A microcontroller interfaced smartphone application to perform and analyze a laboratory experiment in real time

2020 ◽  
Author(s):  
Abhijit Poddar ◽  
Monali Poddar
Keyword(s):  
Laboratory Experiment ◽  
Real Time ◽  
Smartphone Application

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 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.

scholarly journals 0695 Sleepwell24, A Smartphone Application To Promote PAP Therapy Adherence: Feasibility And Acceptability

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A265-A265
Author(s):  
M E Petrov ◽  
K Hasanaj ◽  
C M Hoffmann ◽  
D R Epstein ◽  
L Krahn ◽  
...  
Keyword(s):  
Real Time ◽  
Smartphone Application ◽  
Sleep Medicine ◽  
Retention Rates ◽  
Activity Levels ◽  
Post Intervention ◽  
Personal Technology ◽  
Lost To Follow Up ◽  
First Time

Abstract Introduction We aimed to test the feasibility and acceptability of SleepWell24, a multicomponent, smartphone-delivered intervention to increase positive airway pressure (PAP) adherence among newly diagnosed OSA patients. Methods SleepWell24 targets PAP adherence along with other health behaviors through education, trouble-shooting, goal-setting, and near real-time biofeedback of PAP machine use, and sleep and physical activity levels (via Fitbit integration), and other chronic disease self-management components. Patients with a first-time diagnosis of OSA (AHI≥5) and prescribed PAP therapy were enrolled from the Centers for Sleep Medicine at Mayo Clinic in Rochester, MN and Phoenix, AZ. Patients were randomized to SleepWell24 or usual care (UC) and assessed for PAP use over 60 consecutive nights. UC patients received a Fitbit monitor to control for non-specific intervention effects related to the introduction of a new personal technology. Feasibility was assessed with recruitment and retention rates and acceptability was assessed post-intervention with the validated, 8-item Treatment Evaluation Questionnaire (TEQ; range:0-4). ANCOVA models, adjusting for age, sex, and AHI severity, compared intervention arms on acceptability ratings. Results OSA patients were consented and randomized (N=111). Before the intervention began 4 participants withdrew, 12 were lost to follow-up, and 5 could not start the trial due to durable medical equipment (DME) vendor barriers. Ninety OSA patients (n=41 SleepWell24, n=49 UC; age M±SD=57.2±12.2; 44.4% female, 61.1% AHI≥15) started the intervention, with 2 participants withdrawing, 1 becoming deceased (unrelated to treatment) and 7 with missing PAP data due to DME vendor barriers. There was no significant between-groups differences on post-treatment acceptability (SleepWell24 M±SD=2.7±1.1 vs. UC M±SD=3.1±0.9, F[1,73]=2.3, p=0.11), and 77% of SleepWell24 participants found the app to be moderately to totally acceptable. Conclusion Overall, SleepWell24 was found to be feasible for delivery in two large clinical sleep medicine centers, and patients found the app to be acceptable. A number of challenges in trial delivery were encountered that have implications for scaled-up efficacy testing: (a) partnerships with DME vendors for near real-time PAP data integration; (b) alignment with clinical practice (i.e., referral, medical record integration); and (c) patient engagement. Support National Institute of Nursing Research / National Institutes of Health: R21NR016046

scholarly journals Design and Testing of a Smartphone Application for Real-Time Self-Tracking Diabetes Self-Management Behaviors

2018 ◽  
Vol 09 (02) ◽  
pp. 440-449 ◽  
Author(s):  
Danielle Groat ◽  
Hiral Soni ◽  
Maria Grando ◽  
Bithika Thompson ◽  
David Kaufman ◽  
...  
Keyword(s):  
Real Time ◽  
Alcohol Intake ◽  
Perfect Match ◽  
Smartphone Application ◽  
Self Management ◽  
Patient Specific ◽  
Pilot Test ◽  
Patient Centered ◽  
Survey Responses ◽  
Management Behaviors

Background Type 1 diabetes (T1D) care requires multiple daily self-management behaviors (SMBs). Preliminary studies on SMBs rely mainly on self-reported survey and interview data. There is little information on adult T1D SMBs, along with corresponding compensation techniques (CTs), gathered in real-time. Objective The article aims to use a patient-centered approach to design iDECIDE, a smartphone application that gathers daily diabetes SMBs and CTs related to meal and alcohol intake and exercise in real-time, and contrast patients' actual behaviors against those self-reported with the app. Methods Two usability studies were used to improve iDECIDE's functionality. These were followed by a 30-day pilot test of the redesigned app. A survey designed to capture diabetes SMBs and CTs was administered prior to the 30-day pilot test. Survey results were compared against iDECIDE logs. Results Usability studies revealed that participants desired advanced features for self-tracking meals and alcohol intake. Thirteen participants recorded over 1,200 CTs for carbohydrates during the 30-day study. Participants also recorded 76 alcohol and 166 exercise CTs. Comparisons of survey responses and iDECIDE logs showed mean% (standard deviation) concordance of 77% (25) for SMBs related to meals, where concordance of 100% indicates a perfect match. There was low concordance of 35% (35) and 46% (41) for alcohol and exercise events, respectively. Conclusion The high variability found in SMBs and CTs highlights the need for real-time diabetes self-tracking mechanisms to better understand SMBs and CTs. Future work will use the developed app to collect SMBs and CTs and identify patient-specific diabetes adherence barriers that could be addressed with individualized education interventions.

scholarly journals Ecological momentary assessment using patient smartphones: a pilot study in depression (Preprint)

2019 ◽  
Author(s):  
Guillaume Delval ◽  
Redwan Maatoug ◽  
Terence Brochu ◽  
Benjamin Pitrat ◽  
Bruno Millet
Keyword(s):  
Real Time ◽  
Ecological Momentary Assessment ◽  
Large Scale ◽  
Smartphone Application ◽  
Or Efficiency ◽  
Patient’S Satisfaction ◽  
Ecological Momentary ◽  
Momentary Assessment ◽  
Graphic Rendering

UNSTRUCTURED Background: Ecological Momentary Assessment (EMA) is a promising tool in the management of psychiatric disorders and particularly depression; it allows for a real-time evaluation of symptoms and an earlier detection of relapse or efficiency of the treatment associated. The generalization of the smartphone in modern societies also offers a new large-scale support for EMA. Objective: The present study aims to evaluate the feasibility in terms of compliance as well as graphic rendering and user experience of an EMA with patients suffering from unipolar depression. Method: Eleven patients at La Pitié-Salpêtrière Hospital were followed during 28 days with the help of a smartphone application installed on the patient’s personal smartphones. The results of the real-time collected data were reviewed during three follow-up consultations, by a psychiatrist interacting with the patient, on a “dashboard” aggregating all the patient’s data in a user-friendly manner. Results: Seven patients out of eleven have followed the protocol for its total length of time. Two patients continue to fill in the questionnaires without showing for the consultation which suggests that EMA is easy to use with a good compliance. The global response rate to the questionnaires was 58% with an average follow-up duration of 21 days out of 28 days in total. In light of the results in terms of graphic rendering and patient’s satisfaction, we strongly believe that EMA should be the focus on follow-up and early intervention studies.

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.

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