRV may reduce the risk of developing a full-blown depressive episode.

MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals

This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user's face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible.

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a “Wearables” R package and a Shiny “E4 dashboard” application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.

Investigating How Auditory and Visual Stimuli Promote Recovery After Stress With Potential Applications for Workplace Stress and Burnout: Protocol for a Randomized Trial

Background

Work-related stress is one of the top sources of stress amongst working adults. Relaxation rooms are one organizational strategy being used to reduce workplace stress. Amongst healthcare workers, relaxation rooms have been shown to improve perceived stress levels after 15 min of use. However, few studies have examined physiological and cognitive changes after stress, which may inform why relaxation rooms reduce perceived stress. Understanding the biological mechanisms governing why perceived stress improves when using a relaxation room could lead to more effective strategies to address workplace stress.

Objective

The purpose of this research study is to understand how physiological measures, cognitive performance, and perceived stress change after acute stress and whether certain sensory features of a relaxation room are more effective at promoting recovery from stress.

Methods

80 healthy adults will perform a stress induction task (Trier Social Stress Test, TSST) to evaluate how physiological and cognitive responses after stress are affected by sensory features of a relaxation room. After the stress induction task, participants will recover for 40 min in a MindBreaks™ relaxation room containing auditory and visual stimuli designed to promote relaxation. Participants will be randomized into four cohorts to experience auditory and visual stimuli; auditory stimuli; visual stimuli; or no stimuli in the room. Measures of heart rate and neural activity will be continuously monitored using wearable devices. Participants will perform working memory assessments and rate their perceived stress levels throughout the experiment. These measures will be compared before and after the stress induction task to determine how different sensory stimuli affect the rate at which individuals recover.

Results

Recruitment started in December 2021 and will continue until December 2022 or until enrollment is completed. Final data collection and subsequent analysis are anticipated by December 2022. We expect all trial results will be available by early 2023.

Discussion

Findings will provide data and information about which sensory features of a relaxation room are most effective at promoting recovery after acute stress. This information will be useful in determining how these features might be effective at creating individualized and organizational strategies for mitigating the effects of workplace stress.

Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables

We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. The dataset was used to derive predictive models of how occupants operate room air-conditioning units. Second, we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data, as well as daily surveys to query the occupants' thermal comfort, learning engagement, emotions and seating behaviours. Overall, the combined dataset could be used to analyse the relationships between indoor/outdoor climates and students' behaviours/mental states on campus, which provide opportunities for the future design of intelligent feedback systems to benefit both students and staff.

An ideographic study into physiology, alcohol craving and lapses during one hundred days of daily life monitoring

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In a 100 days intensive study considerable intra- and interindividual differences were established in physiology and psychological craving of people with alcohol use disorder.

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For one third of the people heightened heart rate was associated with high craving.

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For most participants that reported lapses, lapses cooccurred with craving in at least 92% of the time.

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During treatment ambulatory physiological data can support the detection and discussion of possible high risk craving situations in innovative and reliable ways.

Introduction

Alcohol craving is a highly challenging obstacle to achieve long-term abstinence. Making alcohol use disorder patients timely aware of high-risk craving situations may protect them against relapse by prompting them to mobilize their coping resources. Current advances in wearable and smart-phone technology provide novel opportunities for the development of detecting these situations of heightened risk of craving, by enabling continuous tracking of fluctuations in psychological and physiological parameters. The present study therefore aims to determine the association between self-reported craving and relapses, and between heightened physiological activity. Specifically, we measured cardiovascular and electrodermal activity, and self-reported craving during one hundred days in the daily life of people trying to recover from alcoholism. The secondary aim is to study whether the association between physiology and craving can be strengthened by the inclusion of context related psychological parameters.

Methods

An intensive repeated and continuous measures in naturalistic settings case-study design was employed. Ten participants were monitored with wearable bio-sensors and answered multiple questions every three hours on a smartphone app about craving, lapsing and multiple evidence based contextual variables. The association between physiology, craving and lapses was explored using Matthews correlation coefficients both with a current and 3 h lagged design. The contextual variables were included in a decision tree together with the physiological parameters to explore the added effect on the correlation of these contextual variables.

Results

The association between lapses and craving was highly different across individuals, varying between a weak to a strong association. The association between cardiovascular activity and heightened self-reported craving was negligible to weak, however with a high specificity, meaning that most craving events were accompanied by increase heart rate. However, the association between electrodermal activity and craving was lower than with cardiovascular activity for most participants, both prior (lagged) and during craving. For two of the participants the association between physiology and craving improved by adding contextual variables, however, precision was too low.

Conclusions

People differ strongly in their bodily reactions and psychological experiences during the first months of their addiction treatment. No individual in our study had unique one-to-one mappings between on the one hand physiological or psychological precursors, and on the other hand craving and (re)lapses. Therefore, detecting high risk craving situations with both physiological activity measured with wearables and psychological precursors to alert people specifically for an imminent (re)lapse, does not seem viable on the basis of the current results. We do see an added benefit of using physiology during treatment, as physiology can help start the conversation about possible high risk craving situations during that week. This would also help the counselor to gain added insights into the fluctuating states of the clients, and help to ameliorate the recall bias of clients. The present study showed the possibility and paved the way for future intensive longitudinal designs integrating both physiological, psychological and contextual factors during the challenging and lengthy recovery from addiction.

Wearable devices for continuous monitoring of biosignals: Challenges and opportunities

The ability for wearable devices to collect high-fidelity biosignals continuously over weeks and months at a time has become an increasingly sought-after characteristic to provide advanced diagnostic and therapeutic capabilities. Wearable devices for this purpose face a multitude of challenges such as formfactors with long-term user acceptance and power supplies that enable continuous operation without requiring extensive user interaction. This review summarizes design considerations associated with these attributes and summarizes recent advances toward continuous operation with high-fidelity biosignal recording abilities. The review also provides insight into systematic barriers for these device archetypes and outlines most promising technological approaches to expand capabilities. We conclude with a summary of current developments of hardware and approaches for embedded artificial intelligence in this wearable device class, which is pivotal for next generation autonomous diagnostic, therapeutic, and assistive health tools.

Heart rate prediction from facial video with masks using eye location and corrected by convolutional neural networks

Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications. The emergence of novel coronavirus pneumonia COVID-19 has attracted worldwide attentions. Contact photoplethysmography (cPPG) methods need to contact the detection equipment with the patient, which may accelerate the spread of the epidemic. In the future, the non-contact heart rate detection will be an urgent need. However, existing heart rate measuring methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and wearing a mask). In this paper, we proposed a method of heart rate detection based on eye location of region of interest (ROI) to solve the problem of missing information when wearing masks. Besides, a model to filter outliers based on residual network was conceived first by us and the better heart rate measurement accuracy was generated. To validate our method, we also created a mask dataset. The results demonstrated that after using our method for correcting the heart rate (HR) value measured with the traditional method, the accuracy reaches 4.65 bpm, which is 0.42 bpm higher than that without correction.

Odour enhances the sense of presence in a virtual reality environment

Virtual reality (VR) headsets provide immersive audio-visual experiences for users, but usually neglect to provide olfactory cues that can provide additional information about our environment in the real world. This paper examines whether the introduction of smells into the VR environment enhances users’ experience, including their sense of presence through collection of both psychological and physiological measures. Using precise odour administration with an olfactometer, study participants were exposed to smells while they were immersed in the popular PlayStation VR game “Resident Evil 7”. A within-subject study design was undertaken where participants (n = 22) walked-through the same VR environment twice, with or without the introduction of associated congruent odour stimuli. Directly after each gameplay, participants completed a questionnaire to determine their sense of presence from the overall gameplay and their sense of immersion in each of the virtual scenes. Additionally, physiological measurements (heart rate, body temperature and skin electrodermal activity) were collected from participants (n = 11) for each gameplay. The results showed the addition of odours significantly increased participants’ sense of spatial presence in the VR environment compared to VR with no odour. Participants also rated the realism of VR experience with odour higher compared to no odour, however odour addition did not result in change in emotional state of participants (arousal, pleasure, dominance). Further, the participants’ physiological responses were impacted by the addition of odour. Odour mediated physiological changes were dependent on whether the VR environment was novel, as the effect of odour on physiological response was lost when participants experienced the aroma on the second gameplay. Overall, the results indicate the addition of odours to a VR environment had a significant effect on both the psychological and physiological experience showing the addition of smell enhanced the VR environment. The incorporation of odours to VR environments presents an opportunity to create a more immersive experience to increase a person’s presence within a VR environment. In addition to gaming, the results have broader applications for virtual training environments and virtual reality exposure therapy.

Nature Ambience in a Lunch Restaurant Has the Potential to Evoke Positive Emotions, Reduce Stress, and Support Healthy Food Choices and Sustainable Behavior: A Field Experiment among Finnish Customers

Laboratory experiments have indicated that exposure to restorative ambiences in food environments can lead to beneficial outcomes for consumers, but there is little evidence if this positive effect holds true in real-life consumption conditions. Therefore, the aim of this study was to analyze the effects of lunch restaurant ambience on customers’ emotional responses, stress recovery, food choices, and generation of plate waste. The expectation was that ambience inducing positive emotional responses would lead to alleviated stress, healthier food choices, and reduced plate waste. A field experiment with a baseline and two experimental ambiences (‘nature ambience’ to induce positive emotions and ‘fast food ambience’ to induce less positive emotions) including visual and auditory stimuli was conducted in a lunch restaurant for one week per ambience. Emotional responses, and objective and subjective stress were measured from a subgroup of participants (n = 32). Food choices and plate waste were measured for all customers (n = 1610–1805 depending on the study week). During ‘nature ambience’ week, customers more often chose vegetarian dishes and generated less plate waste. The results on emotional responses and stress recovery were partially in line with the expectations. The study provides real-life evidence that restaurant ambience modification could lead to beneficial consequences for customers.

Bio-behavioral synchrony is a potential mechanism for mate selection in humans

The decision with whom to form a romantic bond is of great importance, yet the biological or behavioral mechanisms underlying this selective process in humans are largely unknown. Classic evolutionary theories of mate selection emphasize immediate and static features such as physical appearance and fertility. However, they do not explain how initial attraction temporally unfolds during an interaction, nor account for mutual physiological or behavioral adaptations that take place when two people become attracted. Instead, recent theories on social bonding emphasize the importance of co-regulation during social interactions (i.e., the social coordination of physiology and behavior between partners), and predict that co-regulation plays a role in bonding with others. In a speed-date experiment of forty-six heterosexual dates, we recorded the naturally occurring patterns of electrodermal activity and behavioral motion in men and women, and calculated their co-regulation during the date. We demonstrate that co-regulation of behavior and physiology is associated with the date outcome: when a man and a woman synchronize their electrodermal activity and dynamically tune their behavior to one another, they are more likely to be romantically and sexually attracted to one another. This study supports the hypothesis that co-regulation of sympathetic and behavioral rhythms between a man and a woman serves as a mechanism that promotes attraction.

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

Enhancement of Closed-Loop Cognitive Stress Regulation using Supervised Control Architectures

Goal: We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledge-based layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledge-based layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.

The Internet of Cooperative Agents Architecture (X-IoCA) for Robots, Hybrid Sensor Networks, and MEC Centers in Complex Environments: A Search and Rescue Case Study

Cloud robotics and advanced communications can foster a step-change in cooperative robots and hybrid wireless sensor networks (H-WSN) for demanding environments (e.g., disaster response, mining, demolition, and nuclear sites) by enabling the timely sharing of data and computational resources between robot and human teams. However, the operational complexity of such multi-agent systems requires defining effective architectures, coping with implementation details, and testing in realistic deployments. This article proposes X-IoCA, an Internet of robotic things (IoRT) and communication architecture consisting of a hybrid and heterogeneous network of wireless transceivers (H2WTN), based on LoRa and BLE technologies, and a robot operating system (ROS) network. The IoRT is connected to a feedback information system (FIS) distributed among multi-access edge computing (MEC) centers. Furthermore, we present SAR-IoCA, an implementation of the architecture for search and rescue (SAR) integrated into a 5G network. The FIS for this application consists of an SAR-FIS (including a path planner for UGVs considering risks detected by a LoRa H-WSN) and an ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Moreover, we discuss lessons learned from using SAR-IoCA in a realistic exercise where three UGVs, a UAV, and responders collaborated to rescue victims from a tunnel accessible through rough terrain.

Experience with an Affective Robot Assistant for Children with Hearing Disabilities

This study presents an assistive robotic system enhanced with emotion recognition capabilities for children with hearing disabilities. The system is designed and developed for the audiometry tests and rehabilitation of children in a clinical setting and includes a social humanoid robot (Pepper), an interactive interface, gamified audiometry tests, sensory setup and a machine/deep learning based emotion recognition module. Three scenarios involving conventional setup, tablet setup and setup with the robot+tablet are evaluated with 16 children having cochlear implant or hearing aid. Several machine learning techniques and deep learning models are used for the classification of the three test setups and for the classification of the emotions (pleasant, neutral, unpleasant) of children using the recorded physiological signals by E4 wristband. The results show that the collected signals during the tests can be separated successfully and the positive and negative emotions of children can be better distinguished when they interact with the robot than in the other two setups. In addition, the children’s objective and subjective evaluations as well as their impressions about the robot and its emotional behaviors are analyzed and discussed extensively.

Digital Resilience Biomarkers for Personalized Health Maintenance and Disease Prevention

Health maintenance and disease prevention strategies become increasingly prioritized with increasing health and economic burden of chronic, lifestyle-related diseases. A key element in these strategies is the empowerment of individuals to control their health. Self-measurement plays an essential role in achieving such empowerment. Digital measurements have the advantage of being measured non-invasively, passively, continuously, and in a real-world context. An important question is whether such measurement can sensitively measure subtle disbalances in the progression toward disease, as well as the subtle effects of, for example, nutritional improvement. The concept of resilience biomarkers, defined as the dynamic evaluation of the biological response to an external challenge, has been identified as a viable strategy to measure these subtle effects. In this review, we explore the potential of integrating this concept with digital physiological measurements to come to digital resilience biomarkers. Additionally, we discuss the potential of wearable, non-invasive, and continuous measurement of molecular biomarkers. These types of innovative measurements may, in the future, also serve as a digital resilience biomarker to provide even more insight into the personal biological dynamics of an individual. Altogether, digital resilience biomarkers are envisioned to allow for the measurement of subtle effects of health maintenance and disease prevention strategies in a real-world context and thereby give personalized feedback to improve health.

Challenges and Opportunities for Applying Wearable Technology to Sleep

Wearable technology has a history in sleep research dating back to the 1970s. Because modern wearable technology is relatively cheap and widely used by the general population, this represents an opportunity to leverage wearable devices to advance sleep medicine and research. However, there is a lack of published validation studies designed to quantify device performance against accepted gold standards, especially across different populations. Recommendations for conducting performance assessments and using wearable devices are now published with the goal of standardizing wearable device implementation and advancing the field.

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

Stress is a physical, mental, or emotional response to a change and is a significant problem in modern society. In addition to questionnaires, levels of stress may be assessed by monitoring physiological signals, such as via photoplethysmogram (PPG), electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), facial expressions, and head and body movements. In our study, we attempted to find the relationship between the perceived stress level and physiological signals, such as heart rate (HR), head movements, and electrooculographic (EOG) signals. The perceived stress level was acquired by self-assessment questionnaires in which the participants marked their stress level before, during, and after performing a task. The heart rate was acquired with a finger pulse oximeter and the head movements (linear acceleration and angular velocity) and electrooculographic signals were recorded with JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan). We observed significant differences between the perceived stress level, heart rate, the power of linear acceleration, angular velocity, and EOG signals before performing the task and during the task. However, except for HR, these signals were poorly correlated with the perceived stress level acquired during the task.

Validity of electrodermal activity-based measures of sympathetic nervous system activity from a wrist-worn device

Measuring electrodermal activity (EDA) on the wrist with the use of dry electrodes is a promising method to help identify person-specific stressors during prolonged recordings in daily life. While the feasibility of this method has been demonstrated, detailed testing of validity of such ambulatory EDA is scarce. In a controlled laboratory study, we examine SCL and ns.SCR derived from wrist-based dry electrodes (Philips DTI) and palm-based wet electrodes (VU-AMS) in 112 healthy adults (57% females, mean age = 22.3, SD = 3.4) across 26 different conditions involving mental stressors or physical activities. Changes in these EDA measures were compared to changes in the Pre-ejection period (PEP) and stressor-induced changes in affect. Absolute SCL and ns.SCR frequency were lower at the wrist compared to the palm. Wrist-based ns.SCR and palm-based ns.SCR and SCL responded directionally consistent with our experimental manipulation of sympathetic nervous system (SNS) activity. Average within-subject correlations between palm-based and wrist-based EDA were significant but modest (r SCL = 0.31; r ns.SCR = 0.42). Changes in ns.SCR frequency at the palm (r = -0.44) and the wrist (r = -0.36) were correlated with changes in PEP. Both palm-based and wrist based EDA predicted changes in affect (6.5%-14.5%). Our data suggest that wrist-based ns.SCR frequency is a useful addition to the psychophysiologist's toolkit, at least for epidemiology-sized ambulatory studies of changes in sympathetic activity during daily life.

[Use of machine learning for the prediction of stress using the example of logistics]

Stress und seine komplexen Wirkungen werden bereits seit Anfang des 20. Jahrhunderts erforscht. Die vielfältigen psychischen und physischen Stressoren in der Arbeitswelt können in Summe zu Störungen des Organismus und zu Erkrankungen führen. Da die Ausprägung körperlicher und subjektiver Folgen von Stress individuell unterschiedlich ist, lassen sich keine absoluten Grenzwerte ermitteln. Zur Erforschung der systematischen Mustererkennung physiologischer und subjektiver Stressparameter sowie einer Stressvorhersage, werden in dem vorliegenden Beitrag Methoden des maschinellen Lernens (ML) eingesetzt. Als praktischer Anwendungsfall dient die Logistikbranche, in der Belastungsfaktoren häufig in der Tätigkeit und der Arbeitsorganisation begründet liegen. Ein Gestaltungselement bei der Prävention von Stress ist die Arbeitspause. Mit ML-Methoden wird untersucht, inwieweit Stress auf Basis physiologischer und subjektiver Parameter vorhergesagt werden kann, um Pausen individuell zu empfehlen. Im Beitrag wird der Zwischenstand einer Softwarelösung für ein dynamisches Pausenmanagement für die Logistik vorgestellt.

Praktische Relevanz: Das Ziel der Softwarelösung „Dynamische Pause“ besteht darin, Stress in Folge mentaler und physischer Belastungsfaktoren in der Logistik präventiv vorzubeugen und die Beschäftigten auf lange Sicht gesund, zufrieden, arbeitsfähig und produktiv zu halten. Infolge individualisierter Erholungspausen als Gestaltungselement, können Unternehmen unterstützt werden, Personalressourcen entsprechend der dynamischen Anforderungen der Logistik flexibler einzusetzen.

Framework for selecting and benchmarking mobile devices in psychophysiological research

Commercially available consumer electronics in (smartwatches and wearable biosensors) are increasingly enabling acquisition of peripheral physiological and physical activity data inside and outside of laboratory settings. However, there is scant literature available for selecting and assessing the suitability of these novel devices for scientific use. To overcome this limitation, the current paper offers a framework to aid researchers in choosing and evaluating wearable technologies for use in empirical research. Our seven-step framework includes: (1) identifying signals of interest; (2) characterizing intended use cases; (3) identifying study-specific pragmatic needs; (4) selecting devices for evaluation; (5) establishing an assessment procedure; (6) performing qualitative and quantitative analyses on resulting data; and, if desired, (7) conducting power analyses to determine sample size needed to more rigorously compare performance across devices. We illustrate the application of the framework by comparing electrodermal, cardiovascular, and accelerometry data from a variety of commercial wireless sensors (Affectiva Q, Empatica E3, Empatica E4, Actiwave Cardio, Shimmer) relative to a well-validated, wired MindWare laboratory system. Our evaluations are performed in two studies (N = 10, N = 11) involving psychometrically sound, standardized tasks that include physical activity and affect induction. After applying our framework to this data, we conclude that only some commercially available consumer devices for physiological measurement are capable of wirelessly measuring peripheral physiological and physical activity data of sufficient quality for scientific use cases. Thus, the framework appears to be beneficial at suggesting steps for conducting more systematic, transparent, and rigorous evaluations of mobile physiological devices prior to deployment in studies.

Physiological and self-reported arousal in virtual reality versus face-to-face emotional activation and cognitive restructuring in university students: A crossover experimental study using wearable monitoring

BACKGROUND

Arousal may be important for learning to restructure ones' negative cognitions, a core technique in depression treatment. In virtual reality (VR), situations may be experienced more vividly than, e.g., in an imaginative approach, potentially aiding the emotional activation of negative cognitions. However, it is unclear whether such activation and subsequent cognitive restructuring in VR elicits more physiological, e.g. changes in skin conductance (SC), heart rate (HR), and self-reported arousal.

METHOD

In a cross-over experiment, 41 healthy students experienced two sets, one in VR, one face-to-face (F2F), of three situations aimed at activating negative cognitions. Order of the sets and mode of delivery were randomised. A wristband wearable monitored SC and HR; self-reported arousal was registered verbally.

RESULTS

Repeated measures analyses of variance revealed significantly more SC peaks per minute, F (1, 40) = 13.89, p = .001, higher mean SC, F (1,40) = 7.47, p = .001, and higher mean HR, F (1, 40) = 75.84, p < .001 in VR compared to F2F. No differences emerged on the paired-samples t-test for self-reported arousal, t (40) = -1.35, p = .18.

DISCUSSION

To the best of our knowledge, this is the first study indicating that emotional activation and subsequent cognitive restructuring in VR can lead to significantly more physiological arousal compared to an imaginative approach. These findings need to be replicated before they can be extended to patient populations.

Accuracy of heart rate variability estimated with reflective wrist-PPG in elderly vascular patients

Optical heart rate monitoring (OHR) with reflective wrist photoplethysmography is a technique mainly used in the wellness application domain for monitoring heart rate levels during exercise. In the absence of motion, OHR technique is also able to estimate individual beat-to-beat intervals relatively well and can therefore also be used, for example, in monitoring of cardiac arrhythmias, stress, or sleep quality through heart rate variability (HRV) analysis. HRV analysis has also potential in monitoring the recovery of patients, e.g. after a medical intervention. However, in order to detect subtle changes, the calculated HRV parameters should be sufficiently accurate and very few studies exist that asses the accuracy of OHR derived HRV in non-healthy subjects. In this paper, we present a method to estimate beat-to-beat-intervals (BBIs) from reflective wrist PPG signal and evaluated the accuracy of the proposed method in estimating BBIs in a cross-sectional study with 29 hospitalized patients (mean age 70.6 years) in 24-h recordings performed after peripheral vascular surgery or endovascular interventions. Finally, we evaluate the accuracy of more than 30 commonly used HRV parameters and find that the accuracy of certain metrics, for example SDNN and triangular index, shown in the literature to be associated with the deterioration of the status of the patients during recovery from surgical intervention, could be adequate for patient monitoring. On the other hand, the parameters more affected by the high-frequency content of the HRV and especially the LF/HF-ratio should be used with caution.

Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls

We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16-19 years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11 min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9 min. Proportional biases were detected for "light" and "deep" sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+ N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for "light" sleep, 59% for "deep" sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R² = .16, p = .048). The magnitude of the heart rate acceleration/deceleration between consecutive PSG epochs was an important factor affecting FC3 classifications of sleep stages. Our results are in line with a general trend in the literature, suggesting better performance for the recently introduced multi-sensor devices compared to motion-only devices, although further developments are needed to improve accuracy in sleep stage classification and wake detection. Further insight is needed to determine factors potentially affecting device performance, such as accuracy and reliability (consistency of performance over time), in different samples and conditions.

Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states compared to laboratory equipment. The electrooculographic (EOG), electrodermal activity (EDA) and photoplethysmographic (PPG) signals were acquired from a group of 17 volunteers who took part to the experimental protocol in which different working scenarios were simulated to induce different levels of mental workload, stress, and emotional state. The results demonstrated that the parameters computed by the consumer wearable and laboratory sensors were positively and significantly correlated and exhibited the same evidences in terms of mental states discrimination.

Capturing emotion coherence in daily life: Using ambulatory physiology measures and ecological momentary assessments to examine within-person associations and individual differences

While emotion coherence has long been theorized to be a core feature of emotion, to date, studies examining response coherence have been conducted in laboratory settings. The present study used a combined approach of ambulatory physiology measures and ecological momentary assessment conducted over a 4-week period to examine the extent to which emotional experience and physiology show coherence in daily life within-persons; and whether individual differences in response coherence are associated with between-person differences in well-being, negative emotionality, and gender. Results revealed that, on average, individuals exhibited coherence between subjective experience and physiology of emotion, but that there was substantial between-person variation in coherence in daily life. Exploratory analyses revealed no credible link between levels of response coherence and well-being, negative emotionality, or gender. Findings contribute to the literature by demonstrating a novel methodological approach to measuring coherence in daily life and supporting the generalizability of coherence to ecologically valid contexts.

Null effects of therapy dog interaction on adolescent anxiety during a laboratory-based social evaluative stressor

BACKGROUND AND OBJECTIVES

Animal-assisted interventions (AAIs) are increasingly popular as treatments to reduce anxiety. However, there is little empirical evidence testing the mechanisms of action in AAIs, especially among adolescents. We examined whether two possible mechanisms, social interaction and/or physical contact with a therapy dog, might reduce anxiety during a social stressor.

DESIGN AND METHODS

To test these mechanisms, we randomly assigned 75 adolescents with low, middle, and high levels of social anxiety to complete a laboratory-based social evaluative stressor in one of three conditions: social interaction with a therapy dog (no physical interaction), social plus physical interaction with a therapy dog, or no interaction with a therapy dog. We measured self-reported anxiety and autonomic reactivity during the social stressor to assess the effects of contact with a therapy dog.

RESULTS AND CONCLUSIONS

We found no evidence that the presence of a real dog, with or without the opportunity to touch it, reduced anxiety or autonomic reactivity or improved cognitive performance relative to the presence of a stuffed dog in the control condition, regardless of levels of preexisting social anxiety.Trial registration: ClinicalTrials.gov identifier: NCT03249116.