scholarly journals Real-Time Monitoring of Passenger’s Psychological Stress

2019 ◽  
Vol 11 (5) ◽  
pp. 102
Author(s):  
Gaël Vila ◽  
Christelle Godin ◽  
Oumayma Sakri ◽  
Etienne Labyt ◽  
Audrey Vidal ◽  
...  
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
High Stress ◽  
Ambulatory Monitoring ◽  
Real Life ◽  
Smartphone Application ◽  
Physiological Signals ◽  
Sensor Data ◽  
Moderate Activity ◽  
Stress Levels

This article addresses the question of passengers’ experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user’s feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user’s stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler’s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user’s stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant’s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger’s stress while travelling.

scholarly journals Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3956
Author(s):  
Youngsun Kong ◽  
Hugo F. Posada-Quintero ◽  
Ki H. Chon
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Smartphone Application ◽  
Real Time Processing ◽  
Pain Stimulation ◽  
Significant Difference ◽  
Before And After ◽  
Electrical Pulses ◽  
Pain Detection ◽  
High Level

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

scholarly journals ECOCAPTURE@HOME: Protocol for the Remote Assessment of Apathy and Its Everyday-Life Consequences

2021 ◽  
Vol 18 (15) ◽  
pp. 7824
Author(s):  
Valérie Godefroy ◽  
Richard Levy ◽  
Arabella Bouzigues ◽  
Armelle Rametti-Lacroux ◽  
Raffaella Migliaccio ◽  
...  
Keyword(s):  
Everyday Life ◽  
New Technologies ◽  
Electrodermal Activity ◽  
Real Life ◽  
Smartphone Application ◽  
Measurement Model ◽  
Strong Impact ◽  
Active Data ◽  
Personalised Treatment

Apathy, a common neuropsychiatric symptom associated with dementia, has a strong impact on patients’ and caregivers’ quality of life. However, it is still poorly understood and hard to define. The main objective of the ECOCAPTURE programme is to define a behavioural signature of apathy using an ecological approach. Within this program, ECOCAPTURE@HOME is an observational study which aims to validate a method based on new technologies for the remote monitoring of apathy in real life. For this study, we plan to recruit 60 couples: 20 patient-caregiver dyads in which patients suffer from behavioral variant Fronto-Temporal Dementia, 20 patient-caregiver dyads in which patients suffer from Alzheimer Disease and 20 healthy control couples. These dyads will be followed for 28 consecutive days via multi-sensor bracelets collecting passive data (acceleration, electrodermal activity, blood volume pulse). Active data will also be collected by questionnaires on a smartphone application. Using a pool of metrics extracted from these passive and active data, we will validate a measurement model for three behavioural markers of apathy (i.e., daytime activity, quality of sleep, and emotional arousal). The final purpose is to facilitate the follow-up and precise diagnosis of apathy, towards a personalised treatment of this condition within everyday life.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Effect of a real-time feedback smartphone application (TCPRLink) on the quality of telephone-assisted CPR performed by trained laypeople in China: a manikin-based randomised controlled study

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e038813
Author(s):  
Xuejie Dong ◽  
Lin Zhang ◽  
Helge Myklebust ◽  
Tonje Soraas Birkenes ◽  
Zhi-Jie Zheng
Keyword(s):  
Real Time ◽  
Real Life ◽  
Smartphone Application ◽  
Secondary Outcome ◽  
Controlled Study ◽  
Feedback Group ◽  
Randomised Controlled Study ◽  
Release Ratio ◽  
Randomised Controlled ◽  
Off Time

ObjectivesTo determine the effect of a free smartphone application (TCPRLink) that provides real-time monitoring and audiovisual feedback on chest compressions (CC) on trained layperson telephone-assisted cardiopulmonary resuscitation (T-CPR) performance.DesignA manikin-based randomised controlled study.SettingThis study was conducted at a multidisciplinary university and a community centre in China.ParticipantsOne hundred and eighty-six adult participants (age 18–65 years) with T-CPR training experience were randomly assigned to the TCPRLink (n=94) and T-CPR (n=92) groups with age stratification.InterventionsWe compared the participants’ performance for 6 min of CC in a simulated T-CPR scenario both at the baseline and after 3 months.Primary and secondary outcome measuresThe primary outcomes were the CC rate and proportion of adequate CC rate (100–120 min−1). The secondary outcomes included the proportion of participants counting the CC rhythm, time to first CC, CC depth, hands-off time and CC full-release ratio.ResultsParticipants in the TCPRLink feedback group more consistently performed CC with higher rate, both initially and 3 months later (median 111 (IQR 109–113) vs 108 (103–112) min−1, p=0.002 and 111 (109–113) vs 108 (105–112) min−1, p<0.001, respectively), with less need to count the rhythm (21.3% vs 41.3%, p=0.003% and 7% vs 22.6%, p=0.004, respectively) compared with the T-CPR group. There were no significant differences in time to the first CC, hands-off time or CC full-release ratio. Among 55–65 year group, the CC depth was deeper in the TCPRLink group than in the TCPR group (47.1±9.6 vs 38.5±8.7 mm, p=0.001 and 44.7±10.1 vs 39.3±10.8 mm, p=0.07, respectively).ConclusionsThe TCPRLink application improved T-CPR quality in trained laypersons to provide more effective CCs and lighten the load of counting out the CC with the dispatcher in a simulated T-CPR scenario. Further investigations are required to confirm this effectiveness in real-life resuscitation attempts.

Real-time High-Level Pain Quantification using a Smartphone and a Wrist-worn Electrodermal Activity Sensor (Preprint)

2020 ◽  
Author(s):  
Youngsun Kong ◽  
Hugo Posada-Quintero ◽  
Ki Chon
Keyword(s):  
Real Time ◽  
Pain Perception ◽  
Cross Validation ◽  
Cold Water ◽  
Tissue Injury ◽  
Electrodermal Activity ◽  
Communication Disorders ◽  
Smartphone Application ◽  
Heat Pain ◽  
High Level

BACKGROUND The subjectiveness of pain leads to inaccurate pain management, which can exacerbate drug addiction and overdose. The consequence is tremendous cost to society and individuals as the opioid crisis grows. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real time. OBJECTIVE We developed a smartphone-based system for real-time objective pain measurement and assessment using a wrist-worn electrodermal activity (EDA) device. METHODS Our smartphone application collects EDA signals from a wrist-worn device and evaluates pain based on the computation of three pain-sensitive EDA indices: the time-varying index of EDA (TVSymp); modified TVSymp (MTVSymp), and the derivative of phasic EDA (dPhEDA). For testing of our computational algorithms that were embedded in a smartphone application, ten subjects underwent heat pain using a thermal grill, which delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). The thermal grill induces heat pain perception without tissue injury using the temperature difference between warm and cold water. All of the wearable-collected EDA signal processing was performed using a smartphone application. Furthermore, another group of fifteen subjects underwent pain stimulation using electrical pulses (EP), which elicited a VAS pain score level 7 out of 10. For EP data collection, EDA signals were collected using a non-wearable device but the same smartphone application was used to calculate the EDA-derived pain indices. We set 5-second segments before and after each pain stimulus to be painless and pain segments, respectively, and trained eight machine-learning classifiers to test the feasibility of our smartphone and EDA-based system to detect pain in real-time. Parameters of the classifiers were optimized using the grid search cross-validation technique. We trained and tested the classifiers on both datasets with leave-one-subject-out cross-validation approach to prevent over-fitting of the models. RESULTS We obtained up to 82.1% accuracy in detecting pain. We also trained using only one dataset at a time and tested with other datasets (and vice versa) and achieved up to 83.1% accuracy. CONCLUSIONS Our results show the potential of a smartphone application to provide near real-time objective pain detection. This approach can potentially enable pain quantification for both acute and chronic pain and it is especially suited for subjects with communication disorders as well as infants.

scholarly journals ADAPT: A Smart Mask for Active Defense Against Air-Borne Pathogens

2021 ◽  
Author(s):  
Rohan Reddy Kalavakonda ◽  
Naren Vikram Raj Masna ◽  
Soumyajit Mandal ◽  
Swarup Bhunia
Keyword(s):  
Real Time ◽  
Preventive Measure ◽  
Smartphone Application ◽  
Sensor Data ◽  
Loop Control ◽  
Active Defense ◽  
Closed Loop Control System ◽  
Surgical Masks ◽  
Loop Control System ◽  
Micro Controller Unit

Abstract Face masks are a primary preventive measure against airborne pathogens. Thus, they have become one of the keys to controlling the spread of the COVID-19 virus. Common examples, including N95 masks, surgical masks, and face coverings, are passive devices that minimize the spread of suspended pathogens by inserting an aerosol-filtering barrier between the user’s nasal and oral cavities and the environment. However, the filtering process does not adapt to changing pathogen levels or other environmental factors, which reduces its effectiveness in real-world scenarios. This paper addresses the limitations of passive masks by proposing ADAPT, a smart IoT-enabled “active mask”. This wearable device contains a real-time closed-loop control system that senses airborne particles of different sizes near the mask by using an on-board particulate matter (PM) sensor. It then intelligently mitigates the threat by using mist spray, generated by a piezoelectric actuator, to load nearby aerosol particles such that they rapidly fall to the ground. The system is controlled by an on-board micro-controller unit (MCU) that collects sensor data, analyzes it, and activates the mist generator as necessary. A custom smartphone application enables the user to remotely control the device and also receive real-time alerts related to recharging, refilling, and/or decontamination of the mask before reuse. Experimental results on a working prototype confirm that aerosol clouds rapidly fall to the ground when the mask is activated, thus significantly reducing PM counts near the user. Also, usage of the mask significantly increases local relative humidity (RH) levels.

scholarly journals SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 408
Author(s):  
Jonas Chromik ◽  
Kristina Kirsten ◽  
Arne Herdick ◽  
Arpita Mallikarjuna Kappattanavar ◽  
Bert Arnrich
Keyword(s):  
Data Collection ◽  
Observational Studies ◽  
Electrodermal Activity ◽  
Real Life ◽  
Sensor Data ◽  
Rigorous Approach ◽  
Additional Information ◽  
Multiple Sensors ◽  
Multimodal Sensing ◽  
Home Based

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.

Internship as a criterion for South African educators’ registration

2018 ◽  
Vol 32 (7) ◽  
pp. 1333-1347
Author(s):  
Gideon Petrus van Tonder ◽  
Elsa Fourie
Keyword(s):  
South African ◽  
Work Experience ◽  
High Stress ◽  
Real Life ◽  
Well Being ◽  
Structured Interviews ◽  
Content Type ◽  
Learner Performance ◽  
Stress Levels ◽  
Method Approach

Purpose The purpose of this paper is to explore the possible use of internships to support educators with the increase in their administrative, professional workload. Design/methodology/approach This research utilised a mixed method approach. Quantitative and qualitative data was gathered. Pragmatism was the research paradigm. Two structured Likert scale questionnaires were used. Semi-structured interviews were conducted with purposefully selected in-service educators from schools. Findings The participants in this study indicated that the increase in their administrative workload impacted negatively on their well-being, they were overwhelmed by the amount of documentation they had to compile, they indicated that assessment was difficult, that they did not have time to constantly adapt to changes, that they had limited opportunities for professional development. Educators said they suffered from anxiety, had high stress levels, were moody, physically and emotionally exhausted and considered to leave the profession. Practical implications The implementation of an internship model could impact positively on educators’ administrative workload. It could lead to a decrease in educators’ administrative workload; lower stress levels and increased learner performance. Student educators will have opportunities to experience how a school operates, get feedback on their teaching skills, learn to discipline learners, attend meetings and serve as part of an educational team. Originality/value In the light of the findings of this research it seems that the implementation of an internship model would provide opportunities to expose student educators to a real-life work experience and opportunities to work together with experienced educators acting as mentors.

scholarly journals A smart mask for active defense against airborne pathogens

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rohan Reddy Kalavakonda ◽  
Naren Vikram Raj Masna ◽  
Soumyajit Mandal ◽  
Swarup Bhunia
Keyword(s):  
Real Time ◽  
Preventive Measure ◽  
Smartphone Application ◽  
Sensor Data ◽  
Loop Control ◽  
Active Defense ◽  
Closed Loop Control System ◽  
Surgical Masks ◽  
Loop Control System ◽  
Micro Controller Unit

AbstractFace masks are a primary preventive measure against airborne pathogens. Thus, they have become one of the keys to controlling the spread of the COVID-19 virus. Common examples, including N95 masks, surgical masks, and face coverings, are passive devices that minimize the spread of suspended pathogens by inserting an aerosol-filtering barrier between the user’s nasal and oral cavities and the environment. However, the filtering process does not adapt to changing pathogen levels or other environmental factors, which reduces its effectiveness in real-world scenarios. This paper addresses the limitations of passive masks by proposing ADAPT, a smart IoT-enabled “active mask”. This wearable device contains a real-time closed-loop control system that senses airborne particles of different sizes near the mask by using an on-board particulate matter (PM) sensor. It then intelligently mitigates the threat by using mist spray, generated by a piezoelectric actuator, to load nearby aerosol particles such that they rapidly fall to the ground. The system is controlled by an on-board micro-controller unit that collects sensor data, analyzes it, and activates the mist generator as necessary. A custom smartphone application enables the user to remotely control the device and also receive real-time alerts related to recharging, refilling, and/or decontamination of the mask before reuse. Experimental results on a working prototype confirm that aerosol clouds rapidly fall to the ground when the mask is activated, thus significantly reducing PM counts near the user. Also, usage of the mask significantly increases local relative humidity levels.

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